70911 A WORLD BANK STUDY Financial and Fiscal Instruments for Catastrophe Risk Management ADDRESSING LOSSES FROM FLOOD HAZARDS IN CENTRAL EUROPE John Pollner A W O R L D B A N K S T U D Y Financial and Fiscal Instruments for Catastrophe Risk Management Addressing Losses from Flood Hazards in Central Europe John Pollner © 2012 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved 1 2 3 4 15 14 13 12 World Bank Studies are published to communicate the results of the Bank’s work to the development community with the least possible delay. The manuscript of this paper therefore has not been prepared in accordance with the procedures appropriate to formally edited texts. This work is a product of the staff of The World Bank with external contributions. Note that The World Bank does not necessarily own each component of the content included in the work. 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All queries on rights and licenses should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. ISBN (paper): 978-0-8213-9579-0 ISBN (electronic): 978-0-8213-9580-6 DOI: 10.1596/978-0-8213-9579-0 Cover photo: Flooding in the Czech Republic. © The World Bank. Library of Congress Cataloging-in-Publication data has been requested. Contents Acknowledgments ....................................................................................................................xi Abbreviations and Acronyms ............................................................................................... xii Executive Summary ............................................................................................................... xiii 1. Risk Analysis and Financial Instruments for Managing Catastrophe Exposures ....... 1 Planning for the Fiscal Exposure Impacts of Major Flood Losses in Central Europe ............................................................................................................. 1 The Fiscal and Macroeconomic Framework and Its Relation to Disaster Impacts .... 2 The Role of the EUSF in Disaster and Fiscal Management ........................................... 3 Measuring the Level of Risk and Potential Financial and/Fiscal Impacts................... 4 Structuring Financial and Fiscal Instruments for Funding Losses Ex Ante................ 5 Country Loss Distributions on Account of Flood Hazards ........................................... 7 Rationale for Government/Public Sector Insurance Needs ........................................... 8 Market Insurance and Private Property Coverage ....................................................... 11 The Range of Catastrophe Insurance Instruments ....................................................... 12 Structuring Catastrophe Mechanisms to Optimize Coverage at the Lowest Cost................................................................................................................. 14 Costs of Catastrophe Risk and Associated Market Pricing ......................................... 15 Comparing Insurance Premiums with Contingent Loan Pricing .............................. 17 Optimal Structuring of Funding Mechanisms for Loss Coverage ............................. 18 Applying a Parametric Insurance Structure .................................................................. 19 Risk-Based Pricing (Based on Loss Exposure and Probability) .................................. 21 Summary of Risk Management Efficiencies Applied to Speciï¬?c Instruments ......... 23 Institutional Set-Up to Manage Pooled Structures ....................................................... 26 Conclusion .......................................................................................................................... 27 2. Catastrophic Loss Exposure Analysis: Flood Exposures and Probabilistic Loss Estimations in Poland, the Czech Republic, Hungary, and the Slovak Republic ..... 31 Introduction........................................................................................................................ 31 1. Deï¬?nitions ...................................................................................................................... 34 2. Input Data and Providers ............................................................................................. 35 3. Methodology .................................................................................................................. 37 4. Outputs ........................................................................................................................... 59 Conclusion .......................................................................................................................... 69 3. Modeling for Losses Correlated to Flood Magnitude Triggers ............................... 79 Data—Countries and River Catchments used in the Analysis ................................... 79 Historical Data on River Discharges ............................................................................... 80 iii iv Contents Generation of Losses and Models Used ......................................................................... 80 Correlation Matrices—Methodology.............................................................................. 82 Results of Correlation Matrices ....................................................................................... 84 Results of Dependency Structures of Discharges ......................................................... 86 Results on Relationship between Losses and Discharges............................................ 88 Outputs per Gross Loss .................................................................................................... 92 Outputs under Assumed Parametric Reinsurance Contracts ..................................... 92 Scenario with Threshold of EUR 1.5 billion................................................................... 95 Results ................................................................................................................................. 95 Trigger Methodology ........................................................................................................ 95 Se ing Threshold Flood Triggers per Country for Parametric Contract Design ...... 102 Conclusion ........................................................................................................................ 104 Annex: Monthly Correlations between Catchments .................................................. 105 4. Private Insurance Markets and Public Disaster Financing Mechanisms ............ 117 Overview .......................................................................................................................... 117 The Impact of Natural Catastrophes on Central Europe ........................................... 120 Survey of Catastrophe Insurance Markets in Central Europe .................................. 121 Catastrophe Risk Policy Coverage ................................................................................ 121 Czech Republic—Catastrophe Insurance Market Overview .................................... 124 Poland—Catastrophe Insurance Market Overview ................................................... 128 The Slovak Republic—Catastrophe Insurance Market Overview ........................... 133 Hungary—Catastrophe Insurance Market Overview................................................ 135 Conclusion ........................................................................................................................ 141 5. Fiscal Sustainability Effects of Natural Disaster Shocks ........................................ 145 Background and Context................................................................................................ 145 Methodological Approach.............................................................................................. 148 Data.................................................................................................................................... 149 The Impact of Disasters on Expenditures and Output .............................................. 151 Results ............................................................................................................................... 152 Robustness ........................................................................................................................ 153 The Impact of Disasters across Income Levels ............................................................ 154 Indebtedness and the Effect of a Country’s Response to Disasters ......................... 156 Financial Development and Insurance Penetration ................................................... 157 Conclusion ........................................................................................................................ 159 Annex: Impact of Disasters for Different Regions ...................................................... 161 6. Overall Conclusions of the Report.............................................................................. 189 Bibliography ........................................................................................................................... 191 Contents v Boxes Box 1.1. Fiscal and GDP Response Based on Insurance Depth in the Economy................2 Box 1.2. Risk Modeling Methodology ......................................................................................4 Box 1.3. Parametric Trigger Events as an Alternative to Direct Loss Measurement .........5 Box 1.4. An Operating Multicountry Catastrophe Risk Fiscal Insurance Pool: The CCRIF ............................................................................................................................9 Box 1.5. The Range of Catastrophe Insurance Instruments ................................................13 Box 1.6. The Equivalency among Catastrophe Risk and Capital Market Instruments.........................................................................................................................15 Box 1.7. Equivalence of Debt versus Risk Transfer (Insurance) Instruments ...................17 Box 1.8. Parametrically Triggered Payments Calibrated with a Two-Factor Index Scale ..........................................................................................................................20 Figures Figure B1.1. Cumulative Impulse Response Functions versus Insurance Penetration in Economy .....................................................................................................3 Figure 1.1. Technical Modules of the Catastrophe Risk Model ............................................5 Figure 1.2. Individual and Pooled Country Loss Distributions ...........................................7 Figure 1.3. Catastrophe Bond Financing Structure ..............................................................16 Figure 1.4. Proposed Disaster Risk Funding .........................................................................18 Figure 1.5. Risk Coverage Mezzanine Layer .........................................................................19 Figure 1.6. Threshold Payment Triggers................................................................................20 Figure 1.7. Sublayer of Parametric Insurance within the Total Structure .........................23 Figure 1.8. Individual Pricing: Non-pooled ..........................................................................23 Figure 1.9. Pooled: Collective Pricing (Solidarity and Risk-based) ...................................24 Figure 1.10. Pooled Catastrophe Bond Structure .................................................................24 Figure 1.11. Collective Premium Payments into Single Cat Bond Fund ...........................25 Figure 2.1. Overall Exposure Modeling Workflow ..............................................................38 Figure 2.2. Sample Estimation of the Weights of the Districts............................................47 Figure 2.3a Example of the Digital Terrain Model (DTM) with 5:1 Vertical Exaggeration.......................................................................................................................50 Figure 2.3b. Example of the Flood Hazard Zones in the Czech Republic ........................50 Figure 2.4. Vulnerability Functions (Artiï¬?cial Examples)...................................................51 Figure 2.5. Algorithm of The Loss Calculation for One Event (the Flood Return Period) under the Scenario Method ................................................................................52 Figure 2.6. Samples of the Scenarios Generated by the Stochastic Method .....................54 Figure 2.7. Event-Set Generation Overview ..........................................................................55 Figure 2.8. A Sample of the Function Used for the Modeling of the Factors ...................56 Figure 2.9. Sample of LEC as a Function of the Property Loss versus the Return Period (RTP) of the Flood Loss Event .............................................................................57 Figure 2.10. Sample of Survival Function as a Function of the Probability of the Flood Loss Event (in Percent) versus the Property Loss..............................................57 Figure 2.11. Sample of the LEC with the Conï¬?dence Limits ..............................................58 vi Contents Figure 2.12. Sample of the Regional Loss Structure (% of the Total Loss)........................58 Figure 2.13. Sample of the Regional Loss Structure (% of the Province Property) .........58 Figure 2.14. Regional Property Distribution to NUTS-3 Units (Provinces) for the Czech Republic (in %) .......................................................................................................59 Figure 2.15. Regional Property Distribution to NUTS-3 units (Provinces) for the Slovak Republic (in %) ......................................................................................................60 Figure 2.16. Regional Property Distribution to NUTS-3 Units (Provinces) for Hungary (in %) ..................................................................................................................60 Figure 2.17. Regional Property Distribution to NUTS-2 Units (Provinces) for Poland (in %) ......................................................................................................................61 Figure 2.18. Regional Property Distribution to LAU 1 Units (District) for the Czech Republic in % of the Total Property in the Country .........................................62 Figure 2.19. Regional Property Distribution to LAU 1 Units (District) for the Slovak Republic in % of the Total Property in the Country ........................................62 Figure 2.20. Regional Property Distribution to LAU 1 Units (Districts) for Hungary in % of the Total Property in the Country ....................................................63 Figure 2.21. Regional Property Distribution to LAU 1 Units (District) for Poland in % of the Total Property in the Country .....................................................................63 Figure 2.22. Summary of the Exceedance Curves for the Czech Republic for Losses on Total Property ..................................................................................................64 Figure 2.23. Summary of the Exceedance Curves for the Czech Republic for Losses on Public Property ................................................................................................65 Figure 2.24. Summary of the Exceedance Curves for the Slovak Republic for Losses on the Total Property............................................................................................65 Figure 2.25. Summary of the Exceedance Curves for the Slovak Republic for Losses on Public Property ................................................................................................66 Figure 2.26. Summary of Exceedance Curves for Hungary for Losses on Total Property ....................................................................................................................66 Figure 2.27. Summary of Exceedance Curves for Hungary for Losses on Public Property ..............................................................................................................................67 Figure 2.28. Summary of the Exceedance Curves for the Poland for Losses on Total Property ....................................................................................................................67 Figure 2.29. Summary of the Exceedance Curves for the Poland for Losses on Public Property ..................................................................................................................68 Figure 2.30. LEC for Loss on Total Property for Each Country and the V-4 Group .......68 Figure 2.31. LEC for Losses on Public Property for Each Country and V-4 Group ........69 Figure 3.1. Catchments, the Czech Republic .........................................................................82 Figure 3.2. Catchments, the Slovak Republic ........................................................................82 Figure 3.3. Catchments, Poland ..............................................................................................83 Figure 3.4. Catchments, Hungary ...........................................................................................83 Figure 3.5. The Czech Republic, Losses to Discharges ........................................................90 Figure 3.6. The Slovak Republic, Losses to Discharges .......................................................90 Figure 3.7. Hungary, Losses to Discharges ...........................................................................91 Figure 3.8. Poland, Losses to Discharges ...............................................................................91 Figure 3.9. Type I and Type II Errors .....................................................................................97 Contents vii Figure 3.10. CEE Loss from Czech Republic River Catchments.........................................98 Figure 3.11. CEE Loss from Slovak River Catchments ........................................................98 Figure 3.12. CEE Loss from Hungarian River Catchments .................................................99 Figure 3.13. CEE Loss from Polish River Catchments .........................................................99 Figure 3.14. Czech Republic—Per Country Loss ................................................................100 Figure 3.15. Slovak Republic—Per Country Loss ...............................................................100 Figure 3.16. Hungary—Per Country Loss ...........................................................................101 Figure 3.17. Poland—Per Country Loss ...............................................................................101 Figure 3A.1. Czech Republic .................................................................................................106 Figure 3A.2. The Slovak Republic .........................................................................................109 Figure 3A.3. Hungary .............................................................................................................111 Figure 3A.4. Poland ................................................................................................................115 Figure 4.1. Catastrophe Insurance Coverage Penetration among Homeowners...........130 Figure 4.2. Earthquake Map of South Eastern Europe ......................................................137 Figure 5A.1. Cumulative Impulse Response Functions of Levels ...................................167 Figure 5A.2. Cumulative Impulse Response Functions of Differences ...........................168 Figure 5A.3. Cumulative IRFs Adding Lags .......................................................................169 Figure 5A.4. Cumulative IFRs Using Different Disaster Indicators ................................170 Figure 5A.5. Cumulative IRFs Using a Different Measure of GDP .................................172 Figure 5A.6. Cumulative IRFs Using Interest Rate Level..................................................173 Figure 5A.7. Cumulative IRFs Changing Order in VAR ...................................................174 Figure 5A.8. Cumulative IRFs for High Income Countries ..............................................175 Figure 5A.9. Cumulative IRFs for Middle Income Countries...........................................176 Figure 5A.10. Cumulative IRFs for Low and Middle Income Countries ........................177 Figure 5A.11. Cumulative IRFs for Higher Middle Income Countries ...........................178 Figure 5A.12. Cumulative IRFs for Different Debt Levels ................................................179 Figure 5A.13. Cumulative IRFs by Debt Controlling for Income Level ..........................181 Figure 5A.14. Cumulative IRFs for Different Levels of Financial Development ...........182 Figure 5A.15. Cumulative IRFs by Financial Development Controlling for Income Level ....................................................................................................................184 Figure 5A.16. Cumulative IRFs for Countries with Low Insurance Penetration ...........185 Figure 5A.17. Cumulative IRFs by Insurance Penetration Controlling for Income Level ..................................................................................................................................187 Tables Table 1.1. Central Europe Floods: Economic Losses in Selected Years (in Nominal US$ Million Equivalent) .....................................................................................................2 Table 1.2. Outlays Incurred by the EUSF (EUR Million) .......................................................4 Table 1.3. Water Flow Level/Discharge per River Catchment Correlated to a EUR 500 Million per Country Loss ............................................................................................6 Table 1.4. Effect on Budget Revenues and GDP, from Reference Catastrophe Floods ..................................................................................................................................10 Table 1.5. Insurance and Financing Options .........................................................................12 viii Contents Table 1.6. Price Advantages of Pooling for Country Flood Insurance (Based on a Combined Aggregate Loss of EUR 7.6 billion)..............................................................21 Table 2.1. Industry Branch Classes as Used in the Study ....................................................39 Table 2.2. Industry Branch Classes as Reclassiï¬?ed into Three Property Types ...............40 Table 2.3. Classiï¬?cation by Asset Categories Considered in the Study.............................41 Table 2.4. Classiï¬?cation by Asset Categories Not Considered in the Study .....................41 Table 2.5. Structure of Asset Category Classes (“Buildingâ€? and “Contentâ€?) and Their Deï¬?nitions as Used in the Study...........................................................................42 Table 2.6. ESA Sectors/Units Included in the Public Class..................................................42 Table 2.7. Reclassiï¬?cation of the ESA Sectors/Units .............................................................43 Table 2.8. Property Reclassiï¬?cation Summary ......................................................................43 Table 2.9. Sector/Purpose Reclassiï¬?cation of the Property .................................................44 Table 2.10. Five Vulnerability Classes as Combinations of the Super-classes of the Industry Branch versus Asset Category .........................................................................49 Table 3.1. List of Stations/Countries .......................................................................................79 Table 3.2. Exchange Rates Used ..............................................................................................80 Table 3.3. Total Insured Value (TIV) per Type of Risk and Area (EUR Million)..............81 Table 3.4. Average of Monthly Discharge Rank Correlation Matrices ..............................85 Table 3.5. Linear Correlation Matrix as Input in t-Copula ..................................................87 Table 3.6. Correlation of All Gross Losses between Countries ...........................................92 Table 3.7. Rank Correlation Matrix of Discharges when Overall CEE Loss Exceeds EUR 200 Million .................................................................................................................93 Table 3.8. Selected Options and Probabilities, Given a Loss Exceeding the EUR 500 million Threshold on an Individual Basis; and Given That the Overall CEE Loss Exceeds EUR 500 Million ................................................................................94 Table 3.9. Rank Correlation of Losses between Countries—CEE Loss Exceeds EUR 500 Million ..........................................................................................................................95 Table 3.10. Ceded and Retained Losses under Pooled and Individual Se ing ................95 Table 3.11. Selected Options and Probabilities, Given Loss Exceeding EUR 1.5 Billion Threshold on an Individual Basis; and Given that the Overall CEE Loss Exceeds EUR 1.5 Billion ...........................................................................................96 Table 3.12. Rank Correlation of Losses between Countries—CEE Loss Exceeds EUR 1.5 Billion ...................................................................................................................96 Table 3.13. Ceded and Retained Losses under Pooled and Individual Se ings ..............96 Table 3.14. Model for Exposure Using Total Insured and Uninsured Assets ................102 Table 3.15. List of Optimized Triggers as Return Period and Relevant Flow Discharge (in Cubic Meters per Second), for Loss Threshold of EUR 200 Million ...............................................................................................................103 Table 3.16. List of Optimized Triggers as Return Period and Relevant Discharge (in Cubic Meters per Second) for Loss Thresholds of EUR 500 Million ..................104 Table 4.1. Catastrophe Insurance Penetration in Central European Countries (Estimates) ........................................................................................................................118 Table 4.2. Economic and Insured Losses from 1997 Flood in Central Europe ...............121 Table 4.3. Historical Disaster Losses in the Czech Republic .............................................124 Contents ix Table 4.4. Flood Premium Rates in Different Flood Risk Zones.......................................126 Table 4.5. Most Signiï¬?cant Floods in Poland (1997–2008) .................................................129 Table 4.6. History of Sizeable Earthquakes in Hungary ....................................................136 Table 4.7. Hungary’s Flood Exposure ..................................................................................137 Table 4.8. Insured Flood Losses.............................................................................................138 Table 5A.1. Summary Statistics .............................................................................................161 Table 5A.2. Unit Root and Cointegration Tests ...................................................................166 Table 5A.3. Comparing Years With and Without Disasters: Two Sample Mean Tests ........167 Acknowledgments T his report was prepared by a team led by John D. Pollner, Lead Financial Officer, of the Private and Financial Sector Development Department (ECSPF) and Country Sector Coordinator for the Central Europe and the Baltics Country Department (ECCU5) of the Bank’s Europe and Central Asia Region (ECA). The team included Intermap Tech- nologies (Consulting Firm), Aon Benï¬?eld (Consulting Firm), Martin Melecky, Financial Economist (ECSPF), Eugene Gurenko, Lead Financial Sector Specialist (GCMNB), and Claudio Radda , Senior Economist (DECMG). Chapter 1 of the report was wri en by John Pollner. This chapter is the main section of the report and summarizes the main ï¬?ndings and develops design options for a flood disaster catastrophe insurance pool for Central Europe. Chapter 2 was wri en by Intermap Technologies based in the Prague Office and edited by John Pollner. The ï¬?rm Intermap specializes in hazard risk mapping based on digital terrain mapping technologies and has a practice in flood risk modeling. This is the main data analysis section of the report, which details the methodology and individ- ual country risks. It also builds the loss distribution functions used for the probabilistic estimation of events as well as pricing of insurance or alternative ï¬?nancial instruments for coverage. Chapter 3 was prepared by Aon Benï¬?eld, a catastrophe reinsurance analysis ï¬?rm and reinsurance broker, and reviewed and edited by John Pollner. This section analyz- es the statistical correlations between different flood magnitudes in each country and historical and projected insured losses in flood affected areas. The objective was to de- termine predictable correlations between threshold floods and monetary losses so that parametric-based contracts could be built with associated ï¬?nancial compensation based on surpassing of any such physical thresholds. Chapter 4 was wri en by Eugene Gurenko. This section reviews and analyzes the private sector insurance markets in the V-4 and their degree of coverage of catastrophic events. It also analyzes the public sector mechanisms in place by central governments, such as budget reserve funds, their characteristics, and mode of application for funding disaster events. Chapter 5 was wri en by Martin Melecky and Claudio Radda and edited by John Pollner. This section covers a large sample of countries globally, that are subject to natu- ral disasters, and analyzes the GDP recovery path and post-disaster ï¬?scal sustainability (when using deï¬?cit or debt ï¬?nancing) based on a countries’ economic, ï¬?nancial sector, debt raising, and insurance characteristics. The peer reviewers for the ï¬?nal report were Olivier Mahul, Program Coordina- tor (GCMNB), and Francis Ghesquiere, Lead Disaster Risk Management Specialist (LCSUW). The Sector Managers for this report were Lalit Raina (ECSF1) and Sophie Sirtaine (ECSF2). The Sector Director was Gerardo Corrochano (ECSPF). The Country Director was Peter Harrold (ECCU5). The Regional Vice President was Philippe Le Houerou, (ECAVP). xi Abbreviations and Acronyms API Antecedent Precipitation Index CAT Catastrophe CDF Cumulative Distribution Function CEE Central and Eastern Europe CRED Center for Research on the Epidemiology of Disasters CRESTA Catastrophe Risk Evaluating and Standardizing Target Accumulations CORINE Coordination of Information on the Environment (for soil erosion) CZ Czech Republic df Degrees of Freedom DTM digital terrain models ECB European Central Bank ESA European System of Accounts EUR Euro EURIBOR Euro Interbank Offered Rate (interbank lending rate) EUSF European Union Solidarity Fund FLEXA Fire, Lightning, Explosion, Aviation HU Hungary GDP Gross Domestic Product GIS Geographic Information System GMR Geomorphologic Regression IRF Impulse Response Function LAU Local Administrative Unit LEC Loss Exceedance Curve MARS Multiple Non-linear Regression Analysis NACE Classiï¬?cation of Economic Activities in the European Community NUTS Nomenclature of Units for Territorial Statistics OEP Occurrence Exceedance Probability p Probability PL Poland PML Probable Maximum Loss PPP Purchasing Power Parity PVAR Panel Vector Autoregression RTP Return Period SK Slovak Republic SPV Special Purpose Vehicle TIV Total Insured Value UN United Nations VAR Vector Autoregression V-4 Visegrad-4 Countries (Poland, the Czech Republic, Hungary, the Slovak Republic) WDI World Development Indicators XL Excess-of-Loss (a catastrophe insurance coverage) xii Executive Summary T his report addresses the large flood exposures of Central Europe and proposes efficient ï¬?nancial and risk transfer mechanisms to mitigate ï¬?scal losses from natu- ral catastrophes. In particular, the Visegrad countries (V-4) of Central Europe—namely, Poland, the Czech Republic, Hungary, and the Slovak Republic—have such tremendous potential flood damages that reliance on budgetary appropriations or even European Union (EU) funds in such circumstances becomes ineffective and does not provide needed cash funds for the quick response and recovery needed to minimize economic disruptions. The report is primarily addressed to the governments of the region, which should build into their ï¬?scal planning the necessary contingent funding mechanisms, based on their exposures. The report is addressed to ï¬?nance ministries and also to the insur- ance and securities regulators and the private insurance and capital markets, which may all play a role in the proposed mechanisms. An arrangement using a multicountry pool with a hazard-triggered insurance payout mechanism complemented by contingent ï¬?- nancing is proposed, to be er manage these risks and avoid major ï¬?scal volatility and disruption. The historical and more current flood losses in the region have been substantial, given the countries’ river basin topographies and exposures to weather events. In 2010 the V-4 demonstrated their historical and topographic vulnerability to floods and associ- ated loss exposures. Poland suffered US$3.2 billion in flood related losses, comparable to its US$3.5 billion of losses back in 1997. The economic vulnerability generated by flood events can be immensely cata- strophic. Flood modeling analysis of the V-4 shows that a disaster event with a 5 percent probability in any given year can lead to economic losses in these countries of between 0.6 percent to 1.9 percent of GDP, as well as between 2.2 percent to 10.7 percent of gov- ernment revenues. Larger events (albeit with lower probabilities) could quadruple the size of such losses. Although Poland has the highest overall exposure to loss, the Slovak Republic is quite vulnerable given its lower share of the government budget in the over- all economy. The Czech Republic and Hungary fall in between and have substantial exposures. The European Union Solidarity Fund (EUSF) is available as a mechanism to chan- nel EU member budgetary contributions to disaster-stricken states. But it comes into effect at very high levels of losses in relation to government revenues, and does not pro- vide sufficient funding. It is also not nimble as a mechanism to provide immediate funds for recovery and emergency—thus it is utilized mainly for medium-term post-disaster reconstruction. The governments of the V-4 maintain modest budgetary reserves for catastrophic events, but a more optimal system should include ï¬?nancial mechanisms to supplement the funding gap between budget reserves, EUSF resources, and actual losses that occur requiring immediate ï¬?nancing for fast recovery. In the insurance sector in Central Europe, protection against loss damages for private households and business counts on a well-developed industry. The insurance industry provides coverage to between 50–75 percent of households in the V-4 coun- xiii xiv Executive Summary tries, showing a relatively high level of development of the sector. Government assets and infrastructure, however, as well as potentially less advantaged households, are not broadly insured, implying contingent loss liabilities to the state. An insurance-like mechanism for national governments can be tailored for ‘mac- ro-portfolio’ country needs. Fiscal support in the event of country-level disaster losses, by virtue of their broad territorial scope, should use mechanisms that provide payments triggered by physical flood intensities (rather than exact site-by-site losses as in the tra- ditional insurance industry). This is accomplished via innovative yet ultra-safe ï¬?nancial instruments. For example, if infrastructure losses near a flood zone can be historically shown to have high correlations to flood levels, then ï¬?nancial counterparties can accept contracts that pay solely when the flood level is exceeded, in exchange for a fee or pre- mium. This is called a parametric-based contract since the physical parameter, not the monetary level of loss, triggers the payout. A multicountry mechanism for pooling of risks to protect government assets and infrastructure can also provide major cost efficiencies to all governments, using para- metric-based contracts. Since a coverage-provider can rely on a larger more diversiï¬?ed and predictable portfolio with a greater number of participants, savings on a V-4 coun- try pooling mechanisms can range from 25 to 33 percent of the ï¬?nancing costs that each country would otherwise have paid if it obtained such ï¬?nancial coverage on its own. There is also evidence that countries’ use of insurance-like instruments improve their long-term ï¬?scal sustainability prospects. The economic and statistical analysis of disaster events occurring globally in several countries with hazard exposures shows that those countries with deeper ï¬?nancial and insurance markets, recover more quickly in terms of the post-disaster GDP growth path. As well, they experience reduced public sector deï¬?cits compared to those countries with li le or scant use of ï¬?nancial and/or insurance mechanisms. There are several instruments and options for both risk transfer/insurance and risk ï¬?nancing/debt mechanisms for funding catastrophes. Governments should con- sider both classes of instruments to optimize the coverage-versus-cost beneï¬?t. Both in- struments can be analyzed based on equivalencies in terms of market spreads, taking into account the probabilities of invoking them. A hybrid-like instrument, the catastro- phe bond, is really a risk transfer instrument but structured as a debt security. Under this instrument, ï¬?nancial investors take the risk of a loss of principal if the disaster occurs (beneï¬?ting the bond issuer with free funds to pay for recovery). This report conducts an analysis of a possible four-country catastrophe bond that the V-4 countries could partake of, to ï¬?nance large flood disasters if they occurred in their territories. Contingent loans or debt can be also used as stand-by facilities that cost less than insurance when unused. However, their application should be for higher levels of losses so that they are invoked less frequently. This would avoid an excessive buildup of debt if such instruments were used for frequently recurring hazard events. Individual countries need not be concerned about subsidizing others’ losses if the pricing approach under a multicountry initiative is risk adjusted. A pooled mecha- nism using risk transfer instruments such as the ones discussed above, can use pure risk-based pricing considering each country’s individual exposure proï¬?le. The opposite would be pricing of coverage equally among all participants. A balanced approach might be to consider a combined risk-pricing approach with a portion of the pricing based on Executive Summary xv solidarity (that is, some equal pricing as well as some based on individual country risk). This would bring beneï¬?ts to all participants through the “large portfolio effect,â€? as well as build in a partial solidarity element to share the highest exposures. Legal provisions and institutional governance requirements are also required to ensure that any such scheme operates with clear rules of loss assessment and pay- ment. To implement the above, an institutional set up would be needed under a legal entity, meeting minimum capital reserves (in the case of a pooled parametric insurance mechanism) or underwriting costs (in the case of a catastrophe bond) and needing the establishment of an issuing trust vehicle. A simpler approach could also involve directly contracting with international insurers or reinsurers, though such pricing may be higher in the long run than se ing up a funded vehicle. A feasibility analysis needs to be completed to provide an additional level of reliability of the potential losses involved, and their likelihoods of occurrence. To design the approaches contemplated, further validation of asset and property values and vulnerabilities need closer analysis. Governments have several choices and can also select speciï¬?c assets or amounts they wish to protect, to receive compensation under a custom-designed loss ï¬?nancing scheme. Se ing up flood-level based contracts would require additional physical and hy- drological risk modeling. This ensures that flood magnitude correlations with qualify- ing losses are closely linked. If catastrophe bonds are used, a market pricing test for the ï¬?nal spread asked will be needed. For all the above, governments should count on a small team of independent experts to monitor such contracts, ensure ï¬?nancial manage- ment of funds, oversee the trust vehicles, and assure the requisite governance proce- dures and trigger veriï¬?cation mechanisms needed to invoke payments under the pro- posed disaster funding schemes. The V-4 countries should therefore begin to set up the ï¬?nancial mechanisms to prevent major ï¬?scal losses from future catastrophic floods. The instruments proposed can be market tested under a ï¬?nal feasibility analysis and implemented with a relatively streamlined institutional infrastructure. Political agreements will be needed among the participants to launch these mechanisms which, with collective participation, are shown to be highly cost effective. CHAPTER 1 Risk Analysis and Financial Instruments for Managing Catastrophe Exposures Planning for the Fiscal Exposure Impacts of Major Flood Losses in Central Europe T his catastrophe risk management report examines ï¬?nancial mechanisms to apply for ï¬?scal contingencies including the design of innovative funding approaches. These are proposed for large catastrophic events and climate change risks in the four Visegrad countries (V-4)—Poland, Hungary, the Czech Republic, and the Slovak Republic—and demonstrate how risk transfer instruments can improve ï¬?scal smoothing to manage ma- jor expenditure outlays. The report is addressed to country governments, particularly the Ministries of Finance as well as the insurance and securities regulators and the pri- vate insurance and capital markets. The report examines the disaster-funding arrange- ments in the context of the European Union Solidarity Fund (EUSF) that is used to help ï¬?nance European Union (EU) members following natural disasters. The report proposes optimizing the ï¬?nancial and ï¬?scal mechanisms to tackle large future losses that dispro- portionately impact country budgets and economies. The report focuses on assessing the flood risk exposures of the public sector in the V-4 countries and proposes ï¬?nancial mechanisms that can be used to mitigate govern- ments’ resultant ï¬?scal losses. The report examines how catastrophic events for the V-4 and their loss exposures, compare with shares of national gross domestic product (GDP) and ï¬?scal budgets. It then demonstrates that risk pooling arrangements for these coun- tries are feasible to cover major losses before and after EUSF ï¬?nancing kicks in, and also reviews other available instruments (budgetary, reserves, risk insurance, catastrophe bonds, parametric contracts, contingent loans and other) that may be appropriate to complement funding and avoid ï¬?scal disruptions from major disasters, which are main- ly floods, but also could potentially be used for hazards such as earthquakes, droughts, wind storms or extreme temperatures. The years 2009 and 2010 demonstrated once again the return of peak floods in Cen- tral Europe. This showed how signiï¬?cant the exposure and vulnerability to floods con- tinues to be for the V-4. As can be seen from experience in the last two decades, flood losses do not occur annually at catastrophic magnitudes; however, when they do occur, the impacts are signiï¬?cant in terms of overall economic losses and public expenditure commitments (see table 1.1). 1 2 A World Bank Study Table 1.1. Central Europe Floods: Economic Losses in Selected Years (in Nominal US$ Million Equivalent) Country 1997 1999 2001 2002 2004 2009 2010 Poland 3,500 — 700 — — 100 3,200 Czech Republic 1,850 — — 2,420 — 150 60 Hungary — 293 — — — — 358 Slovak Republic 60 113 — — 383 — — Source: Center for Research on the Epidemiology of Disasters (CRED). Note: — = not applicable. The Fiscal and Macroeconomic Framework and Its Relation to Disaster Impacts The ï¬?scal and debt adjustment process following natural disasters is dependent on ini- tial conditions in the ï¬?nancial sector’s structure and the ï¬?scal and/debt accounts. In this context, the report also considers the impact of natural disasters on public ï¬?nance sus- tainability by characterizing how government spending and debt typically responds to catastrophes. The strategies followed by different governments regarding the combina- tion of expenditures, revenues and borrowing depends in part on their access to loans or market ï¬?nancing, their cost, and the demand for government services, all of which are related to the degree of insurance in the economy. While this part of the study uses a much larger sample of countries worldwide to arrive at more reliable estimations of ï¬?scal response effects, this larger sample provide insights regarding government strate- gies, ï¬?nancial instruments, and market instruments to be er handle disaster costs in the V-4 countries. The analysis found that countries with more developed ï¬?nancial or insurance mar- kets suffer less from disasters in terms of output declines. The way this is achieved dif- fers in each case. In ï¬?nancially developed markets, governments are able to raise funds and increase deï¬?cits. This response helps alleviate the impact of the disasters. In coun- tries with high insurance penetration, the smaller impact of disasters on GDP occurs without a large ï¬?scal expansion. Financial markets and development institutions can thus help in the design and use of ï¬?scal insurance policies or hedging debt instruments to further diminish disaster consequences. Surprisingly, those countries that were more indebted seemed to be those with be er access to debt. Thus, debt levels on average also appear to proxy for be er access to capital markets rather than constrained ï¬?scal space. However, those with deeper insurance markets suffered less from rising ï¬?scal deï¬?cits when ï¬?nancing post-disaster growth (see box 1.1). Box 1.1. Fiscal and GDP Response Based on Insurance Depth in the Economy An analysis of insurance depth in the economy shows its effect on the recovery process following a disaster. Figure B1.1 shows the cumulative impulse response functions (IRF) for GDP, government expenditures, and government revenues. These are for climate-related disasters for a set of countries with high and low levels of insurance penetration. Time 0 (Box continues on next page) Financial and Fiscal Instruments for Catastrophe Risk Management 3 Box 1.1 (continued) (x axis) is when the disaster strikes. The solid lines show the impact of a type of disaster for countries with high levels of insurance penetration (thick black line) and for those with low levels of insurance penetration (solid thin line). The dotted lines show one standard deviation conï¬?dence bands. The availability of insurance results in a much better GDP recovery which in turn helps government revenues and allows for a less constrained expenditure path. De- tails and scenarios are further discussed in chapter 5. Figure B1.1. Cumulative Impulse Response Functions versus Insurance Penetration in Economy Climatic, GDP Climatic, government expenditure 0.060 0.100 0.040 IRS IRS 0.020 0.050 0.000 –0.020 0.000 –5 0 5 10 –5 0 5 10 Time in years Time in years Climatic, government revenue 0.150 0.100 IRS 0.050 0.000 –0.050 –5 0 5 10 Time in years Source: Melecky and Raddatz 2011. The Role of the EUSF in Disaster and Fiscal Management The EUSF was established in late 2002 in the aftermath of major European floods. The EUSF is used to partially compensate public budgets for damage suffered as a result of natural disasters. The eligibility to use EUSF funds becomes available once disaster losses equal EUR 3.0 billion or 0.6 percent of GDP, whichever ï¬?gure is lower. The amount of aid provided under the EUSF is 2.5 percent of losses below EUR 3 bil- lion equivalent, plus 6 percent of any losses above that threshold. If the loss, for example, is exactly EUR 4 billion, aid would amount to 2.5 percent of 3 billion (75 million) plus 6 percent of 1 billion (60 million) or a total of 135 million. For a large disaster, while EUSF outlays provide extra funding, they can at times be very small in relation to total losses (see table 1.2). Given its ‘trigger points’ the fund- ing is also small in relation to the average size of the economies of EU members. Hence there is a need to examine additional targeted risk management and disaster ï¬?nancing mechanisms that further reduce the gaps generated by emergency disaster funding out of government resources. 4 A World Bank Study Table 1.2. Outlays Incurred by the EUSF (EUR Million) Outlay 2002 2003 2004 2005 2006 2007 2008 EUSF total aid provided 728 107 20 205 24 445 281 Average amount of aid per event/country 182 18 20 205 12 74 56 Source: European Commission. Note: 2002: Austria, Czech Republic, France, and Germany; 2003: Spain (2), Italy (2), Portugal, and Malta; 2004: France; 2005: the Slovak Republic, Estonia, Latvia, Sweden, Lithuania, Romania (2), Bulgaria (2) and Austria; 2006: Greece and Hungary; 2007: Germany, France (2), United Kingdom, Greece, and Slovenia; 2008: United Kingdom, Greece, Slovenia, France, and Cyprus. Measuring the Level of Risk and Potential Financial and/Fiscal Impacts In the Czech Republic in 2002, the flood disasters amounted to 3.5 percent of GDP, put- ting strains on the government budget. Even though EUSF payments became available these arrived with substantial delays and were essentially used to reï¬?nance costs al- ready incurred. Such levels of losses generate economic and social vulnerabilities with associated disruptions that require immediate government responses and a quick reso- lution of the event impacts. This report therefore quantiï¬?es the exposures and risks, and provides policy and in- strumental solutions. Such solutions, including ï¬?nancial or insurance-like mechanisms which are meant to stabilize the ï¬?scal risks that may materialize, are used to supplement and render more effective the public actions needed to prepare for, and address future catastrophic losses whether they are caused by periodic natural perils or climate change induced events. The methodology for quantifying exposures and risks is shown in box 1.2. Box 1.2. Risk Modeling Methodology The methodological elements for estimating ï¬?nancial impacts used in this report comprise expert risk modeling techniques that link physical hazards with loss outcomes. The model elements used are shown in ï¬?gure 1.1 and essentially cover the stochastic, hazard, damage, and ï¬?nancial modules. Digital terrain mapping and risk modeling methods are used to create uniform high-resolution three-dimensional digital models of the earth-surface including eleva- tion data and geometric images based on geographic information systems, engineering and hydrology variables, and insurance risk factors, in order to determine the potential frequency and severity of major flood risks and associated losses (affecting the State’s resources as well as private sector assets). The sources of data on inventory and values of assets including infrastructure in the econo- my were obtained from statistical institute information classiï¬?ed according to EU standards. Where assets, buildings or infrastructure were not valued, monetary information was ob- tained from economic and market sources. Missing data was based on interpolating informa- tion from similar assets based on standard construction costs, population centers, income information, and other economic and social variables. The technical chapter 2 that follows, explains the methodology in detail and the currency and other conversion factors used for data on the four countries. Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. Financial and Fiscal Instruments for Catastrophe Risk Management 5 Figure 1.1. Technical Modules of the Catastrophe Risk Model Stochastic Module: Hazard Module: Generation of hazard event and Characteristics of hazard intensity statistical frequency distributions (flood level, terrain effects on flow), (historical data, scientific analysis, and hazard-effect distributions expert opinions). geographically and topographically. Damage Function: Financial Module: Calculation of structural damage and Loss quantification in money terms. vulnerability coefficients against Insurance contract pricing based on different hazard intensities exceedance probabilities of € or $US (engineering expertise, damage loss levels. Determination of capital experience). requirements. Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. Structuring Financial and Fiscal Instruments for Funding Losses Ex Ante For unfunded exposures, special forms of budgetary and ‘insurance-like’ ï¬?nancing should be considered. These can be used to fund both: (i) mezzanine (mid-level) catas- trophe losses before EU Solidarity Funding becomes eligible, and (ii) high levels of losses where EUSF funding is limited. In the ï¬?rst case, reasonable allocations of emergency funding as well as reallocations of public budgetary funds need to be identiï¬?ed before determining the gap between such funds and EUSF funding. In the second case, after EUSF funding is provided at the highest level of losses (predeï¬?ned in monetary terms) the unfunded part of these highest losses can be subject to special ï¬?nancing and macro insurance-type arrangements discussed below. Loss prediction tools, however, are ï¬?rst needed so that mezzanine mid-level and highest-level loss funding can be invoked based on accurately modeled losses. Mod- eled losses are estimated from physical phenomena (such as levels and intensity of wa- ter flow in key flood areas, earthquake intensities, or wind storms) that are correlated with historical damages and have a predictable relation to asset and infrastructure value losses (see box 1.3). Given the complexity of modeling floods and water flow, this report focuses on this particular peril which is the primary hazard in the V-4 countries and can be used as a “macroâ€? predictor of loss. Box 1.3. Parametric Trigger Events as an Alternative to Direct Loss Measurement Physical event threshold triggers can be used instead of ï¬?nancial loss levels. In the insur- ance/capital market industry parlance, the physical (versus ï¬?nancially reported) loss triggers are referred to as “parametricâ€? triggers. If a physical phenomenon such as a measured flood level can be associated reliably with potential losses to housing, buildings or infrastructure, then an insurance contract can pay for losses once that certain physical trigger is reached (for (Box continues on next page) 6 A World Bank Study Box 1.3 (continued) example, water/flood level) instead of paying out based on an ex post site-by-site examina- tion of actual losses (the latter being the “loss adjustmentâ€? method in the industry parlance). Flood magnitudes are thus directly associated with losses. To obtain estimates of such para- metric triggers in a reliable way, hazard distributions, exposure values, vulnerabilities, and loss estimations are ï¬?rst calculated. Following that, correlation simulations of flood levels (measured as volume of water discharged over a time period) are calculated against ï¬?nancial losses associated with such flood levels/discharges as per historical experience and pro- jected forecasts. This exercise can be done across a number of river basins within a country so that the measurement of flood events at each speciï¬?c location can be correlated reliably with predictable losses under robustly tested risk models. Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. Table 1.3 summarizes the link between the level of water flow and potential losses. The correlation results show country losses of EUR 500 million associated with the vol- ume of water (cubic meters/second) discharged during historical and projected disaster events for river catchments in each of the countries examined. Table 1.3. Water Flow Level/Discharge per River Catchment Correlated to a EUR 500 Million per Country Loss Czech Republic Slovak Republic Hungary Poland m3/sec m3/sec — m3/sec Odra-CZ Dunaj-SK Ipel-HU Odra-PL 2,410 14,014 — 4,344 m3/sec m3/sec — m3/sec Morava-CZ Vah-SK Dunaj-HU Wisla-PL 785 2,037 — 8,486 m3/sec m3/sec m3/sec m3/sec Dyje-CZ Hornad-SK Salo-HU Narew-PL 918 1,278 758 1,805 m3/sec m3/sec — Vltava-CZ Bodrog-SK Hornad-HU 2,997 2,032 — m3/sec — — m3/sec Labe-CZ Tisza-HU 3,751 — — 4,810 m3/sec Leitha-HU 276 Source: Aon Benï¬?eld, Czech Republic. Note: — = not applicable. While a ï¬?nancial contract based on flood levels is innovative and simpler to execute, it can result in errors known as “basis risk.â€? By using river-catchment flood measur- ing devices with automatic transmission of data to a centralized information station to prevent manipulation, ï¬?nancial/insurance contracts can be designed to pay countries predeï¬?ned sums if the water level exceeds the threshold levels. This can involve some “basis riskâ€? meaning that the flood level might occur (though with lower losses than projected) but the contract would still have to pay out. Alternatively, the flood level may not reach the threshold level speciï¬?ed in the “contract triggerâ€? (even though substantial damages occurred) but the contract would not pay out. These are risks in such an ap- Financial and Fiscal Instruments for Catastrophe Risk Management 7 proach, although its simplicity is beneï¬?cial and this “basis riskâ€? can be borne by national governments more easily than by a single private party. This is because governments op- erate on a large country-wide “asset portfolioâ€? of risks where such discrepancies would be smoothed out across the entire territory and over time. Country Loss Distributions on Account of Flood Hazards Historically, the four countries analyzed have experienced major flood events, aggra- vated by both heavy rain storms as well as higher average temperatures. Temperature increases at times generate a more voluminous water flow from melting ice and snow. While these countries can partake of EUSF which are essentially budget contributions from other member states in the event of major catastrophes, the funds ‘kick in’ at only very high level of losses as described, and only cover a small portion of such losses. Thus ï¬?scal disruptions occur well before EUSF assistance is activated for mega sized events. Figure 1.2 estimates the probabilities of occurrence and potential losses to state and/ public property and infrastructure from major flood events. The return period expressed in years can also be seen as an annual probability of occurrence or loss at the levels shown (in euro millions). This does not necessarily reflect a loss from a single event (such as one major flood) in a given year, but can also reflect the accumulation of several flood losses during the year which add up to the annual loss level. For example a 100- year return period means a 1 percent chance of the loss occurring annually. Figure 1.2. Individual and Pooled Country Loss Distributions 18,000 Czech Republic Slovak Republic 16,000 Hungary Poland V-4 14,000 Sum over individual countries 12,000 Loss (EUR million) 10,000 8,000 6,000 4,000 2,000 0 0 50 100 150 200 250 300 350 400 450 500 Return period (years) Source: Intermap Technologies. 8 A World Bank Study Given the rare catastrophic nature of these events, the 1 percent probability, howev- er, has a wide variation of uncertainty. Such an event can therefore at times occur more frequently within such a variation/range. As can be observed, for a 2 percent annual probability (assuming a 50-year return period) the four-country combined loss exceeds 4 billion euros (Sum curve). As this report demonstrates later, given the ratio of annual potential losses versus government revenues, and each country’s level of GDP, the exist- ing budgets of governments would be severely strained before EUSF payments (which take several months to process) come into play. This would thus call for an intermediate mechanism of risk ï¬?nancing, to supplement the liquidity needed to augment budget resources, even before the EUSF funds are invoked. The beneï¬?ts of pooling risks across countries and reducing costs, becomes evident. In the ï¬?gure 1.2, the “Sumâ€? curve shows the simple aggregate of the losses of each coun- try added together under the individual scenarios. Poland has the largest potential loss given that it is the largest economy with the most assets at risk. The “V-4â€? curve repre- sents the statistical pooling of the four countries under a combined loss portfolio. What is notable is that this loss curve has lower aggregate losses (and would thus have lower premiums if governments insured these assets) than the simple “Sumâ€? curve. This is because the “V-4â€? curve relies on lower overall standard deviations of losses given a larger number of event observations for the four countries combined, that is, a portfolio diversiï¬?cation effect that reduces volatility.1 In practical terms this means that if countries were to insure themselves individu- ally the cost of catastrophic coverage would be signiï¬?cantly higher than as a group. An insurer of any kind would cost the premiums based on individual country exposures which would have higher individual variances (requiring higher pricing). This is be- cause on a single-country basis, catastrophe events would occur less often than when observed as a group. If the countries combined their portfolios, the larger number of ca- tastrophe observations would reduce the statistical variances within the larger event set, and allow an insurer to price them with fewer uncertainties about predicted loss events, and thus charge lower “groupâ€? rates. The cost savings from pooling are directly quantiï¬?ed based on the V-4 combined loss distribution curve. Observing the graph above one can see that for a 500-year (0.2 percent probability) event, the summed losses for each country individually, amount to a potential loss of approximately EUR 16,000 million. Let us assume that a premium for insuring that entire amount (under a parametric trigger based contract) would be 2 percent of the loss covered, or EUR 320 million, which would be payable proportion- ately according to the risk level of each country. However, as a combined insured risk portfolio (the V-4 curve) the countries show a potential loss of EUR 12,000 million for the same event probability (due to the pooling effect and lower standard deviation of the combined country risks). Thus the premium, if kept at 2 percent in this case, would be EUR 240 million representing a collective savings of EUR 80 million, or a 25 percent reduction in the actuarial premium. Rationale for Government/Public Sector Insurance Needs The above analysis makes a strong case for consideration of a collective country ï¬?scal insurance mechanism to cover losses. Similar schemes have already been implemented in other parts of the world (see box 1.4). Several instruments can be utilized for this Financial and Fiscal Instruments for Catastrophe Risk Management 9 Box 1.4. An Operating Multicountry Catastrophe Risk Fiscal Insurance Pool: The CCRIF The Caribbean Catastrophe Risk Insurance Facility (CCRIF) is the ï¬?rst multicountry risk pool using parametric insurance policies. The policies are funded by initial capital as well as by insurance and capital market instruments. CCRIF is a regional catastrophe risk fund for Ca- ribbean governments developed with support from the World Bank to help mitigate the short- term cash flow problems that small economies suffer after major natural disasters. A critical challenge addressed by the CCRIF is the need for short-term liquidity to maintain essential government services until additional resources become available. Although post-disaster funding from budget sources, or bilateral and multilateral sources can be important ï¬?nancing sources, external assistance often takes months to materialize, and usually supports speciï¬?c infrastructure projects. CCRIF has in its ï¬?rst three years of operation, offered separate hurricane (wind) and earth- quake policies. Caribbean governments may purchase coverage which triggers a “one-in- 15-yearâ€? hurricane and a “one-in-20-yearâ€? earthquake, with maximum coverage of US$100 million available for each peril. The cost of coverage is a direct function of the amount of risk being transferred, ensuring no cross-subsidization of premiums and a level playing-ï¬?eld for all participants. There are four main reasons CCRIF was designed as a parametric facility: (a) payouts can be calculated and made very quickly because loss adjusters do not have to be relied on to es- timate damage after a catastrophe event, which can take months or years; (b) governments do not have to provide detailed asset values and other information prior to the insurance program commencing, and have just one form to sign during the entire claims process; (c) calculation of payouts is totally objective based on a few simple input parameters published in the public domain by the body responsible for estimating those particular parameters, and a set of formulae which form part of the policy; and (d) the risk which drives policy pricing is uniformly deï¬?ned (there is no subjectivity in its deï¬?nition). The parametric insurance program was developed using physically measurable indicators of known hazards to trigger payments. This allows the CCRIF to estimate the loss on the ground by using data from the National Hurricane Centre (NHC) in the case of hurricanes and the United States Geological Survey (USGS) in the case of earthquakes, and a proxy relationship developed under a pre-agreed catastrophe risk model. The information provided by the NHC and the USGS are in the public domain and so are available for scrutiny, as are the variables in the risk model. Under the CCRIF’s impact measurement process, the loss is calculated through an index. The index represents the hazard levels (wind, storm surge, and waves for hurricane, ground shaking for earthquake) which are used as the driving variables to establish proxies for loss- es. It is important to note that the object of the Facility is not to cover the entire losses faced by affected states, but to provide in case of a major adverse event, short-term liquidity to cover both disaster response and basic government functions. Payouts following disasters have been triggered under the CCRIF. In 2007, the CCRIF paid out almost US$1 million to the Dominican and St Lucian governments after the November 29 earthquake in the eastern Caribbean, and in 2008, CCRIF paid out US$6.3 million to the Turks & Caicos Islands after Hurricane Ike made a direct hit on Grand Turk Island. More recently, Haiti received a payment of US$7.75 million (approximately 20 times their premium for earthquake coverage) fourteen days after being struck by a devastating earthquake of magnitude 7.0 in January 2010. In September 2010, Anguilla received a payment of US$4.3 million following Tropical Cyclone Earl in August. In November 2010, after October’s Tropi- cal Cyclone Tomas, the CCRIF made payments to the governments of Barbados (US$8.6 million), Saint Lucia (US$3.2 million) and St. Vincent and the Grenadines (US$1.1 million). Source: World Bank, CCRIF. 10 A World Bank Study purpose. However, this does not mean that standard insurance contracts are a solution for covering potential ï¬?scal liabilities from natural and climate induced disasters such as floods, given that such standard contracts would require extensive on-site damage as- sessments and costly loss-adjustment procedures. Rather, certain macro/ï¬?scal insurance mechanisms may be more apt for this purpose and for uses by governments. Catastrophic losses can be very signiï¬?cant in relation to macroeconomic accounts. In terms of macroeconomic planning, flood disasters in Central Europe generate substantial resource impacts when compared to national GDP and government budget revenues. Table 1.4 shows the impact of relatively modest catastrophes on macro/ï¬?scal variables, for the V-4 countries, and where both total country assets (the sum of private and public property and infrastructure) are shown, as well as public property and infrastructure by itself. More severe events (such as those with an annual 0.5 percent likelihood) would escalate these losses signiï¬?cantly. Table 1.4. Effect on Budget Revenues and GDP, from Reference Catastrophe Floods Event Poland Czech Republic Hungary Slovak Republic 20-year event (5% probability) Euro amount Total property (€ million) 2,508 768 966 1,191 Public property (€ million) 904 245 248 412 % of Revenue Total property 2.2 1.4 2.3 10.7 Public property 0.8 0.4 0.6 3.7 % of GDP Total property 0.8 0.6 1.0 1.9 Public property 0.3 0.2 0.3 0.6 100-year event (1% probability) Euro amount Total property (€ million) 9,954 3,405 4,235 5,452 Public property (€ million) 3,586 1,086 1,087 1,887 % of revenue Total property 8.6 6.2 10.0 48.8 Public property 3.1 2.0 2.6 16.9 % of GDP Total property 3.2 2.5 4.6 8.5 Public property 1.2 0.8 1.2 2.9 Source: Intermap Technologies and IMF International Financial Statistics, 2008 and 2009. In terms of loss ratios, for a 100-year 1 percent probability event, the impact from flood losses is very disruptive in relation to government revenues, and has signiï¬?cant loss impacts versus GDP. A 20-year event is less so, but not insigniï¬?cant. For total public and private property, the losses from both events represent a signiï¬?cant as a percentage of budget revenues, with the Slovak Republic being highly affected (due to a smaller government sector) and with signiï¬?cant effects for Hungary and Poland. For public property exposure alone, under the 1 percent annual probability, the Slovak Republic and Poland appear most vulnerable. Interestingly though, in recent years, except for 2004, the Slovak Republic has not suffered from flood damages as much as its other three neighbors, though this is not a reason for complacency given its assessed risk exposure. Financial and Fiscal Instruments for Catastrophe Risk Management 11 Large losses below and above EUSF eligibility thresholds present major ï¬?scal chal- lenges. Total losses as a percentage of GDP range from 2.5 percent to 8.5 percent for the V-4 countries for a 1 percent probability event. For public property, only the Czech Republic’s losses are under 1 percent of GDP (and 2 percent of budget revenues). What is worth noting, is that for the 5 percent (20-year) probability event, with the possible exception of the Slovak Republic, none of the countries would be able to access solidarity EUSF funds if public sector losses fall below the threshold for access. In any case, even when private sector losses are included and eligibility for tapping EUSF resources exists, there are clearly signiï¬?cant public expenditures that would need to be deployed imme- diately for losses below the EUSF threshold, as well as to bridge expenditures pending the receipt of EUSF funds plus signiï¬?cant losses above and beyond amounts provided by the EUSF. Budget reserves and insurance are thus complementary measures to be used as ï¬?s- cal tools for disaster management. As mentioned, given the large variance and uncer- tainty of catastrophes, a 100-year event represents an average forecast and does not nec- essarily mean that the event will occur at that magnitude only every 100 years. Prudent government risk managers would actually budget reserves representing 1 percent of the loss each year to ensure funding for such a “bigâ€? event.2 As described further below, budget reserves are not necessarily the most optimal or efficient form to fund and protect against these disasters. While governments should count on budgets for relatively low-level losses, and on the EUSF for a portion of ex- tremely high losses, the middle or mezzanine level of losses, and losses above EUSF funding eligibility should count on much more optimal risk management tools to stabi- lize ï¬?scal outlays. Market Insurance and Private Property Coverage Flood catastrophes inevitably affect private property, particularly homes and dwellings, although the catastrophe insurance market is quite well developed in the V-4 countries. Insurance penetration (expressed as percentage of homeowners with catastrophe insur- ance) averages between 50 and 75 percent. This is positive and given that not all regions of any particular country are subject to catastrophic risks, it represents a rather high insurance penetration level. The unique feature of the V-4 markets is that catastrophe insurance perils are tradi- tionally included in the overall scope of coverage under homeowners’ policies. In most other markets this represents an optional policy ‘endorsement.’ As a result, almost all households with a ï¬?re policy in the V-4 countries are automatically covered against floods, windstorm, landslides, hail and avalanches. In the Czech Republic, most insurers charge an adequate risk premium for the catastrophe portion of the risk while in Poland the risk premium is driven more by market competition. Reinsurance for individual country subsidiaries is typically arranged in a central- ized fashion through reinsurance departments of parent companies. The parent compa- nies in turn place the cover for the whole group. This approach allows realizing consid- erable savings which translates into lower premiums for homeowners. In the case of the Polish state-owned insurer, the sheer size of the company allows it to pool the risk on a country-wide basis, which results in a well-diversiï¬?ed risk exposure and highly afford- able pricing. 12 A World Bank Study However, despite a relatively high level of insurance penetration, governments still carry a considerable budgetary exposure to catastrophic floods. Governments of the V-4 countries may consider changing the existing post-disaster compensation policies for housing reconstruction by introducing a strong element of private responsibility for losses inflicted by natural disasters. Such a policy change would likely increase insur- ance penetration among homeowners and would also help signiï¬?cantly to reduce gov- ernment ï¬?scal exposures to the private sector against natural disasters. Based on the above factors there does not appear to be a case for government sup- port for enhancing the capacity of the private catastrophe market in the V-4 countries. The market counts on a robust insurance industry and reinsurance support from the major European reinsurers, Munich Re and Swiss Re. Based on total country exposure and potential losses, further studies regarding the capacity of the insurance industry to absorb major losses (and its requisite capital) would be prudent, coupled with strength- ened regulatory monitoring and risk assessment techniques, in order to ensure that ac- tuarially such risks are well quantiï¬?ed, and covered by adequate appropriate insurance reserves. (See chapter 4 for more detail). The Range of Catastrophe Insurance Instruments Before laying out a proposed ï¬?nancial structure for pooling the V-4 countries’ flood risks, recent developments in ï¬?nancial mechanisms for disaster contingencies should be disclosed. As shown in table 1.5, there are a range of ï¬?nancial instruments used in the past and more currently, to handle catastrophic risks. Table 1.5 shows in the ï¬?rst column, instruments which fully transfer the risk of loss to another party in exchange for a pre- mium. The premium payment is a method of smoothing losses over time without the ï¬?scal shocks that an outlay for single major loss creates for a country. Table 1.5. Insurance and Financing Options Insurance (risk transfer) instruments Loss ï¬?nancing instruments Indemnity insurance Budget reserves Parametric insurance/reinsurance Contingent loans Catastrophe (“catâ€?) bonds Bond guarantees Catastrophe swaps Catastrophe equity puts Exchange-traded catastrophe options (index-based) Contingent notes Weather derivatives Contractual/contributory solidarity payments Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. However, these instruments (risk transfer, insurance) should be only used once oth- er funding instruments have been exhausted or when they are no longer cost-effective. This would occur in situations where budget or debt ï¬?nancing are insufficient or uneco- nomical from a medium-term ï¬?scal perspective. In the second column, the instruments shown are purely ï¬?nancing instruments. They do not transfer the risk of loss as in in- surance but typically do not require any prior payment if no loss occurs. The different instruments are briefly described in box 1.5. The deployment of debt for disaster ï¬?nancing is a useful instrument for smoothing losses but should be limited to certain thresholds of losses. Debt ï¬?nancing for disasters in the low to mid-level range of losses can result in being used frequently, thus building Financial and Fiscal Instruments for Catastrophe Risk Management 13 Box 1.5. The Range of Catastrophe Insurance Instruments Risk Transfer Instruments. The traditional form of insurance is Indemnity Insurance. A premi- um is paid annually in exchange for payment covering a loss if it occurs. However, the loss has to be on the speciï¬?c property that is insured. For that reason it “indemniï¬?esâ€? the policy holder for the particular loss on the asset contractually insured. An insurance loss adjuster needs to verify this before the payment is effected. As discussed, Parametric Insurance or Reinsurance works more as a ï¬?nancial option contract where once surpassing a threshold of a measurable physical event or physical index (such as temperature) it results in a promise to pay a predeï¬?ned sum of money, regardless of what the actual level of damage is. The difference between actual and ‘modeled’ damage is called ‘basis risk’ but for large portfolios such as a country’s infrastructure, this risk is minimized, versus when applied to individual properties. Catastrophe (“catâ€?) Bonds can take the form of either indemnity or parametric insurance though more recently they have been mostly parametric. As further described below, they work just like parametric insurance contracts except that the insurer is an investor who receives a bond yield in lieu of a premium payment. While maturities of cat bonds vary, most congregate around the three–four year maturity range. The investor/purchaser is at risk of ‘default’ or losing his/her capital if the catastrophic event occurs, in which case the ‘insured’ party can use the bond princi- pal proceeds to pay for losses. Catastrophe Swaps, another instrument, are simply a portfolio or instrument swap. One party who may hold catastrophe insurance risk (either as a cat bond or an insurance portfolio) can swap such instruments with another party in exchange for another type of portfolio (this can be either a regular bond/ï¬?xed income portfolio or a catastrophe portfolio but covering another type of risk or hazard than the one swapped). The objective is to diversify portfolios and thus minimize risk concentrations. Exchange Traded Catastrophe Options are ï¬?nancial options which are triggered when industry- wide losses (or subregional industry-wide losses) in a given country, exceed a certain threshold which then allows the option to pay out. Typically the losses are expressed as an index (for example, the Dow or S&P being a constructed index) rather than as monetary amounts, to make measurement easier. Insurers that have potential losses associated with the index would ï¬?nd such contracts advantageous. Weather Derivatives are also option-like contracts which get triggered when temperatures (sometimes called the temperature ‘index’) exceeds or falls below a certain level. Such contracts are useful for both energy companies (who want to get compen- sated during extreme temperatures) as well as agricultural producers who could suffer crop losses with extreme degrees. Risk Financing Instruments. These instruments are primarily funding mechanisms adapted to the needs of catastrophe losses. Budget Reserves as practiced by the V-4 countries, re- flect allocations made annually for sole use in funding public losses (and some low-income private household losses) from disasters. Typically such reserves do not cover mega events in which case additional budget reallocations are made. Contingent Loans are pre-negotiated undisbursed loans which can be invoked quickly in the event of a major disaster to supply fast liquidity. Multilateral institutions as well as private commercial banks offer such facilities. Bond Guarantees can be considered supranational guarantees for bonds issued by a country in the aftermath of a disaster to raise funds. While countries can issue bonds without such guarantees, the latter can lower the ï¬?nancing cost during a period where economic and ï¬?scal stress may have been generated by the disaster. Catastrophe Equity Puts have been used by private companies and reflect pre-arranged con- tracts for a company to issue shares to raise funds if a disaster has affected its operations. This requires prior agreement with investors who are willing to purchase shares after such an event. Contingent Notes are similar to catastrophe equity puts but entail the issuance of notes or bonds to pre-agreed creditors who would invest in them to provide a company with liquidity in the event of a disaster. They would usually be priced above the going bond rate for such a company thus providing a yield beneï¬?t, but are not catastrophe (risk transfer) bonds. Contractual/contribu- tory solidarity payments are multiparty contributory arrangements such as the EUSF discussed above where an economic community of sovereign or sub-sovereign entities contributes to a common “potâ€? to assist members. Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. 14 A World Bank Study up unsustainable debt. Larger disasters require more ï¬?nancing but they occur less fre- quently. However, insurance premiums are also more expensive for frequent low-level losses as a payout claim would be more likely. This would therefore call for using debt ï¬?nancing at less frequent, higher level loss- es (where repeat uses of debt would also be less likely). Insurance should be used at mid- level to high levels of losses (where premiums will not be so high versus at lower levels with more frequent losses). The optimality of the ï¬?nancing instrument can essentially be determined by conducting simulation modeling covering periods of several years, to demonstrate the cost effectiveness of each. Structuring Catastrophe Mechanisms to Optimize Coverage at the Lowest Cost Based on the several indicators and approaches discussed, complementary catastro- phe ï¬?nancing mechanisms for the V-4 countries need to take into account three key considerations: â–  Does the loss level of ï¬?nancing falls below the eligibility level of EUSF funding and substantially above budget funding available to make such a scheme worthwhile. â–  If risk transfer mechanisms are used, are the beneï¬?ts of risk-pooling signiï¬?cant enough, or should ï¬?nancial mechanisms be used on a single country basis. â–  If risk transfer mechanisms are used, what type would be best suited, and should these mechanisms be supplemented by ï¬?nancing instruments such as contingent debt to optimize the long-term coverage/cost ratio and beneï¬?ts. In terms of economic loss and ï¬?scal risk considerations, it is clear that a “mezza- nineâ€? layer of funding as well as a supplementary top layer are crucial to affront ex- pected disaster costs. As table 1.4 earlier showed, a 5 percent probability event (a 20-year event) would cause signiï¬?cant losses to public property ranging from 2.2 percent to 10.7 percent of the respective governments’ revenue bases. This would also generate losses ranging from 0.3 percent to 0.6 percent of GDP for public property alone and from 0.8 percent to 1.9 percent for all properties. A 1 percent probability event would cause losses that are multiples higher. Several of these losses fall above the budget capacity of the countries and below the level where the EUSF funds may be invoked. Even when the EUSF funds are triggered, alternative funding mechanisms will provide quick needed liquidity and bridge ï¬?nanc- ing for immediate recovery of destroyed assets and infrastructure life lines, as well as to supplement the loss ï¬?nancing at the topmost levels that cannot be covered by EUSF funding even when invoked. The ï¬?nancial efficiencies obtained from multicountry pooling can be a deciding factor as to whether governments should use insurance-style mechanisms. In the country loss distributions shown earlier, and as discussed below, the beneï¬?ts of pooling are substantial in terms of cost savings given the diversiï¬?cation advantage accruing to the insurance risk- takers or investors, in pricing this type of risk. Pooling can also be set up with an element of pure risk-based pricing across countries which beneï¬?ts both the V-4 as a group as well as individual countries with risk-speciï¬?c proï¬?les without subsidies between countries. A versatile mix of ï¬?nancial instruments will optimize the ï¬?scal stability and debt minimization strategies of governments. Financing (versus risk transfer) mechanisms should be considered if the probabilities are so clear as to make a least cost case for us- Financial and Fiscal Instruments for Catastrophe Risk Management 15 ing debt ï¬?nancing versus insurance instruments. As mentioned, ï¬?nancing should not be placed at overly low (more frequently recurring) loss levels since debt can buildup, un- less the terms of such debt are sufficiently favorable so as to make such ï¬?nancing cost ef- fective. Another option is to use favorable ï¬?nancing terms to fund insurance premiums or cat bond spreads——if the low cost debt is of a sufficiently long maturity, in which case the amortization of the premiums is spread out and, coupled with low ï¬?nancing rates, minimizes the impact on the budget. Costs of Catastrophe Risk and Associated Market Pricing The pricing nature of catastrophe instruments should be assessed across different instru- ments to make informed decisions regarding their long-term net costs. It is easiest to understand the instruments of insurance (including cat bonds) and risk ï¬?nancing instru- ments (loans, or standard bonds) and their pricing, when expressed in equivalent terms such as an interest rate spread over a risk-free rate. An excess-of-loss (XL) insurance contract typically used to cover only high catastrophe level losses, could, for example, command a premium equivalent to 3.7 percent of the amount insured, or expected to be paid out, if the qualifying event occurred. As described in box 1.6 a cat bond is really an insurance contract with investors. It pays an interest rate which should reflect the insurance risk plus a basic risk free rate. Thus, if we use Euribor as a proxy for the risk-free rate (though admi edly it is margin- ally higher than an entirely risk-free rate), an insurance-like premium paid as interest to a cat bond investor could be equal to a 2.5 percent insurance-risk spread, plus a Euribor or similar “risk-freeâ€? base rate of 1.2 percent, for a total 3.7 percent interest rate paid on the catastrophe (“catâ€?) bond. The cat bond rate of return thus has several components. As can be seen in ï¬?gure 1.3, the ï¬?nal bond return comprises the basic risk free rate obtained from investing the bond Box 1.6. The Equivalency among Catastrophe Risk and Capital Market Instruments Disaster Occurrence as a Market Default. The excess of loss (XL) insurance rate for a ca- tastrophe level risk can also be seen in the context of a typical ï¬?nancial market instrument. In this regard the catastrophe probability risk spread is akin to a regular bond’s default spread. If a corporation, for example, had to pay a bond spread of 2 percent over the risk free rate, the market would assume that its probability of default was 2 percent. Similarly in the case of a cat bond insurance contract, the spread above the risk free rate represents both the possibility of the disaster occurring (and triggering the contract), or the possibility of the bond defaulting. The default would effectively take place if the disaster occurred, as the bondholder would lose his principal which would be used to pay the ‘insured’ party for the disaster loss. Operation of a Cat Bond. The pricing of a cat bond was briefly described above. However, it must be noted that the cat bond spread only represents the insurance risk but not the credit risk of the issuer. This is because the issuer is not the “insuredâ€? party as one would expect. In a cat bond transaction, as illustrated below, the ‘insured’ party (for example, the govern- ment) only pays the premium or risk spread for ï¬?nancial protection from catastrophes. The bond issuer is a legally separate and de facto protected special purpose vehicle (SPV) which is immune from any claims or from credit quality of the ï¬?nal recipient. This allows the SPV to be rated AAA. Being that it is a single purpose legal entity it does not carry any of the risks associated with SPVs or SIVs publicized during the 2008–09 ï¬?nancial crisis.3 (Box continues on next page) 16 A World Bank Study Box 1.6 (continued) An Alternative Nontraditional Design. An SPV is not essentially needed, however. If the government wished to be the direct issuer this could be done as well. However, in such a case the investor might require an additional spread for any existing sovereign credit risk. Any such spread could be reduced though, via the use of supranational credit guarantees to investors, which, while costing a modest fee to the issuing government, might raise the bond rating to AAA thus reducing the credit risk spread by a larger proportion than the guarantee fee cost itself. Features of the Special Purpose Vehicle. In the typical design, the SPV has three main functions: (a) to provide absolute credit risk protection to bondholders as per above, (b) to serve as the de facto insurance conduit providing the government catastrophe coverage, and (c) to issue the bonds to the investors and collect the risk premium from the government or other beneï¬?ciary of the insurance. A trust account is set up wherein the bond proceeds are deposited earning a risk free or similar rate. By earning a risk free rate, the government no longer needs to pay that portion of the bond spread as it is already paid out of investment returns from the trust. Thus the government only needs to pay the “insurance risk spread.â€? Investing in cat bonds has a signiï¬?cant “upsideâ€? for several investors. The approach described (also summarized in the diagram below) reflects a typical arrangement for inves- tors in cat bonds. While it may appear like a loser’s gamble, the beneï¬?t for investors are twofold: (a) they receive a relatively high yield compared to other market instruments (in the event of no default), and (b) the cat bond instrument helps diversify their portfolios with a risk (that is, natural or climate disaster related) which is not correlated to other ï¬?nancial market bonds or securities. Since cat bonds ï¬?nance losses beyond a certain probability (for example, in the 1 percent range of probability) investors also consider that the “default riskâ€? is worth taking to obtain the beneï¬?ts described. Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. Figure 1.3. Catastrophe Bond Financing Structure Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. Financial and Fiscal Instruments for Catastrophe Risk Management 17 proceeds in a protected account (with a 1 percent return assumed) plus the insurance spread reflected in a variance-adjusted probability of the occurrence of the catastrophic event (assumed at 2.5 percent). The total bond coupon interest rate (annualized) paid to investors is thus 3.5 percent in the example below. It should be noted that there is no risk of principal default due to credit risk. This is because the government (or any other client) does not hold the funds nor is responsible for directly repaying principal. This is paid out of the trust account via the special pur- pose vehicle (SPV) if no catastrophe occurs to trigger any loss payment. As mentioned, if the catastrophe event triggers, then the bondholders lose their principal and the funds in the trust are transferred as an insurance payment to the government. Comparing Insurance Premiums with Contingent Loan Pricing In comparing debt ï¬?nancing versus insurance-type instruments, the long-term effect of expenditures and ï¬?scal outlays for each ï¬?nancial instrument, needs to be quantiï¬?ed. Price comparability requires multi-period simulation since the instruments’ funding characteristics and contractual conditions are unique. An approximation of equivalent pricing can be visualized under speciï¬?c catastrophe scenarios (for example, probability- based events) where each instrument can be tested for its cost effectiveness. This is de- scribed in box 1.7. Box 1.7. Equivalence of Debt versus Risk Transfer (Insurance) Instruments Occurrence versus non-occurrence scenarios need to be ï¬?rst considered. If a probabili- ty of occurrence of a major flood event in a given river catchment in the Czech Republic is say, 5 percent in a given year (that is, p = 5 percent), and a EUR 300 million loan, is considered to fund such a loss, if it is priced at 3 percent over Euribor then the average annual payments, including principal and interest, over an assumed 10-year maturity period, would provide a “premium-equivalentâ€? annual rate of approximately 22 percent for the total cost of such credit during that time period. On the face of it, this seems highly expensive. However, since there is a high probability (95 percent, the converse of 5 percent) that the credit will not be utilized at all, then the expected cost of such an instrument must weigh the two possibilities. Thus, the projected cost of the loan would be: p × (22 percent) + (1 − p) × (0 percent), where p is the 5 percent occurrence probability and 22 percent is the all-in cost of the credit expressed as a ï¬?xed level payment (of principal and interest) over the average loan balance. No grace period is assumed. This therefore equals: 5 percent × (22 percent) + 95 percent × 0 percent = 1.1 percent, which is lower than the insurance premium of 2.5 percent discussed above. However, if the loan (or an additional loan) needed to be utilized again during the same time period, then the cost would of course rise and begin multiplying. Of course, once the loan is utilized, its effective overall cost of 22 percent (if treated as an insurance contract) is substan- tially higher than paying an insurance premium (which has no principal repayment) or a cat bond spread; as the loan is fully repayable debt and there is no risk transfer. (Box continues on next page) 18 A World Bank Study Box 1.7 (continued) Advantages of Loan and Insurance Instruments. The loan becomes more cost effective at less frequent probabilities since in such cases, it is likely to be utilized less, in which case the cost of non-use is zero or close to zero.4 However, since estimates of catastrophe prob- abilities require a large variance/range around the mean probability (which is one reason why insurers price premiums at a multiple of the expected loss probability) provisions must be made for such a variance. An incorrectly speciï¬?ed probability or a higher adjusted probability (for example, due to climate change effects), may indeed make insurance pricing more attrac- tive if debt ï¬?nancing is triggered too often. While typically more expensive, insurance does not face the risk of unexpected large repayments and can be more easily built into public budgets as uniform level annual payments. Source: World Bank. Optimal Structuring of Funding Mechanisms for Loss Coverage Combining the available ï¬?nancial and budgetary instruments would optimize govern- ments’ ï¬?scal risk management against disasters. As discussed, for Poland, the Czech Republic, Hungary, and the Slovak Republic individual country disaster ï¬?nancing struc- tures can be designed, as well as combined/pooled structures to gain diversiï¬?cation and pricing beneï¬?ts accruing to the four countries. Any pooling structure would apply pri- marily to the middle mezzanine funding layer and above, as shown in ï¬?gure 1.4, since the lower layer represents national government budget reserves for disasters, and the topmost layer is partially covered by EU solidarity fund payments. The mezzanine layer can also overlap with the top layer in order to provide quick liquidity and bridge ï¬?nanc- ing while EUSF payments are processed and to fund any losses above those not covered by the EUSF. In the case of Poland, for example, the mezzanine layer could cover public property/infrastructure losses amounting to around 0.3 percent of GDP (as per table 1.4) under a loss scenario with a 5 percent annual probability. Figure 1.4. Proposed Disaster Risk Funding EU SOLIDARITY FUND + RISK TRANSFER/INSURANCE Loss in RISK TRANSFER/INSURANCE EUR billions LAYER (MEZZANINE) BUDGET RESERVES Source: World Bank. Financial and Fiscal Instruments for Catastrophe Risk Management 19 If the mezzanine funding layer is speciï¬?cally examined (see ï¬?gure 1.5 for an expan- sion of “mezzanineâ€? box from ï¬?gure 1.4), it can comprise a hybrid of instruments to op- timize the ï¬?nancial cost of catastrophic coverage. In the example of the structure given, a contingent loan is used to cover a pro-rata portion of the potential loss, while risk transfer instruments in the form of a catastrophe bond and/or a parametric insurance policy provide the remainder of the coverage. In the case of a catastrophe bond under a multicountry pooled structure, each country would pay the calculated portion of the insurance risk spread to the SPV which would issue the ï¬?nal bond to investors. Further below is the discussion of risk-adjusted spreads for each country, based on their respec- tive loss exposures and probabilities. Figure 1.5. Risk Coverage Mezzanine Layer CATASTROPHE BOND CONTINGENT LOAN LOAN PARAMETRIC INSURANCE Source: World Bank. Applying a Parametric Insurance Structure Under the parametric-based contract, the flood level itself is the payout trigger. The parametric instrument can be used both with the cat bond instrument shown in box 1.6 as well as under an insurance policy within box 1.8, the “parametric insuranceâ€? box (the la er can be offered by an insurance arm of a major international reinsurer such as Swiss Re or Munich Re). The parametric contract works like a ï¬?nancial option: if the techni- cal measured flood event is recorded above a certain threshold (such as flood height or water flow volume) the ï¬?nancial/insurance contract will pay out a pre-speciï¬?ed amount. Thus the flood level needs to be associated (with a degree of modeling accuracy) to ac- tual historical or forecasted losses. The contract does not pay on a property-by-property basis since this mechanism is geared toward national governments with country-wide asset portfolios and ensuring a streamlined approach. The trigger a ributes can also be custom designed. In the example that can apply to both a parametric insurance contract or a catastrophe bond as explained earlier, the insurance payment trigger can be further calibrated depending on the flood height level and on the location of the flood in relation to population centers. The parametric con- tract triggers thus need to be speciï¬?ed according to the flood magnitude and location a ributes, with payment amounts related to asset value characteristics of each country (and city). In the illustration in box 1.8, the principle is that, as the flood height becomes larger, the insurance payout becomes greater as well. In this regard, parametric contracts need not be only binary and can be calibrated along a graduated scale. 20 A World Bank Study Box 1.8. Parametrically Triggered Payments Calibrated with a Two-Factor Index Scale Measurement and Application of the Trigger. The payment is higher if the threshold flood trigger is breached within the city center (or within the perimeter established; see ï¬?gure 1.6) and lower if the threshold flood is breached outside the central city limits (this is designed as such since typically asset values outside the perimeter are less concentrated and would thus suffer lower losses). The percentage (%) ï¬?gure refers to the total available ï¬?xed limit insur- ance coverage under the contract for a worst case scenario (in this instance a full 100 percent payment occurs either when the flood height is 70 meters in the inner city grid, or 100 meters in the outer grid). In all other scenarios, the payment would only be a proportion of the total as shown in the threshold payment trigger table. Technology and Data Validation. As such mechanisms require flood measurement equip- ment in each river catchment in each country, such equipment and its technical standards would need to fully certiï¬?ed across all countries. To enhance credibility and conï¬?dence in a multicountry scheme, a radio satellite device that picks up the flood readings from the equip- ment, should be attached to each site and transmit the flood data to a central ofï¬?ce (for ex- ample, regional emergency or monitoring station) immediately so that the flood reading is not reliant solely on local based staff. This ‘central data collection’ location is also important for communication to investors (under a cat bond contract) so they can rely on one centralized location for reporting on whether triggers were breached or not. Annual Event Occurrence versus Annual Cumulative Threshold. An alternative method to structure and interpret the contract triggers would be to make them cumulative or equiva- lent to several events, within an annual period. Thus, instead of the contract paying out only when the flood exceeds a certain level in a given location, it might be speciï¬?ed so that given flood levels receive certain “weights.â€? If those flood levels and weights occur frequently within a year, the sum of the “weightsâ€? can be used to build a composite variable which if exceeding a pre-speciï¬?ed ‘composite weight’ threshold, would allow a contractual payment to be made even if this was not caused by one single event. This is somewhat akin to the case of Marcelo Rios (a Chilean tennis player) who achieved top seed status in world tennis in 1998 by win- ning the several smaller competitions, but without having won any of the Grand Slam Cups (that is, US Open, French Open, Wimbledon, Australian Open). Source: World Bank. Figure 1.6. Threshold Payment Triggers Flood height 40 70 100 Outer grid of city (in meters) % insurance payment 40 100 Inner grid Inner grid of city % insurance payment 20 60 100 Outer grid Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. Financial and Fiscal Instruments for Catastrophe Risk Management 21 Global capital market capacity allows an efficient diversiï¬?cation of insurance sources. The global insurance/reinsurance market capacity and its available capital may not be sufficient to cover large new risks such as those for central European floods. This would be determined by a market test and contract bids, and would also depend on the levels of coverage sought. This is why in the earlier diagrams, the ‘parametric insurance’ layers were split by cat bonds and parametric insurance to diversify and take advantage of additional funding sources to transfer such risks. For parametric contracts, a much broader funding source is the cat bond market since the capital markets have over 100 times the exposure capital than that of global insurers and reinsurers. Risk-Based Pricing (Based on Loss Exposure and Probability) Individual country pricing under pooled funding arrangements should be risk based to avoid cross-subsidies. One key concern of governments when participating in pooled insurance structures is the fear that they will be subsidizing their riskier neighbor(s) who will suffer more of the losses. A parallel debate is occurring in the banking realm within the auspices of the Financial Stability Board (FSB) with regard to burden sharing for potential cross border deposit insurance rules. Under flood insurance pools as described in this report, it is very straightforward to add risk-based elements to the pricing to mini- mize cross-subsidization across countries. The pooling effect in itself reduces the pricing margin for all participants anyway due to the larger diversiï¬?ed portfolio. A solidarity element is also an option that can be considered. In table 1.6, alternate design options are shown regarding the ï¬?nancial risk-spread pricing cost for certain levels of coverage. In table 1.6, based on the country loss distri- bution analysis, an aggregate loss of EUR 7.6 billion is used for the countries combined. Table 1.6. Price Advantages of Pooling for Country Flood Insurance (Based on a Combined Aggregate Loss of EUR 7.6 billion) Individual pricing Countries pooled with Pooled with solidarity Country non-pooled risk pricing risk pricinga Poland 4.50% 3.02% 2.35% Slovak Republic 2.40% 1.61% 1.64% Czech Republic 1.50% 1.01% 1.34% Hungary 1.60% 1.07% 1.37% Average expected loss probability 1.00% 0.67% 0.67% Average spread/premiumb 2.50% 1.68% 1.68% In euros Sum of individual premiums 190,000,000 Sum of pooled country premiums 127,300,000 Savings from pooling 62,700,000 Source: World Bank. a. Solidarity pricing means that half the pooling beneï¬?ts are shared equally—the other half are allocated by risk to each country (i.e., those with lower risk ge ing the most remaining half beneï¬?ts). b. Over Euribor, which is currently around 1.2 percent. The spread reflects the pure risk probability pre- mium (expected loss probability) plus the variance uncertainty premium (risk load) around the mean of the risk probability. 22 A World Bank Study In the table’s ï¬?rst column, is the ï¬?nancial spread that each country would pay if they would individually contract such insurance or use a cat bond on their own.5 The second column shows the reduction in each country’s ï¬?nancial spread or ‘premium’ if the pub- lic property/infrastructure was insured as a pooled portfolio. The savings in premiums (or ï¬?nancial spread) amount to EUR 62 million or 33 percent of the non-pooled amount when summed. Contract sizes can also be scaled to individual country funding preferences. It should be recalled that since these are parametric contracts, they are structured as ï¬?nan- cial options triggered by threshold flood levels, therefore the total coverage amount can be adjusted accordingly (for example, one country may wish to have a coverage of EUR 100 million and another EUR 1 billion) according to each country’s preference. This is because the loss payments that are made, are based on the technical model and triggers and not on actual damages sustained by individual assets on the ground. Of course the intent of the model is to capture a similar level of losses, given a speciï¬?c flood magni- tude. The scaling down of any contract coverage therefore, would result in a propor- tional reduction in the risk premium, to an amount equivalent to the individual country risk spread multiplied by the coverage amount desired. The probability levels (and thus the end-pricing) can also be adjusted depending on whether a government wishes to fund more frequent flood losses or rarer events. While the pooling and/aggregation beneï¬?ts are obtained up-front by all countries, solidarity pricing elements can also be built in. In such a case, and as shown in the third column of table 1.6, this would entail pricing for half of each country’s risk based on its proportionate loss probability (that is, risk-based) and the other half split evenly among all participants (that is, a non-risk based, solidarity element). No more than this is rec- ommended as a solidarity element, as any higher amount would result in over-subsidi- zation by lower risk countries. Alternatively, the pricing can be left as purely risk-based price in which case even the highest risk country will still beneï¬?t from the pooling effect. The amounts shown above are entirely illustrative and could just as well constitute 1/10th or 1/100th of coverage levels individually selected by each country, with commensurate premium levels for such lower coverage amounts. Pooled pricing as a portfolio, results in lower ï¬?nancing spreads than is achieved at individual average country risk spreads. As can be seen, the sum of loss probabilities for the four countries in the above example, in order to reach the aggregate loss level shown, represents a 1 percent probability when countries insure alone, but a 0.67 percent probability when they pool their risks. Due to the variance uncertainty of mega events the insurance pricing raises the spread to 2.5 percent, on average, when countries insure individually, since each country is assessed as an independent portfolio with a corre- sponding variance volatility. Pricing is reduced to a 1.68 percent average cost spread when pooling the risks, versus the 2.50 percent average spread when insuring separately. Table 1.6 shows the average individual and portfolio costs and spreads for the cases of (a) individual based pricing, (b) pooled pricing fully adjusted for country risks, and (c) pooled pricing with half of the pricing based on country risks and half of the pooling beneï¬?ts distributed equally among participants as a solidarity element. Financial and Fiscal Instruments for Catastrophe Risk Management 23 Summary of Risk Management Efï¬?ciencies Applied to Speciï¬?c Instruments The choice or combination of funding instruments for the mezzanine and top levels of ï¬?scal disaster coverage will require ï¬?nal market pricing tests. These include bids, avail- able terms, more detailed physical risk modeling, and loss probability estimations before obtaining ï¬?nal ï¬?gures for loss probabilities and risk pricing. However, based on the pre- liminary results of this study as reflected in the above analysis for parametric contracts, the pricing should have a range close to these ï¬?gures. In some instances additional ï¬?nancial service fees will exist beyond the risk spreads alone. Due to underwriting and other fees, a catastrophe bond contract for example may have a higher “all-inâ€? cost. It will also have a coupon rate would include the risk free rate plus the risk spread whose pricing may need additional market testing. Alterna- tively, a contingent loan contract can also be used where its disbursement would be parametrically triggered.6 To simplify the ï¬?nal analysis, the next illustrations focus on the parametric insurance component of the broader mezzanine layer within a risk fund- ing structure. Figure 1.7. Sublayer of Parametric Insurance within the Total Structure Parametric Insurance-only Layer Source: World Bank. If the parametric insurance portion of the mezzanine layer illustrated in ï¬?gure 1.7 is split into individual country risk spreads and costs, these would be as shown in ï¬?gure 1.8, assuming each country separately and individually contracted such insurance. Figure 1.8. Individual Pricing: Non-pooled Poland Czech Republic Hungary Slovak Republic 4.5% 1.5% 1.6% 2.4% Source: World Bank. If the V-4 countries pooled their risks within a single mechanism, the spread/cost pricing would be reduced as per ï¬?gure 1.9. In this example, the partial solidarity sce- nario is used where half of the reduction in spreads would comprise the proportional 24 A World Bank Study Figure 1.9. Pooled: Collective Pricing (Solidarity and Risk-based) Poland Czech Republic Hungary Slovak Republic 2.35% 1.34% 1.37% 1.64% Source: World Bank. allocation of the pooled savings assigned to each country based on its exact risk proï¬?le, while the other half of the pooled savings are allocated in equal shares to each country. This would result in the following risk-cost spread(s) for each country: Under this arrangement all parties beneï¬?t from the multicountry diversiï¬?cation of the ï¬?scal disaster insurance coverage, with risk-sharing based half on country risk and half on the solidarity element. The option of only risk-based pricing would be repre- sented by table 1.6, column 2, where there is no solidarity element. An alternative or supplement to the risk transfer insurance portions of a ï¬?scal insur- ance structure would comprise a multicountry cat bond (ï¬?gure 1.10). Since such bond pays a risk free rate (approximated by Euribor in this report) plus the additional spread for insurance risk probabilities, its total cost appears initially higher than under the purely parametric insurance approach which does not have a base rate to contend with. But this is not the case, as the Euribor base rate component is invested and yields a return accruing to each country. Figure 1.10. Pooled Catastrophe Bond Structure Poland Czech Republic Hungary Slovak Republic Euribor Euribor Euribor Euribor + + + + 2.35% 1.34% 1.37% 1.64% Source: World Bank. As discussed previously, since cat bond proceeds are invested in risk-free or similar securities, pending an event, this cancels out most of the Euribor cost. To illustrate, the current average euro government bond spreads approximate 1 percent. The “close to risk- freeâ€? three-month euro deposit rates approximate 1.04 percent. Thus, the cost (in interest payments) on a cat bond would not be Euribor plus the insurance risk spread, but rather Euribor minus the risk free rate returns earned, plus the insurance spread. The Euribor rate less the risk free rate base would thus only amount to about 0.2 percent and potentially less depending on the funds and securities that are invested in, with the cat bond proceeds. Financial and Fiscal Instruments for Catastrophe Risk Management 25 Governments will likely be concerned about the issuance of a single bond covering all four country risks. However, there should be no concern at all since the de facto legal issuer of a cat bond is not a particular sovereign government but rather a special purpose legal entity vehicle that sells the bond to the investors (and on the other hand, acts as a captive single purpose insurance company to the governments). Therefore the issuance of a single bond for the V-4 countries falls outside and does not affect national public debt limits. Essentially each country would pay its risk-based spread to the cat bond legal entity vehicle (that would be set up by the sponsoring insur- er), and the sum of the country spread or premiums would be allocated for paying the required interest to the bond investors (in addition to the Euribor base factor, discussed above, which accrues for the most part from the bond funds invested). In this regard, the cat bond is not accounted for as public debt. Rather, the premium/spread payments into the scheme represent government budgetary obligations and an expenditure item. If a catastrophe occurred in a given country under the cat bond mechanism, the af- fected country would be paid from a portion of the invested cat bond principal that had been set aside in the trust. The size of the payment assumes a graduated compensation structure based on the severity of the hazard as per the discussion above. The remaining funds would still accrue interest for the investors (albeit on a lower principal base) but still be available for disaster payments if a qualifying catastrophe occurred in any of the other countries. Figure 1.11 illustrates the structure and flows under such a mechanism. Given the principal-protected design of the SPV under a cat bond, there is no ‘issuer’ credit risk for the repayment of principal. This of course excludes the event of a qualify- ing disaster which constitutes an insurance risk versus a credit risk. As mentioned, in addition to the insurance spreads paid per country, the differential of approximately 0.2 Figure 1.11. Collective Premium Payments into Single Cat Bond Fund Source: Prepared by the authors using World Bank data and other ï¬?nancial market information. 26 A World Bank Study percent between the Euribor rate to be paid as part of the bond coupon, and the risk free rate earned on the invested funds, would add another element to the cost. Bond prepara- tion and underwriting fees would also need to be factored in. To provide investors additional assurances, a credit guarantee can also be applied to the coupon interest which comes from public sector budgets. For example, the premium/ coupon payments could potentially carry a minor credit risk depending on the market rating of each country and its reliability in making budgetary payments. If this was of any concern, a supranational guarantee could be provided to investors to assure the timely payment of the coupon interest. Since coupons are a small fraction of total prin- cipal, any guarantee fee would be very minimal (for example, 0.5 percent of a coupon representing 1.68 percent of total principal, that is, extremely small). A coupon payment guarantee can be “rolling,â€? which means that it would cover only the next payment incrementally, when it arises. If a guarantee of this sort is invoked by investors (in an overdue payment situation), the guarantee would ï¬?rst pay the inves- tors and then deactivate (that is, it is not reinstated so any subsequent coupons would not covered by the guarantee). While this would seem less than desirable from the in- vestors’ point of view they would have already been locked into the cat bond contract. Investors would thus have also already accepted a lower coupon interest spread given the up-front existence of a guarantee. This spread would continue to beneï¬?t the government(s) until the end of the contract. The rolling feature of the guarantee, while not activated, would also make its fee cheaper as it would only cover coupon payments as they come due. Of course, a guarantee could not cover principal since by deï¬?nition under a cat bond, the principal is at risk due to natural ‘insurance’ causes, until it comes due as a ï¬?nal bullet payment, assuming the absence of a qualifying disaster event. Institutional Set-Up to Manage Pooled Structures A four-country catastrophe insurance ï¬?scal scheme with the options discussed above could be set up by creating a new legal entity owned by the V-4 governments. For ex- ample, this new legal entity could be based in a different country with a well established trust industry such as Swi erland. The legal entity would collect premiums from the participants and promise coverage based on up-the-line contracts engaged in with glob- al insurers or reinsurers, as approved by the entity’s Board which would include all the country governments. In the case of a parametric contract for participating members, the entity would ideally require initial capitalization to meet minimum technical reserve requirements. This would help to lower the up-the-line cost of insurance since the initial capital would provide the ï¬?rst cushion of coverage. That means that additional purchased reinsur- ance coverage would only be contracted at higher loss levels, at a lower proportional cost. This design feature is important since premium payments made by members in the initial years would be insufficient to make up a sufficient cushion in case of an early di- saster occurrence. Under such a parametric mechanism (excluding the use of a cat bond), a small initial capital contribution would thus be required from participating members. Such a contribution would also help lower the premium spreads beyond the beneï¬?ts obtained from pooling alone. An alternative arrangement would be to use an existing global insurer or reinsurer to provide direct coverage. This would result in a higher spread/rate because such an Financial and Fiscal Instruments for Catastrophe Risk Management 27 insurer would also need to recover a return on capital plus the pricing for the disaster coverage risk. Such an arrangement would be simpler to set up and would nevertheless provide the pooling beneï¬?ts of a four country portfolio. A multicountry catastrophe bond mechanism may not be the lowest cost mecha- nism but it would be a pioneering approach and the most innovative option. Using a multicountry catastrophe bond (whose ï¬?nal pricing would need validation from market offers) would not need any initial capital and would be priced based on the market’s demand for an adequate risk-based spread, given the flood risk characteristics. A cal- culation of all costs and fees should be ï¬?rst quantiï¬?ed as well as establishing the trig- ger probabilities. Triggers for a lower likelihood (larger upper level loss) event would demand lower proportional premiums, and also compensate mainly at high loss levels which is the main purpose of these instruments. Conclusion The V-4 governments should consider it a priority to design and implement a joint ca- tastrophe risk management scheme to ameliorate the ï¬?scal impact of major disasters. Flood based catastrophes require not only fast response and emergency relief ser- vices from governments, but also adequate ï¬?nancial and ï¬?scal planning to ensure that such events do not unduly disrupt economic and ï¬?scal planning or generate setbacks in the development path. Historical and projected flood scenarios can result in losses amounting to signiï¬?cant shares of budget revenue and GDP, not only for total country- wide losses but also for damages to public/state property and infrastructure. While existing mechanisms such as government budget funds and the EU Solidarity Fund certainly provide needed funds in the event of major floods or other disasters, their quantum as well as their speed of deployment does not match the urgent short term needs of disaster situations. In this context, governments should analyze their potential nationwide risk exposures and determine the precise levels of losses and probabilities that would require pre-established funding for expected damages to provide immediate liquidity and smooth out ï¬?scal costs. For the V-4 countries which all share major flood risks, a pooling approach to ï¬?scal funding insurance would also provide signiï¬?cant ï¬?nancial savings (estimated between 25 percent and 33 percent of otherwise individual country approaches) when se ing up such mechanisms. At a macroeconomic level, insurance payments to governments need not be based on site-by-site assessments of asset damages. Instead, using scientiï¬?c risk modeling methods, reliable correlations between flood magnitudes (measured at river catchments) and large area estimated losses can be custom-designed so as to put in place physical event-triggered (parametric) contracts. These can be veriï¬?ed and paid out with speed, and meet the needs of immediate post-disaster ï¬?nancing. Next Steps and Due Diligence Requirements for Setting up a Risk Pooling Mechanism To proceed with any of the above options, additional due diligence analysis is needed to validate and obtain a sufficient degree of precision in the underlying data parameters, in line with the requirements of risk analysts, market players and hydrological experts. This would include the following steps: 28 A World Bank Study â–  Validation of values of property and infrastructure exposures, loss exposures for such assets, and projected loss frequencies. This would involve reï¬?ning the anal- ysis of the likelihood of losses by country and by regions within each country, based on a higher resolution of the data and mapping illustrated in this paper. â–  Reï¬?nement of correlation analyses is needed with respect to flood magnitudes associated with likely losses in each country’s areas surrounding river catch- ments, and obtaining correlation coefficients of sufficient dependability to use and rely on, for parametric contracts. â–  Obtaining further precision in the probability functions (severity and frequency probabilities) will be required to determine loss distribution functions with a very high conï¬?dence level (for example, 99 percent conï¬?dence interval) to allow precise and reliable expert and market pricing. â–  Obtaining market ï¬?nal validation of cat bond pricing or parametric insurance pricing spreads is required. For cat bonds, an inventory should be done of the “all-inâ€? funding costs including credit rating costs, modeling costs, other ï¬?nan- cial services in the SPV structure, and bond underwriting costs. â–  Governments should select the public asset inventories and potential infrastruc- ture losses wishing to be protected (for example, as a subset of total assets at risk) and determining the loss exposure probability functions for these, so as to arrive at the consequent quantiï¬?cation of insurance levels desired for loss compensa- tion and affordable cost for such. â–  An institutional structure and multicountry legal conduit should be set up as an insurance entity to pool the risks and channel resources for the respective options and mechanisms for risk transfer. The rules of operation, method of veriï¬?cation of payment triggers and management of funds would need to be deï¬?ned de- pending on which option or instrument is selected. â–  A small team with expert skills should be selected manage and oversee any in- stitutional structure for the above purposes, and determine the overall ï¬?nal cost of the mechanism selected including carrying out oversight, maintenance and monitoring functions. Next Chapters Following Chapter 1, the report provides additional supporting analysis and data in the next chapters as background information. The next parts of the report cover the following: â–  Chapter 2: Covers the physical risk and loss modeling methodology used to de- termine flood probabilities and effects in different regions of each country and to arrive at modeled losses, based on a range of public and private sector assets and infrastructure, mapped to topographic/terrain characteristics and flood behavior and impacts. â–  Chapter 3: Examines, for the purpose of parametric contract design, the correla- tion analysis of flood frequencies and severities versus monetary losses, in order to establish reliable loss quanta associated with measurable physical flood mag- nitudes per country and per river catchment. â–  Chapter 4: Reports on the institutional analysis of private insurance sector pen- etration and coverage for catastrophic and flood risks in each country, as well Financial and Fiscal Instruments for Catastrophe Risk Management 29 as the availability and scope of public sector budget mechanisms for funding natural disasters. â–  Chapter 5: Develops a multicountry global econometric analysis based on world- wide data, showing ï¬?scal sustainability effects from natural disasters and how different country pre-conditions (such as depth of ï¬?nancial, bond, insurance markets) affect the GDP path and the effectiveness and nature of the ï¬?scal re- sponse in the recovery process. Notes 1. If we make the analogy of investment returns instead of insurance losses, this is akin to having a pension portfolio of 3 bonds versus one of 30 bonds. A 30-bond portfolio would have more even and stable returns while a 3-bond portfolio may have a potential upside but also a large potential downside (loss). If a pension fund manager ï¬?rm had to guarantee at least a zero rate of return on the investments, it would need a larger capital cushion for the 3-bond portfolio, as the chances of it yielding below-zero returns would be much higher. 2. Currently government budget allocations for disasters are rather modest, with the Czech Repub- lic allocating EUR 4 million annually (but relying on ex post bond issuances for disasters), Poland EUR 170 million, and Hungary EUR 15 million. 3. Very few cat bonds experienced credit pricing problems during the crisis as the use of the Libor base rate implied underlying supporting securities with a degree of liquidity and credit risk. For cat bonds, floating rates are frequently swapped into ï¬?xed rates via ‘total return swaps’ and some swap counterparties (for example, Lehman) had credit risks because of this. These were for a very small minority of issuances, however, and now these latent problems are being corrected via the use of cat bonds with a spread over risk free Treasuries instead of Libor, and lesser use of swaps into ï¬?xed rates. 4. Commercial banks typically charge a commitment fee for undisbursed balances so there is an additional cost to consider. Multilaterals such as the World Bank used to charge commitment fees but the current rate on commitment fees for IBRD loans is zero. 5. The ï¬?gures shown are only estimates based on available market and risk exposure information but would need to be validated in more detail if an actual transaction were to be engaged. 6. One possibility would be the use of a multilateral or IBRD loan where currently, a maturity up to 12 years would offer a rate of Euribor or dollar Libor plus a 0.6 percent spread. For dollar Libor, the effective all-in interest rate would be approximately 1.05 percent, and for Euribor higher at around 1.85 percent (using data as of Q1 2011). A loan could be a contingent facility (currently with no commitment charge while pending disbursement) to complement insurance mechanisms. It could also be alternatively used up-front to ï¬?nance premium payments for a parametric insurance policy, or pay for bond coupon interest. CHAPTER 2 Catastrophic Loss Exposure Analysis: Flood Exposures and Probabilistic Loss Estimations in Poland, the Czech Republic, Hungary, and the Slovak Republic Introduction T his chapter analyzes the possible property losses caused by extreme flood events in the four countries of Eastern Central Europe that include Poland, the Czech Re- public, Hungary, and the Slovak Republic, often commonly referred to as the V-4 or Visegrad Group countries. In the nineties and at the beginning of this century, the sub-region was hit with two unusually intensive floods that changed understanding of the risk of flood in the coun- tries of the sub-region. Both floods started with extreme precipitation in areas of very high API301. The change occurred together with major changes in political and social arrangements in the countries. Even after the serious floods in the early summer 1997 in Poland and the Czech Republic, the risk of floods in the region had not been generally con- sidered particularly serious. Decision makers as well as the population at large continued to believe that the region would not suffer from serious floods and that the safe period of the twentieth century would continue. Some risk managers in the insurance industry introduced tools based on geographic information systems (GIS) and sought support of GIS providers. The situation for research in this ï¬?eld improved after the August 2002 flood. The forecasted flooded areas according to the developed models nearly coincided with the experience of the 2002 flood. As a result, the perception of flood risk increased. This al- lowed major support from the insurance industry for the research teams of Intermap Technologies and thus the study represented in this chapter is based on a yearlong experi- ence of the research team2 of Intermap Technologies (located in Prague), providing support for the insurance industry in natural hazard modeling, GIS and software development for reinsurance, underwriting, risk management, loss adjustment, product development, and other departments of the insurance companies, in close cooperation with partner companies and development teams, such as insurers and reinsurers, Nat Cat modelers and universities. The main emphasis of this work was thus on the estimation of possible losses to publicly owned property caused by river floods. River floods are generally understood as mainly regional floods, or partially local river floods caused by heavy rainfall events. In contrast, pluvial floods are understood as surface water flow or “pondingâ€? outside 31 32 A World Bank Study the floodplains caused by heavy rainfall, as well as coastal surges, and are not consid- ered in the study. Public property in these countries usually includes not only public administration, social infrastructure (such as education, culture, and health care) and technical infrastructure (such as transportation and communication networks) but also enterprises that did not fully undergo the process of privatization during the period of economic transformation of these countries. Based on the experience with the major regional floods in the last 15 years, the public sphere was also required to fund a part of the flood losses in private property, especially housing and infrastructure. While the situation has improved now with be er insur- ance penetration, the root causes included relatively low prior penetration of household insurance as well as low limits of claims amount for insured properties due to heavy competition in the insurance market and a poor level of ï¬?nancial awareness among the clients of insurance companies. Therefore, loss estimations on private assets were also a part of the analysis. The study comprising this chapter is divided into four parts and an appendix (avail- able online) with detailed data.3 The ï¬?rst three parts contain descriptions of the meth- ods used in the analysis while the fourth includes the results. Basic terms, content and limitations of the study are deï¬?ned in Part 1 while Part 2 lists the sources for the input data. The methodology itself is described in Part 3. Part 4 is divided into two parts, one describing the results of regional and sector distribution of assets, and the other contain- ing the results of flood loss estimations. The online appendix includes all the detailed information that was not included in the body of the other parts. The appendix is sub- divided into three constituent units, the ï¬?rst one includes detailed data mostly on the inputs, the second one on the outputs, and the third one is mapping loss estimations in a very deep detail. For possible loss estimations, a scenario method of flood exposure modeling was used. This method was originally developed for the insurance market in close coop- eration with some leading Czech insurers in order to estimate the overall risk of their portfolios and check the conditions given to them by the reinsurance companies. Several territorial scenarios were selected for each country including the territorial extent of the recent major flood events, various catchment areas of the major rivers as well as complex geographic areas like such as geomorphologic or historical-cultural units. The results for different scenarios may well represent the eventual range of losses, however, a single typical or mean value of losses had to be found for each country, and given the fact that the possible loss of a compound territory was not an additive function of partial possible losses of its component territories, a typical value for the whole V-4 Group had to be found. Therefore, an extrapolation of a stochastic method of flood ex- posure was done using the information on the regional structure of property, its location in the flood zones and possible losses under different scenarios. The stochastic method that is one of the main inputs in that extrapolation was built up in close cooperation with the Nat Cat Team of the Swiss Re reinsurance company. At the theoretical level, the flood loss modeling is explained in section 3.1 in general, in paragraph 3.9 for scenario-based modeling, and in section 3.10 for the stochastic modeling and its extrapolation. The ter- ritorial scenarios themselves are described in section 3.6 and listed in appendix section 5.1.9. (The Appendix referred to in this chapter is available online at: h p://documents. worldbank.org/curated/en/2012/01/16242871.) Financial and Fiscal Instruments for Catastrophe Risk Management 33 As the main output of the study, loss functions have been constructed for the V-4 Group, the individual countries as well as the distinct scenarios, namely the Loss Ex- ceedance Curves and the Survival Functions. The Loss Exceedance Curve is a functional dependence of the possible loss on the return period of the loss while the Survival Func- tion represents the per cent probability of exceeding a certain loss within a period of one year. In addition to that, possible regional distributions of the losses were estimated and projected to maps of administrative divisions in the countries. The possible loss estima- tions can be found in section 4.2.1 and, in more detail, in the appendix section 5.2.1. Their possible territorial distributions of losses are mentioned in appendix 5.2.4 while the possible distributions into institutional sectors as well as industry branches and asset categories are included in appendix section 5.2.5. appendix section 5.2.3 reports on loss purpose classiï¬?cation of losses for each scenario and appendix section 5.3 contains deep details of the loss estimation for each scenario. To be er understand both territorial and sector structure of the property in the countries that are subject of the study, the outputs also include a split of the assets ac- cording to the sector of economic activity (that is, Industry Branches), physical constitu- tion (that is, Asset Category), and ownership structure (that is, Institutional Sector) as well as their territorial split into ï¬?rst (that is, provincial) or second (that is, district) levels of the state administration. On the methodological level, this topic is covered by sections 3.2 by sector, and 3.4 for the regional distribution, respectively, with listing ancillary data for regional distribution in section 4.1.2 and appendix section 5.1.13. The results are presented in the appendix sections 5.1.5 by sector and 5.1.12 for the regional split. During the course of the work, the team was looking for the best available data on the structure of the assets in the respective countries as well as their regional distribu- tion. Initially, governmental institutions such as ministries of ï¬?nance, education, health, interior, and so forth. were contacted. However, it turned out that only incomplete lists of governmental property were available at those governmental bodies with even less complete information on the values of those properties. Furthermore, all of the V-4 coun- tries went through a signiï¬?cant deconcentration and decentralization process whereby substantial parts of public administration, social infrastructure and public enterprise became a property of regional or local governments. Those properties are not listed by central governments anymore and to obtain a full list of public assets would have meant collecting the lists from up to 80 provincial and approximately 15,000 local administra- tions in addition to the central governments in the V-4 countries. Recognizing this, an alternative way of collecting information was chosen. The four national statistical offices provided lists of assets aggregated nationwide and for the largest statistical territorial units such as the NUTS-2 or NUTS-3 of the pan-EU classiï¬?cation. Even if using the same methodology of a three-dimensional split of the assets, there were slight differences in the availability of those data between individual countries. Therefore, additional inter- polation was required for the countries with data gaps in both nationwide as well as territorial classiï¬?cation of the assets. Even less complete were the records of the losses caused by historical flood events in the last 15 years that were needed for calibration and veriï¬?cation of the model. Estimates were used from hydrological research institutes such as the VUV in the Czech Republic or from national statistical offices as well as from academic sphere. The historical flood loss data are listed in appendix section 5.1.6. 34 A World Bank Study Two very important inputs to the possible flood loss model have been the flood haz- ard zones and the vulnerability functions. The flood hazard zones have been delineated using the Geomorphologic Regression approach that was originally developed by the Nat Cat Team of the Swiss Re reinsurance company and further developed by Inter- map’s developers. The vulnerability functions are based on the historical loss records of the insurance companies in the Czech Republic that were calibrated and veriï¬?ed with the loss records during some of the recent extreme flood events in the V-4 region. Thanks to the cultural proximity and similar historical development of the V-4 countries the vulnerability functions are well transferable to the other three countries of the group. The methodology of modeling of the flood hazard zones is described in section 3.7 and the results are shown in appendix section 5.1.8. Section 3.8 describes the construction of the vulnerability functions. 1. Deï¬?nitions 1.1. Territorial Scope The analysis explores the possible losses caused by floods in the Czech Republic, Po- land, Hungary, and the Slovak Republic. The respective territories of these V-4 countries are administratively divided into three principal hierarchical levels, that is, provinces, districts, and municipalities.4 The provinces correspond to NUTS-3 level5 in the Nomen- clature of Units for Territorial Statistics (NUTS)6 that is used for comparisons within the EU, while the districts correspond to LAU-17 and municipalities to LAU-28 level. For the purposes of the analysis, the districts are used as the smallest territorial unit. 1.2. Thematic Scope The main aim of this analysis is to map possible extreme losses on both public services9 and public commercial enterprises.10 Private properties are mapped as well. Based on the experience from recent extreme floods, the governments have been providing sub- stantial amounts of resources covering the losses on private property, especially those for residential buildings and their contents. Due to efficient application of modeling, the assets have been divided into few class- es using a three-dimensional classiï¬?cation used by national accounting in the respective countries that have been standardized to a certain level for all the EU member states. The three-dimensional classiï¬?cation contains the following dimensions: Industry Branch,11 Asset Category,12 and Institutional Sector.13 1.3. Value Deï¬?nitions 1.3.1. REPRODUCTION (REPLACEMENT) VERSUS HISTORICAL PRICES The Reproduction price stands for the price, for which the property would be purchased in the current year, that is, the current market price of the property. The Historical price represents the value of the property expressed either in the prices of a ï¬?xed historical year (for example, 2000) or in those of the year of purchase. When the analysis estimates real losses, the reproduction prices of the property are applied. 1.3.2. TIME DIMENSION The property values as well as estimated loss values are related to year-end 2007 values.14 Financial and Fiscal Instruments for Catastrophe Risk Management 35 1.3.3. GROSS VERSUS NET ASSET VALUE The Gross value represents the price of the property without depreciation, that is, the value of the new property when purchasing. The Net value is the depreciated gross value (with depreciation amortization subtracted). For the loss modeling as well as property structure classiï¬?cation, the net value (in reproduction prices) is used. 1.3.4. TRANSNATIONAL ESTIMATES For the transnational cross-country comparison, the loss values were denominated in EUR. Pertinent conversions used either Exchange Rates or Purchasing Power Parity (PPP) measures. As the structure of the property considered in the model may signiï¬?cantly differ from the basket of goods used for PPP estimates, the Exchange Rate is used for transnational comparisons rather than PPP. The average exchange rates to convert from national currencies to EUR were used according to the European Central Bank (ECB) data for the period between July 1, 2008 and June 30, 2009.15 1.4. Flood Deï¬?nition The analysis focuses on the effects of the extreme events of river/fluvial flood. The river network considered streams with catchments greater than 20 square kilometers in the headwater areas. Pluvial/flash floods were considered only to the extent that they con- tributed to the losses caused by the river network (that is, when collecting the water from the thunderstorm or collapse of a human structure such as a dam). Losses were not considered when caused by surface water in areas with no permanent rivers/streams. Furthermore, tidal/coastal floods/storm surge were not considered. 2. Input Data and Providers 2.1. Property Value Data The national statistical authorities are the main sources of information on the property values classiï¬?ed in a three-dimensional split as deï¬?ned in section 3.2. These asset classi- ï¬?cations are published for individual countries on a yearly basis. Territorial Distribution of the assets is available only to a limited extent. The data sources of property value and structure in the particular countries are described in sections 3.2.10 and 3.2.11. To obtain information on household property, which is not registered by the nation- al statistical authorities, data from the insurance industry were collected. The procedure for household property estimation is explained in section 3.2.4. The particular value dis- tributions are listed in appendix section 5.1.5 for national level and in appendix section 5.1.12 for the regional level. 2.2. Property Location Data The national statistical offices provide ï¬?xed asset value or formation data regionalized to the NUTS-2 or NUTS-3 level. Data split into lower level administrative units, that is, districts (LAU-1 level) are not available and have been modeled. The property location data sources for the particular countries as well as the regionalization procedure are de- scribed in sections 3.4.1, and 3.4.2. The distribution of the property into the hazard zones is described in section 3.5. Individual property location data from public registers were not used due to un- availability for most countries including property categories. 36 A World Bank Study 2.3. Population and Income Data (Regional Distribution) Both the population distribution and the income distribution data down to the second level administrative units, that is, districts (LAU-1 level) were provided by the national statistical offices. The data population and income data sources as well as their applica- tion are described in section 3.4.2 while the data are listed in appendix section 5.1.13. 2.4. Flood Loss Data Historical flood loss data as well as their data sources are listed in appendix section 5.1.6. Application of the flood loss data for the vulnerability calculation is described in section 2.9. 2.5. Land Cover Data Coordination of Information on the Environment (CORINE) Land Cover data with a 100 meter resolution grid have been used.16 For GIS analyses, the raster data have been vec- torized. Application of the land cover data is described in section 2.5. For the complete list of CORINE categories, see appendix section 5.1.11. 2.6. Hydrological Network Data The river network used for flood hazard zone modeling corresponds to national topo- graphical maps in scales of around 1:25,000 to 1:50,000: â–  Czech Republic. The hydrology network of a 1:25 000 Digital Model of the Terri- tory (Digitální model území 1:25 000) vector data has been used (see [9]). â–  The Slovak Republic. The hydrology network of the Digital Vector Map of the Slovak Republic with 1:50,000 of Mapa Slovakia Plus17 has been used. â–  Hungary. The hydrology network of the Digital Vector Map of Hungary with 1:10,000 of HISZI-Map18 has been used. â–  Poland. Due to lack of data, the CCM River and Catchment Database, version 2.1 (CCM2, see [1]) was used and which corresponds to the SRTM elevation model19 with a 100 meter grid and validated with Landsat TM panchromatic satellite data.20 2.7. Digital Elevation Model The best available digital terrain models (DTM) were used in the particular countries. The flood protection that is recognizable in the terrain model was considered directly in the model. â–  Czech Republic. In the Czech Republic, the DTM is based on contour lines and elevation spots of the 1:25,000 scale national topographic dataset.21 â–  The Slovak Republic. In the Slovak Republic, the DTM is based on contour lines and elevation spots of the 1:50,000 scale national dataset.22 â–  Hungary. In Hungary, the MONA Pro Europe23 dataset derived from topograph- ic maps of 1:50,000 scale maps (DMA series) has been used in combination with SRTM (Shu le Radar Topography Mission) dataset.24 â–  Poland. In Poland, the MONA Pro Europe dataset derived from topographic maps of 1:50,000 scale maps (DMA series) has been used in combination with SRTM (Shu le Radar Topography Mission) dataset. Financial and Fiscal Instruments for Catastrophe Risk Management 37 2.8. Flood Hazard Data Flood hazard zones based on the return period were delineated using an in-house built flood modeling approach that is a modiï¬?cation of the Geomorphologic Regression (GMR)25 method of the Swiss Re26 re-insurer (ï¬?gure 2.1). The rivers and other streams were modeled if their catchment exceeded 20 square kilometers.27 A flood zoning revision was performed using 1:50,000 and 1:25,000 scale topograph- ic maps in order to consider flood protection that is not recorded in the terrain model: â–  Czech Republic. The flood hazard zones were modeled in 2003–05 for usage by the Czech insurance market. After modeling by the Swiss Re using the GMR method, a revision based on topographic maps was done by Multimedia Com- puter.28 â–  The Slovak Republic. The flood hazard zones were modeled in 2005 for usage by the Slovak insurance market. After modeling by the Swiss Re using the GMR meth- od, a revision based on topographic maps was done by Multimedia Computer. â–  Hungary. The flood hazard zones were modeled in 2009 for the purpose of this analysis. The flood hazard zones modeled by the VITUKI29 were used for calibra- tion30 of the model. No revision was done. â–  Poland. The flood hazard zones were modeled in 2009 for the purpose of this analysis. No revision was done. 2.9. Flood Vulnerability Data The source for flood vulnerability data are Intermap’s previous in-house analyses31 based on the flood loss data of various Czech insurers and historical flood loss data, which are listed in appendix section 5.1.6. 3. Methodology 3.1. Flood Exposure Determination The key input to the model is the three-dimensional split of the property values as de- scribed in section 3.2, plus the assets re-classiï¬?ed into simpliï¬?ed classes as deï¬?ned in sections 3.2.2, 3.2.4, and 3.2.6. The spatial distribution of the three-dimensional asset values into the provinces32 is described in section 3.4.1 while the reï¬?nement of the spa- tial distribution to the district level is explained in section 3.4.2. Property values are re- distributed into the flood hazard zones within each district as described in section 3.5. As discussed in section 3.8, vulnerability functions are calibrated according to his- torical loss data separately for each property class shown in section 3.4.3. The above provides basis for construction of a virtual portfolio of districts vs. hazard zones vs. property types. Based on the information on property distribution throughout districts, and the hazard zones and geographically deï¬?ned scenarios (as mentioned in section 3.6), the potential losses are estimated with proper vulnerability functions. As mentioned in section 3.11, the Loss Exceedance Probabilities are predicted for different scenarios whereas potential losses for distinct return periods of the flood loss are represented on the resulting dependence trend of the loss exceedance curve. More- over, the Survival Function is also constructed—this function represents the percent probability that a certain loss will be exceeded. The above two functions represent the main outcomes of the analysis. 38 A World Bank Study Figure 2.1. Overall Exposure Modeling Workflow Re-classification Aggregate Property Property Data Property 2-D Structures Property 3-D Structure Region/Industry Regionalization Weights Property 3-D Structure in Regions District Weights Property 3-D Structure in Districts Evaluation Hazard Flood Hazard Zones Property Structure in Districts and Hazard Zones Scenarios Probability Exposure Calculation Affected Property Affected Property Vulnerability Losses Losses (Scenarios) (Stochastic) Classification Loss Results Source: Intermap Technologies. Financial and Fiscal Instruments for Catastrophe Risk Management 39 In order to evaluate the possible loss of the individual countries as well as the V-4 Group, the loss exceedance curves are calculated for each country and the V-4 Group by using extrapolation of the stochastic method, as described in the section 3.10. The de- tailed classiï¬?cation of the losses for each scenario for a certain return period in the three- dimensional split and spatial distribution of the losses to the province level, is modeled as described in section 3.11.4. 3.2. Property Value and Classiï¬?cation 3.2.1. INDUSTRY BRANCH The ï¬?rst dimension is the classiï¬?cation by Industrial Branches based on the “Classiï¬?- cation of Economic Activities in the European Communityâ€? (NACE), revision 1.1. The NACE classiï¬?cation, which is used by the EU member states, is a modiï¬?ed version of the “International Standard Industrial Classiï¬?cation of all Economic Activitiesâ€? (ISIC) that is supported by the United Nations (UN).33 The NACE categories are deï¬?ned in a hier- archical structure of 4 levels.34 The main categories of the highest level—Sections—as grouped in this work are listed in table 2.1. The property structure in 1D split by Indus- try Branch is listed in appendix section 5.1.5. Table 2.1. Industry Branch Classes as Used in the Study Class Industry A&B Agriculture, hunting, forestry and ï¬?shinga C Mining and quarrying D Manufacturing E Electricity, gas and water supply F Construction G Wholesale and retail trade; repair H Hotels and restaurant I Transportation, storage and communication J Financial intermediation K Real estate, renting, research and business activities L Public administration and defense; compulsory social security M Education N Health and social work Other community, social and personal services (recreation, culture and sport; membership organizations; O sewage and sanitation) Source: Intermap Technologies. a. Assets 12 (inventories) are not considered. 3.2.2. RECLASSIFICATION OF INDUSTRY BRANCHES To compare asset value data with the regional distribution and with loss data, the In- dustry Branches were aggregated into three super-classes: “industrial,â€? “residential,â€? and “networkâ€? property types. Spatial distribution of the Industrial Branches as deï¬?ned in section 3.2.1 is available for the province level only.35 In the model, the simpliï¬?ed prop- 40 A World Bank Study Table 2.2. Industry Branch Classes as Reclassiï¬?ed into Three Property Types Class Industry “Industrialâ€? property type C Manufacturing D Mining and quarrying Fa Constructiona a. 50% of the total assets are included (the rest included in “Residentialâ€? type). “Networkâ€? property type Ia Transportation, storage and communicationa a. Only assets category AN.1112 (Other buildings and structures) is included (the remaining asset categories are included in the “Residentialâ€? type). “Residentialâ€? property type Aa Agriculturea Ba Hunting, forestry and ï¬?shinga E Electricity, gas and water supply Fb Constructionb G Trade and repair H Hotels and restaurants Ic Transportation, storage and communicationc J Financial intermediation K Real estate, renting and business activities L Public administration and defense; compulsory social security M Education N Health and social work O Other services (recreation, culture and sport; membership organizations; sewage and sanitation) a. Assets 12 (inventories) are not considered (see also paragraph 0). b. 50% of the total assets are included (the rest included in “Industrialâ€? type). c. Except for the assets category AN.1112 (Other buildings and structures), which is included in the “Networkâ€? property type. Source: Intermap Technologies. erty types as deï¬?ned in table 2.2 are used for weighting property values during regional- ization, to districts,36 for value distribution to flood hazard zones,37 and for vulnerability calculations.38 3.2.3. ASSET CATEGORY The second dimension is the classiï¬?cation by Asset Categories based on the “European System of Accountsâ€? revision of 1995 (ESA’95) that is an adaptation of the “United Na- tions System of National Accountsâ€? (SNA) used for the purposes of the EU. This clas- siï¬?cation represents physical property structure. For more details about ESA see [5]. For the Asset Category classiï¬?cation, see table 2.3. For the Asset Categories not considered in the study, see table 2.4. The property structure in 1-D split by Asset Category is listed in appendix section 5.1.5. Financial and Fiscal Instruments for Catastrophe Risk Management 41 Table 2.3. Classiï¬?cation by Asset Categories Considered in the Study Code Asset Category AN Non-ï¬?nancial assets AN.1 Produced assets AN.11 Fixed assets AN.111 Tangible ï¬?xed assets AN.1111 Dwellings AN.1112 Other buildings and structures AN.11121 Nonresidential buildings AN.11122 Other structures AN.1113 Machinery and equipment AN.11131 Transport equipment AN.11132 Other machinery and equipment AN.12 Inventories AN.121 Materials and supplies AN.123 Finished goods AN.124 Goods for resale Source: Intermap Technologies. Note: The categories used for the property and loss classiï¬?cation in the study are printed in bold. Table 2.4. Classiï¬?cation by Asset Categories Not Considered in the Study Code Asset Category AN.1114 Cultivated assets (such as livestock, vineyards, orchards, and so forth.) AN.112 Intangible ï¬?xed assets (such as artistic originals, computer software, and so forth.) AN.122 Work in progress AN.13 Valuables (such as precious metals, antiques, and so forth.) AN.2 Non-produced assets AN.21 Tangible non-produced assets (such as land, sub-soil, non-cultivated biological, water, and so forth.) AN.22 Intangible non-produced assets (such as patents, goodwill, and so forth.) AF Financial assets Source: Intermap Technologies. 3.2.4. RECLASSIFICATION OF ASSET CATEGORIES In this analysis, the asset categories as deï¬?ned in section 3.2.3, are aggregated into two super-classes, that is, “buildingâ€? and “contentâ€? (Asset Category). In addition to the classes listed in table 2.3, the Household Equipment class, which is not registered by the national statistical authorities, is deï¬?ned. It is included in the content super-class. The value of Household Equipment is calculated by an expert estimate based on (1) insurance data, (2) population data, and (3) GDP per capita data. The algorithm of the Household Equip- ment value calculation is described in appendix section 5.1.4. 42 A World Bank Study Table 2.5. Structure of Asset Category Classes (“Buildingâ€? and “Contentâ€?) and Their Deï¬?nitions as Used in the Study Building AN.1111 Dwellings AN.1112 Other buildings and structures Content AN.1113 Machinery and equipment AN.12 Inventories — Household equipment Source: Intermap Technologies. Note: For comparison see table 2.3. 3.2.5. INSTITUTIONAL SECTOR The third dimension is the classiï¬?cation by Institutional Sectors also based on the ESA’95.39 The ESA sectors/units included in the “publicâ€? class are listed in table 2.6. Table 2.6. ESA Sectors/Units Included in the Public Class Sector Subsector Unit S.11 Non-ï¬?nancial S.11001 Public Non-ï¬?nancial corporations corporations S.121 Central bank S.122 Other monetary ï¬?nancial institutions S.12201 Public Other monetary ï¬?nancial institutions S.123 Other ï¬?nancial intermediaries S.12301 Public Other ï¬?nancial S.12 Financial corporationsa intermediaries S.124 Financial auxiliaries S.12401 Public Financial auxiliaries S.125 Insurance corporations and pension S.12501 Public Insurance corporations and funds pension funds S.13 General government All subsectors S.15 Non-proï¬?t institutions serving households S.2 Rest of the world Source: Intermap Technologies. a. Although the sector S.12 Financial corporations contains most of the subsectors, most of the property value is included in the sector S.11 Non-ï¬?nancial corporations. 3.2.6. RECLASSIFICATION OF INSTITUTIONAL SECTORS The third dimension is the classiï¬?cation by Institutional Sectors also based on the ESA’95. The ESA sectors/units are re-classiï¬?ed into the following 3 sectors: (1) private, (2) public and (3) households. The reclassiï¬?cation is listed in table 2.7. The property structure in 1-D split by Institutional Sector is listed in appendix section 5.1.5. Financial and Fiscal Instruments for Catastrophe Risk Management 43 Table 2.7. Reclassiï¬?cation of the ESA Sectors/Units Original sector classiï¬?cation Sector Subsector Unit Reclassiï¬?cation S.11 Non-ï¬?nancial S.11001 Public Non-ï¬?nancial corporations Public corporations all other units Private S.121 Central bank Public S.122 Other S.12201 Public Other monetary ï¬?nancial institutions Public monetary ï¬?nancial all other units Private institutions S.123 Other ï¬?nancial S.12301 Public Other ï¬?nancial intermediaries Public S.12 Financial intermediaries all other units Private corporationsa S.124 Financial S.12401 Public Financial auxiliaries Public auxiliaries all other units Private S.125 Insurance S.12501 Public Insurance corporations and pension Public corporations and funds pension funds all other units Private S.13 General all subsectors all units Public government S.14 Households all subsectors all units Households S.15 Non-proï¬?t Public institutions serving households S.2 Rest of the world all subsectors Private Source: Intermap Technologies. a. Although the sector S.12 Financial corporations contains most of the subsectors, most of the property value is included in the sector S.11 Non-ï¬?nancial corporations. 3.2.7. ELIMINATION OF SOME PROPERTY CATEGORIES CONSIDERED IN THE STUDY In the analysis, all asset categories listed in table 2.3 (section 3.2.4) are considered, except for the inventories in the industry branches A (Agriculture) and B (Hunting, forestry and ï¬?shing).40 Moreover, the following kinds of losses are not considered: â–  Ecological losses â–  Losses on subways, tunnels, and underground collectors. 3.2.8. PROPERTY RECLASSIFICATION SUMMARY The summary of the property reclassiï¬?cation is listed in table 2.8. Table 2.8. Property Reclassiï¬?cation Summary Dimension Chapter Table Split Summary Residential C, D, (F) Industry Branch 3.2.2 3.2 Industrial A, B, E, (F), G, H, I, J, K, L, M, N, O Building AN.1111 & AN.1112 Asset Category 3.2.4 3.5 Content AN.1113 & AN.12 & House-Equip. Public S.11001, S.121, S.12X01, S.13, S.15 Institutional Sector 3.2.6 3.7 Households S.14 Private Others Source: Intermap Technologies. 44 A World Bank Study 3.2.9. SECTOR/PURPOSE RECLASSIFICATION OF THE PROPERTY For identiï¬?cation of losses not only on public property but also on public services in particular (that is, excluding public enterprises), a supplementary reclassiï¬?cation of the property to infrastructure/enterprise classes was used. The infrastructure class includes the industry branches which can be considered as social or technical infrastructure while the enterprise class includes other activities. For the property and loss classiï¬?cation, the cate- gories infrastructure/enterprise were used in a combination with the institutional sector classiï¬?cation.41 The sector/purpose reclassiï¬?cation was deï¬?ned as described in table 2.9. Table 2.9. Sector/Purpose Reclassiï¬?cation of the Property Institutional Sector Purpose Public Non-Public Infrastructure Public42 (property in the industry Infrastructure Non-public43 (property in the Infrastructure branches E, I, L, M, N, and O in the “publicâ€? industry branches E, L, M, N, and O in the institutional sector) “privateâ€? or “householdsâ€? institutional sectors) Enterprise Public44 (property in the industry Enterprise Non-public45 (property in the industry Enterprise branches A, B, C, D, F, G, H, J and K in the branches A, B, C, D, F, G, H, I, J and K in the “publicâ€? institutional sector) “privateâ€? or “householdsâ€? institutional sectors) Source: Intermap Technologies. The property structure in the mentioned supplementary split is listed in appendix section 5.1.5 and the loss split in this structure is listed in appendix sections 5.2.3, 5.2.5, and 5.3. Note that the industry branch I (Transportation, storage and communication) is in- cluded in the “Infrastructure Publicâ€? for public assets but included in “Enterprise Non- publicâ€? for the private assets. The reason is that publicly owned property in this branch mostly represents infrastructure and public transportation, while privately owned ones represent mostly enterprise related property, such as storage, cargo transportation, and so forth. 3.2.10. DATA SOURCES ON PROPERTY VALUE The following data sources for the property value and one-dimensional46 classiï¬?cation were used: â–  The Czech Republic • The ï¬?xed asset47 as well as inventories48 values and structures data were pro- vided by the Czech Statistical Office â–  The Slovak Republic • The ï¬?xed asset data49 were provided by the Statistical Office of the Slovak Re- public. However, as the total value of the public sector property provided by the Statistical Office of the Slovak Republic relates only to industry branches L-N, the proportion of the public property in the remaining industry branches was calculated based on the structure of the Czech Republic. Therefore, the proportion of the public property value considered for the model is higher than the one reported by the Slovak Republic’s Statistical Office. • As inventories data (asset category AN.12) are not available for the Slovak Re- public, the value and structure of the inventories was calculated as a propor- Financial and Fiscal Instruments for Catastrophe Risk Management 45 tion of the asset value within each industry branch according to classiï¬?cation of the inventories to industry branches in the Czech Republic. â–  Hungary • The ï¬?xed assets as well as inventories values and structure data50 were pro- vided by the Hungarian Central Statistical Office. â–  Poland • The ï¬?xed asset values and structure data provided by the Central Statistical Office of Poland,51 were used, with the following two adjustments: − As the sector classiï¬?cation used by the Central Statistical Office of Poland contains only public and private sectors (that is, the value of the households sector is included in the private sector), the household property value for each industry branch in Poland was estimated as a percentage of the private52 sec- tor according to proportion of the household property value within the com- bined household + private sectors property value in the Czech Republic. − As the ï¬?xed asset values reported by the Central Statistical Office of Poland compared to the ï¬?xed asset values reported by any other of V-4 national statistical authorities differed substantially from the relation to GDP of the remaining countries,53 the total ï¬?xed asset value considered for modeling purposes was adjusted in order to provide results comparable with the re- maining V-4 countries. In particular, the total property value reported by the Central Statistical Office of Poland was adjusted by the relative GDP index of the Czech Republic vs. Poland. • As inventories data are not available for Poland, the value and structure of the inventories was calculated as a proportion of the asset value within each indus- try according to classiï¬?cation of the inventories into the industry branches in the Czech Republic. 3.2.11. ACHIEVING THE THREE-DIMENSIONAL PROPERTY CLASSIFICATION For purposes of loss modeling, the property is classiï¬?ed in the 3-dimensional54 structure. The 3-D property structure was generated in the following manner: â–  The Czech Republic • The 3-D property structure of the ï¬?xed assets was provided by the Czech Re- public’s Statistical Office,55 • Inventories (asset category AN.12) were distributed into the 3-D structure based on the 1-dimensional classiï¬?cation of the inventories to industry branches and the 2-dimensional classiï¬?cation of industry branches v. institutional sectors.56 â–  The Slovak Republic • The 3-D property structure of the ï¬?xed assets was generated by using 2-D property structure matrices of industry branch vs. asset category57 provided by the Statistical Office of the Slovak Republic and 2-D property structure of the institutional sector vs. industry branch used for the Czech Republic.58 • Inventories were distributed into the 3-D structure based on the 1-dimensional classiï¬?cation of the inventories to industry branches calculated according to section 3.2.10 b). 46 A World Bank Study â–  Hungary • The 3-D property structure of the ï¬?xed assets was generated by using 2-D property structure matrices of industry branch vs. asset category59 and institu- tional sector vs. industry branch60 provided by the Hungarian Central Statisti- cal Office. • The structure of the inventories (asset category AN.12) was generated from the 1-dimensional classiï¬?cation of the inventories into industry branches61 and the 2-D classiï¬?cation of the institutional sector vs. industry branch.59 â–  Poland • The 3-D property structure of the ï¬?xed assets was generated by using the 2-D property structure matrices of industry branch vs. asset category and institu- tional sector vs. industry branch62 provided by the Central Statistical Office of Poland. • Inventories were distributed into the 3-D structure based on 1-dimensional classiï¬?cation of the inventories to industry branches calculated according to section 3.2.10 d) and the 2-D classiï¬?cation of institutional sector vs. industry branch.61 3.3. Time Dimension The total property values as well as estimated loss values were related to year-end 2007. As the property structures provided by the national statistical authorities were related to year-end 2006,63 the property values used in the model were obtained by multiplying the 2006 data by indexes representing the ï¬?xed asset value growth between the years 2006 and 2007 in the particular country. The ï¬?xed asset values in THE years 2006 and 2007 and the indexes calculated as well as the data sources are in Appendix section 5.1.3. 3.4. Regional Distribution of Assets The regional distribution of the property value data, separately for each industry branch, is available at the province/region level (that is, NUTS-3/NUTS-2) only. To estimate the territorial distribution of the property at lower administrative levels, namely in indi- vidual districts (that is, LAU-1, formerly marked as NUTS-4), a regionalization approach has been used. The administrative divisions of the countries are in appendix section 5.1.1. 3.4.1. PROPERTY DISTRIBUTION INTO PROVINCES The weights for the regional distributions of the assets into provinces (that is, NUTS-364 or NUTS-265 level) are obtained either from the Regional Gross Fixed Asset Balance Data for each industry provided by the national statistical institutes (Poland66 and the Czech Republic67) or the Regional Gross Fixed Asset Formation Data (Hungary68 and the Slovak Republic,69 for which the Regional Gross Fixed Asset Balance Data is not available). The property distribution into provinces is in appendix section 5.1.12. 3.4.2. PROPERTY DISTRIBUTION INTO DISTRICTS The distribution of the property into the districts was processed separately for (1) resi- dential and industrial and (2) “networkâ€? property types.70 1. Residential and Industrial Property Types The property distribution from provinces (NUTS-2/3) into districts (LAU-1) level is not provided71 by the national statistical offices. Therefore, the distribution has been esti- Financial and Fiscal Instruments for Catastrophe Risk Management 47 mated as a proportion of the property value in the superior province (NUTS-3)72 or re- gion (NUTS-2).73 The weights were used as follows: â–  For the Czech Republic, the historical data of ï¬?xed asset formation74 (aggregated for all industries) were used for distribution of the property into the LAU-1 units within the NUTS-3 unit. â–  For Poland75 and the Slovak Republic,76 total income within in the LAU-1 unit estimated as income multiplied by the population was used for distribution of the property into the LAU-1 units within the NUTS-2 unit (Poland) or within the NUTS-3 unit (the Slovak Republic). â–  For Hungary,77 total income was used for distribution of the property into the LAU-1 units within the NUTS-2 unit. The weights for the districts are listed in appendix section 5.1.13. Note that in the Pilot Study, the property distribution into the LAU-1 units was estimated separately for the “industrialâ€? and “residentialâ€? property types.69 While the weights for the “residen- tialâ€? types for the Czech Republic were estimated by using income and population data, the weights for the “industrialâ€? types have been estimated based on territorial represen- tation of the “industrialâ€? land cover class.78 Before applying the industrial weights in the ï¬?nal analysis, a comparison of the residential and industrial weights was processed in order to estimate which of them is a be er approximation of the weights based on the real ï¬?xed asset formation data73 for the Czech Republic, which was used as a sample (ï¬?gure 2.2). The comparison showed that the industrial weights provide a much worse79 approximation than the residential weights. The reason is probably the low precision of the CORINE land cover data. There- fore, the industrial weights based on the CORINE land cover data were not used in the ï¬?nal report and the residential weights were applied instead. Figure 2.2. Sample Estimation of the Weights of the Districts a. Spatial distribution of the population—percent share b. Spatial distribution of the income—percent ratio of of the districts from the national total the districts to the national average (Figure continues on next page) 48 A World Bank Study Figure 2.2 (continued) c. Weights of the districts within their respective d. Weights of the districts within their respective province for the residential and industrial categories province for the network category Source: Intermap Technologies. Note: For graphical as well as tabular outputs for all V-4 countries, see section 5.1.13. 2. Network Property Type The network80 property type distribution from provinces (NUTS-2/3) into the district (LAU-1) level has been estimated according to distribution of the transportation net- work length into the districts, based on the transportation network reference data.81 The transportation network categories according to the structure of the reference data are listed in appendix section 5.1.10. First, the length of the network within each district was calculated separately for each network category. Second, the relative value of the network within each district was calculated as a weighted sum of the length of each transportation network category within the district, whereas the weights that the category represents pertains to the rela- tive value of 1 km of the network. The relative values of 1 km of the network were estimated based on data of the Road and Motorway Directorate of the Czech Republic,82 Railway Infrastructure Administra- tion of the Czech Republic,83 and the Czech Ministry of Transport,84 and are listed in appendix section 5.1.10. 3.4.3. DEFINITION OF THE VULNERABILITY CLASSES As a result of property reclassiï¬?cation, ï¬?ve distinct regionalization classes have been constructed, namely: Industrial-Building, Industrial-Content, Residential-Building, Residential- Content and Network. These ï¬?ve classes represent distinct combinations of the three Indus- try Branch super-classes85 and the two Asset Category super-classes86 (see table 2.10). Financial and Fiscal Instruments for Catastrophe Risk Management 49 Table 2.10. Five Vulnerability Classes as Combinations of the Super-classes of the Industry Branch versus Asset Category Asset Category Vulnerability Class Building Content Industrial Industrial-Building Industrial-Content Industry Branch Residential Residential-Building Residential-Content Network Network Source: Intermap Technologies. 3.4.4. LAND COVER CLASSES USED IN THE MODEL For the land cover analysis, the CORINE Land Cover data87 with 100 m resolution grid was used. For GIS analyses, the raster data have been vectorized. In this study, the “in- dustrialâ€? land cover category is represented by the class 121 (industrial or commercial units) of the CORINE classiï¬?cation. Unlike the pilot study, the “residentialâ€? CORINE land cover category was not used due to a low resolution of the CORINE data.88 3.5. Property Distribution into Hazard Zones Individual property locations were not available. Therefore, for the “industrialâ€? prop- erty type,89 the property distribution into flood hazard zones within each district was estimated according to the proportion of each hazard zone area in the “industrial landâ€? CORINE cover class. The distribution of the “residentialâ€? property type was estimated according to the distribution of all reference address points (for the Czech Republic,90 the Slovak Repub- lic,91 and Hungary92) into the flood hazard zones within the district. For Poland, the ad- dress points were not available. Therefore, the distribution of the se lement residential area polygons93 into the hazard zones was used instead. The distribution of the “networkâ€? property type into the flood hazard zones has been estimated according to the distribution of the network relative values into the flood haz- ard zones within each district, an analogy to the procedure used for the network prop- erty distribution into the districts, as described in section 3.4.2 (2). 3.6. Flood Scenario Determination Four types of extreme flood scenarios were determined, as shown below.94 â–  Areas of large catchments: • 7 catchments in the Czech Republic (see appendix section 5.1.9 a/i): Elbe/Labe, Moldau/Vltava, Berounka, Ohre, Morava, Dyje, and Oder/Odra • 5 catchments in the Slovak Republic (see appendix section 5.1.9 b/i): Danube/ Dunaj, Vah, Hron & Ipel, Hornad & Slana, and Tisa & Bodrog • 7 catchments in Hungary (see appendix section 5.1.9 c/i): Danube/Duna, Raba, Sio & Balaton, Drava, Tisza & Bodrog, Hernad & Sajo, and Koros/Crisul • 12 catchments in Poland (see appendix section 5.1.9 d/i): Upper Oder/Odra, Lower Oder/Odra, Warta, Notec, Nysa & Bobr, Upper Vistula/Wisla, Middle Vistula/Wisla, Lower Vistula/Wisla, San, Bug, Western Baltics, and Eastern Baltics 50 A World Bank Study â–  Areas covered by recent major flood events (see appendix sections 5.1.9 a/ii and 5.1.9 d/ii): • the scenario of the August 2002 Flood (applied for the Czech Republic); • the scenario of the July 1997 flood (applied for Poland and the Czech Republic). â–  Large complex geographical units: • for the Czech Republic, two scenarios based on large geographical units were used: the historical territories of Bohemia and Moravia (see appendix section 5.1.9 a/iii); • for the Slovak Republic, two aggregate scenarios were used: Danube/Dunaj (including Vah, Nitra, Hron, and Ipel) and Tisza/Tisa (including Bodrog, Hor- nad, and Slana) (see appendix section 5.1.9 b/ii); • for Hungary, two aggregate scenarios were used: Danube/Duna (including Drava, Sio & Balaton, Raba, and Ipoly) and Tisza (including Bodrog, Hernad, Sajo, and Koros/Crisul) (see appendix section 5.1.9 c/ii); • for Poland, two aggregate scenarios were used: Oder/Odra (including Warta, Notec, Nysa and Bobr) and Vistula/WisÅ‚a (including Bug and San) (see appen- dix section 5.1.9 d/iii). â–  Large cross-border scenario (see appendix section 5.1.9 e): • Oder/Odra (affected countries: Poland and the Czech Republic); • Danube/Duna/Dunaj (affected countries: the Slovak Republic and Hungary); • Tisza/Tisa (affected countries: the Slovak Republic and Hungary). 3.7. Flood Hazard Zoning Construction Intermap’s flood zoning model yields nationwide events covering 50, 100, 250, and 500 year flood events (ï¬?gure 2.3). The underlying assumptions of the Geomorphologic Regression model are as follows: â–  First, naturally flowing rivers shape their channel and flood plains according to basin-inherent forces and characteristics that can be described by flood water vol- ume and catchment descriptors like catchment area, slope, and so forth., whereby Figure 2.3a Example of the Digital Figure 2.3b. Example of the Terrain Model (DTM) with 5:1 Vertical Flood Hazard Zones in the Exaggeration Czech Republic Source: Intermap Technologies. Financial and Fiscal Instruments for Catastrophe Risk Management 51 flood water volume can also be described by catchment a ributes including cli- mate or rainfall information. â–  Second, the extent of actual flood water strongly depends on the shape of the flood plain that can be deï¬?ned by the vertical and horizontal distance to the rel- evant river. The regression model was calibrated and validated using a method of multiple non-linear regression analysis (MARS).95 The flood hazard zones are described in appendix section 5.1.8. 3.8. Flood Vulnerabilities During this analysis, the vulnerability ratio (damage to corresponding Total Value) is a dis- crete function of the property damage versus the extent of the flood (see ï¬?gure 2.4) expressed as vulnerability coefficients within a vulnerability matrix. The matrix explains the relation- ship of the flood event return period96 versus the return period of the flood hazard zone (that is, the likelihood of a flood occurring in that particular zone, see ï¬?gure 2.4, inset).97 Due to limited access to loss data broken up by the desired classes, ï¬?ve distinct vulnerability matrices were used and they are shown in table 2.10,98 namely: Industrial- Building, Industrial-Content, Residential-Building, Residential-Content and Network. Figure 2.4. Vulnerability Functions (Artiï¬?cial Examples) Source: Intermap Technologies. Note: Graph—example of the discrete vulnerability functions (damage vs. flood hazard zone) for differ- ent flood events (the markers correspond to the discrete vulnerability coefficients in the vulnerability matrix). Inset—example of the vulnerability matrix (damage coefficients for various hazard zones vs. flood event). 52 A World Bank Study The vulnerabilities were estimated from the historical loss data of the major insur- ance companies in the Czech Republic. The coefficients were then veriï¬?ed and calibrated according to aggregate real loss data99 during the August 2002 event and veriï¬?ed based on the loss data of the July 1997 event of the Czech Republic and Poland. During the calibration procedure, the loss for the scenario was modeled by using an initial form of the vulnerability matrix and the results were then compared to the actual loss data.100 Based on this comparison, the vulnerability matrices were multiplied by a correction coefficient and the loss was recalculated. This iterative procedure was repeated until the modeled loss value was equal to the real loss value. For veriï¬?cation, the obtained vulnerability matrices were then applied to the July 1997 scenarios (both for Poland and the Czech Republic) and the modeled losses were compared to the real losses. For both Poland and the Czech Republic, the modeled losses were only slightly higher than the real losses and, therefore, the vulnerability matrices did not have to be recalculated. This procedure ensures that, for real-event based sce- narios, the losses calculated by the model are equal to or slightly higher than the actual losses, and therefore in accordance with the safe-side principle. 3.9. Scenario Method of Loss Exceedance Curve (LEC) Calculation The scenario method assumes that the flood event occurs with a constant intensity (that is, flood return period or probability) within the whole area covered by the scenario.101 The losses are calculated for selected return periods of the flood (20, 50, 100, 250, and 500 years). The algorithm of the loss calculation for the scenario method is shown in ï¬?gure 2.5. Figure 2.5. Algorithm of The Loss Calculation for One Event (the Flood Return Period) under the Scenario Method Property value & location data Identification of Loss calculation Results property affected Flood zone data Source: Intermap Technologies. Financial and Fiscal Instruments for Catastrophe Risk Management 53 3.10. Extrapolated Stochastic Method of LEC Calculation For each country, LECs were calculated for several scenarios. However, the main aim of this work was to ï¬?nd an LEC that characterizes the probable losses of the individual countries as well as of the whole V-4 Group. The various scenario-based LECs indicate the range in which the probable loss occurs. Below, the method that enables one to ï¬?nd the appropriate mean value of the LEC for each country is described. This method is based on extrapolation of the stochastic method, taking into consideration the behavior of the probable loss as a function of distinct characteristics of a territory, such as prop- erty value, area, and property distribution in the flood hazard zones. The stochastic method calculates the loss based on a large number of randomly gen- erated scenarios. As the input data, especially the historical n-years discharges and dis- charge correlations, were available only for the Czech Republic, an extrapolation of the Czech Republic results was used in order to calculate the LEC in the remaining countries as well as the whole V-4 Group. The stochastic method is described in sections 3.10.1, 3.10.2, and 3.10.3. The extrapolation of the Czech Republic results toward the remaining countries is described in section 3.10.4. 3.10.1. GENERAL CONCEPT OF THE STOCHASTIC METHOD102 Under the stochastic method, the loss exceedance curve (LEC) is calculated based on the hypothetical events generated by the Monte-Carlo method. Each event speciï¬?es the area affected by the flood and the flood intensity in each spatial unit. The events are gener- ated by the Monte Carlo method and consider the historical information103 about the discharges in the gauging stations104 and the correlations of the discharges within each pair of stations. As the input data for the Monte Carlo method wee available only for the Czech Re- public, extrapolation was used for calculation of the LEC in the remaining countries.105 The overall procedure of the Monte-Carlo method of LEC calculation is described in the following paragraphs. 3.10.2. ASSUMPTIONS OF THE METHOD â–  Spatial Units. For the purposes of the Monte Carlo method, a country area is divided into spatial units, for which the constant flood return period is assumed within each event. In particular, the spatial units for the Czech Republic are de- termined by the 4x4 km square grid.106 â–  One Event Represented by Monthly Maximum Discharges. Each event is rep- resented by maximum monthly discharges at each gauging station. The length of one month was selected as an optimum value, as (a) given the size of the catch- ments in the V-4 countries, there is a high probability that any flood event oc- curs within this period, and (b) occurrence of more signiï¬?cant floods within this period is very unlikely.107 In total, 120,000 synthetic events representing 120,000 ï¬?ctive months in 10,000 ï¬?ctive years were generated. 3.10.3. STOCHASTIC METHOD PROCEDURE â–  Discharge Simulation. The discharge values for all the 120,000 events and the 145 gauging stations were generated by using the Monte Carlo simulation. The input 54 A World Bank Study values were (a) historical N-years of discharges108 at the gauging stations and (b) correlation matrices of the monthly maximum discharges109 within each pair of stations. The results were 120,000 events representing maximum discharges at all 145 stations. The method assumes a log-normal distribution of the discharges.110 â–  Discharge Interpolation. In this step, the discharge values calculated for all 145 gauging stations were extended to the flood return period in all 5,481 spatial units. First, the discharge values in the gauging stations were standardized.111 Second, the information of the standardized discharge values was extended to all spatial units by using the Kriging112 2D spatial interpolation method. Finally, the standardized values in the spatial units were transformed to flood return period values. The result of this step is a set of 120,000 events (that is, maps), where each event represents a particular return period of a synthetic flood in each spatial unit. For sample of the events, see ï¬?gure 2.6. â–  Event-set Generation Overview (see ï¬?gure 2.7). â–  Loss Calculation. For each event generated, the loss is calculated by the same algorithm as applied in the scenario method (see ï¬?gure 2.5). Finally, the loss re- turn periods113 are assigned the loss values which were calculated for each event. Figure 2.6. Samples of the Scenarios Generated by the Stochastic Method Source: Intermap Technologies. Note: Colors represent flood intensity in each spatial unit measured by flood return period in years. Financial and Fiscal Instruments for Catastrophe Risk Management 55 Figure 2.7. Event-Set Generation Overview Real discharges (145 x 30y x 12m measurements) Simulated RTP in each spatial unit (5,481 x 10,000y x 12m values) Discharge Simulation Discharge interpolation Simulated discharges (145 x 10,000y x 12m values) Source: Intermap Technologies. Note: Colors represent flood intensity in each spatial unit measured by flood return period in years. 3.10.4. EXTRAPOLATION OF THE RESULTS TO THE REMAINING COUNTRIES As the input data necessary for the stochastic method were available for the Czech Repub- lic only, the results for the remaining countries were calculated by using extrapolation of the results obtained for the Czech Republic. The extrapolation considers three factors: a. Difference in the property value in the particular country in comparison with the Czech Republic. This factor was calculated as a proportion of the total prop- erty of the respective countries. b. Differences in property distribution in the flood hazard zones in the particular country in comparison with the Czech Republic. This factor was calculated by us- ing proportion of the property in distinct flood zones in the respective countries. c. Differences in the country area in comparison to the Czech Republic. This factor considers the difference between the return period of the flood and return period of the loss.113 The larger the area observed, the bigger is the difference between when the return period occurs. For example, a 100-year loss in a small area, such as district, may represent 100-year flood in the whole area, while in a larger area such as province, it would represent an average return period of the flood that is lower than 100 years. Moreover, in a very large area, such as a country or a group of countries, a considerable area would remain unaffected by the flood at all. This factor was derived on the Czech Republic data, separately for each return period 56 A World Bank Study Figure 2.8. A Sample of the Function Used for the Modeling of the Factors 5.0 4.5 4.0 3.5 3.0 Loss 2.5 2.0 1.5 1.0 0.5 0 1 10 100 1,000 10,000 Area Source: Intermap Technologies. Note: Figure shows area differences based on empirical data, as a function of the territory area (that is, country, NUTS-4, NUTS-3, LAU-1, scenario) vs. probable loss. of the loss, by calculation of the LEC for the whole country, the lower adminis- trative units (NUTS-4, NUTS-3, LAU-1) and the territories deï¬?ned by the flood scenarios (see ï¬?gure 2.8).114 The ï¬?nal formula for calculation of the loss for a particular country and flood return period is thus: Loss(C, n) = Loss(CZ, n) * PF * FzF * AF(n) , where n = the flood loss return period, PF = factor considering for property value difference (see the bullet (a) above), FzF = factor considering for flood hazard zones difference (see the bullet (b) above), AF = factor considering area difference for the given n (see the bullet (c) above), Loss(C, n) = n-years loss in a country C. 3.11. Outputs 3.11.1. LOSS EXCEEDANCE CURVE AND SURVIVAL FUNCTION The main output of this analysis is an estimate of the flood loss probability distribu- tion for selected scenarios expressed by the loss exceedance curve (LEC—see ï¬?gure 2.9), which returns the probable loss (vertical axis) corresponding to a certain return period (horizontal axis). The primary concern for the modeling are losses with extremely high return periods, that is, with return periods between 20 and 500 years. Alternatively, the probability distribution can be expressed as a survival function, which returns a probability (vertical axis), against a loss (on horizontal axis) that will be exceeded (see ï¬?gure 2.10). Financial and Fiscal Instruments for Catastrophe Risk Management 57 Figure 2.9. Sample of LEC as a Figure 2.10. Sample of Survival Function of the Property Loss versus Function as a Function of the the Return Period (RTP) of the Flood Probability of the Flood Loss Event Loss Event (in Percent) versus the Property Loss 2,500 5.0 4.5 2,000 4.0 Loss (EUR millions) Probability [%] 3.5 1,500 3.0 2.5 1,000 2.0 1.5 500 1.0 0.5 0 0 0 100 200 300 400 500 0 500 1,000 1,500 2,000 2,500 Return period (years) Loss (EUR millions) Source: Intermap Technologies. 3.11.2. LEC OF THE POOL OF COUNTRIES VS. INDIVIDUAL COUNTRIES As described in section 3.10.4, the Loss Exceedance Curve (LEC) of a pool of countries is not an additive function of the LEC of individual countries. A sum of the LEC of the in- dividual countries is shown in section 4.2.1 and appendix section 5.2.1 for a comparison with the LEC of the whole V-4 Group (that is, pooled). 3.11.3. LEC CONFIDENCE INTERVALS As the variance and possible error of the inputs have very complex magnitudes,115 it is not possible to estimate the standard probability-based conï¬?dence intervals of the calcu- lated loss exceedance curves,116 that is, the ï¬?nal loss exceedance curve should be inter- preted as a point estimate or estimate of the mean value (or the most probable value) of the loss for a given flood return period and scenario (see ï¬?gure 2.11). However, the conï¬?dence limits of the LEC can be set by an expert estimate, based on analysis of the LEC obtained by using the scenario method in the particular countries (see section 4.2.1). The conï¬?dence intervals were thus set in the following way: â–  For the loss return period 20 years, as a symmetric interval LEC of +/− 60 percent â–  For the loss return period 50 years, as a symmetric interval LEC of +/− 50 percent â–  For the loss return period 100 years and higher, as a symmetric interval of LEC +/− 40 percent. 3.11.4. REGIONAL DISTRIBUTION OF THE ESTIMATED LOSS In order to estimate the loss structure and regional distribution in the case of an extreme flood event, the return period of 250 years was used as standard for the outputs in this study. For each scenario, the spatial distribution of the losses into the regions for the 250 year flood was calculated (a) as a proportion of the total loss (see ï¬?gure 2.12) and (b) as a loss intensity (a proportion of the province’s loss to the province’s total property; see ï¬?gure 2.13). 58 A World Bank Study Figure 2.11. Sample of the LEC with the Conï¬?dence Limits 12,000 Loss (EUR millions) 10,000 Upper confidence limit 8,000 LEC 6,000 4,000 Lower confidence limit 2,000 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. Figure 2.12. Sample of the Figure 2.13. Sample of the Regional Loss Structure (% of the Regional Loss Structure (% of the Total Loss) Province Property) Source: Intermap Technologies. 3.11.5. LOSS STRUCTURE For each scenario, the 1-dimensional as well as the 2-dimensional classiï¬?cation of the losses was calculated as well as the loss regional distribution. For the outputs, see ap- pendix section 5.3. 3.11.6. AVERAGE LOSS STRUCTURE AND REGIONAL DISTRIBUTION For each country, the theoretical scenario assuming the 250-year flood within the whole country is calculated. Based on this scenario, the average loss structure as well as re- gional distribution is estimated. For the average loss regional distribution, see section 5.2.4. For the average loss structure,117 see appendix section 5.2.5 Financial and Fiscal Instruments for Catastrophe Risk Management 59 4. Outputs 4.1. Property Structure 4.1.1. REGIONAL PROPERTY DISTRIBUTION INTO PROVINCES In this section, regional property distributions to NUTS-3 units, that is, provinces (NUTS- 2 in Poland) for each of V-4 countries is shown. For tabular data as well as a regional split into industry branches,118 asset categories119 and institutional sectors,120 see appendix section 5.1.12. a. Regional Property Distribution in the Czech Republic121 Figure 2.14. Regional Property Distribution to NUTS-3 Units (Provinces) for the Czech Republic (in %) Source: Intermap Technologies. 60 A World Bank Study b. Regional Property Distribution in the Slovak Republic122 Figure 2.15. Regional Property Distribution to NUTS-3 units (Provinces) for the Slovak Republic (in %) Source: Intermap Technologies. c. Regional Property Distribution in Hungary123 Figure 2.16. Regional Property Distribution to NUTS-3 Units (Provinces) for Hungary (in %) Source: Intermap Technologies. Financial and Fiscal Instruments for Catastrophe Risk Management 61 d. Regional Property Distribution in Poland124 Figure 2.17. Regional Property Distribution to NUTS-2 Units (Provinces) for Poland (in %) Source: Intermap Technologies. 62 A World Bank Study 4.1.2. REGIONAL PROPERTY DISTRIBUTION INTO DISTRICTS In this section, the regional property distribution to LAU 1 unit (districts) within each of V-4 countries is shown. For tabular data as well as population and salary regional distri- bution, see appendix section 5.1.13. a. Property Distribution into Districts in the Czech Republic125 Figure 2.18. Regional Property Distribution to LAU 1 Units (District) for the Czech Republic in % of the Total Property in the Country Source: Intermap Technologies. b. Property Distribution to Districts in the Slovak Republic126 Figure 2.19. Regional Property Distribution to LAU 1 Units (District) for the Slovak Republic in % of the Total Property in the Country Source: Intermap Technologies. Financial and Fiscal Instruments for Catastrophe Risk Management 63 c. Property Distribution to Districts in Hungary127 Figure 2.20. Regional Property Distribution to LAU 1 Units (Districts) for Hungary in % of the Total Property in the Country Source: Intermap Technologies. d. Property Distribution to Districts in Poland128 Figure 2.21. Regional Property Distribution to LAU 1 Units (District) for Poland in % of the Total Property in the Country Source: Intermap Technologies. 64 A World Bank Study 4.2. Loss Calculation Results 4.2.1. LOSS EXCEEDANCE CURVES In this section, the loss exceedance curves calculated for each country are summarized for each country separately, for total property and public property. For the detailed overview of the results, see the data appendix. Figure 2.22. Summary of the Exceedance Curves for the Czech Republic for Losses on Total Property 12,000 CZ Labe / Elbe CZ Berounka CZ Morava river CZ Dyje CZ Odra (upper) CZ Ohre CZ Vltava CZ 2002 CZ 1997 CZ Bohemia 10,000 CZ Moravia CZ Stochastic 8,000 Loss (EUR millions) Stochastic 6,000 4,000 2,000 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. Financial and Fiscal Instruments for Catastrophe Risk Management 65 Figure 2.23. Summary of the Exceedance Curves for the Czech Republic for Losses on Public Property 4,000 CZ Labe/Elbe CZ Berounka CZ Morava river CZ Dyje CZ Odra (upper) CZ Ohre 3,500 CZ Vltava CZ 2002 CZ 1997 CZ Bohemia CZ Moravia CZ Stochastic 3,000 Loss (EUR millions) 2,500 Stochastic 2,000 1,500 1,000 500 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. Figure 2.24. Summary of the Exceedance Curves for the Slovak Republic for Losses on the Total Property 25,000 SK Dunaj / Danube SK Vah + Nitra SK Hron + Ipel SK Hornad + Slana SK Tisza + Bodrog SK Dunaj / Danube Large SK Tisza Large SK Stochastic 20,000 Loss (EUR millions) 15,000 Stochastic 10,000 5,000 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. 66 A World Bank Study Figure 2.25. Summary of the Exceedance Curves for the Slovak Republic for Losses on Public Property 8,000 SK Dunaj/Danube SK Hron + Ipel SK Tisza + Bodrog SK Tisza Large 7,000 SK Vah + Nitra SK Hornad + Slana SK Dunaj/Danube Large SK Stochastic 6,000 Loss (EUR millions) 5,000 4,000 Stochastic 3,000 2,000 1,000 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. Figure 2.26. Summary of Exceedance Curves for Hungary for Losses on Total Property 18,000 HU Danube/Duna HU Drava HU Hernad + Sajo 16,000 HU Tizsa + Bodrog HU Raba HU Sio HU Koros HU Danube Large 14,000 HU Tisza Large HU Stochastic 12,000 Loss (EUR millions) 10,000 Stochastic 8,000 6,000 4,000 2,000 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. Financial and Fiscal Instruments for Catastrophe Risk Management 67 Figure 2.27. Summary of Exceedance Curves for Hungary for Losses on Public Property 4,500 HU Danube/Duna HU Drava HU Hernad + Sajo 4,000 HU Tizsa + Bodrog HU Raba HU Sio HU Koros 3,500 HU Danube Large HU Tisza Large HU Stochastic 3,000 Loss (EUR millions) 2,500 Stochastic 2,000 1,500 1,000 500 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. Figure 2.28. Summary of the Exceedance Curves for the Poland for Losses on Total Property 50,000 PL Vistula Upper PL Vistula Middle PL Vistula Lower PL Bug PL San PL Odra Upper 45,000 PL Odra Lower PL Warta PL Notec PL Nysa+Bobr PL Baltic West PL Baltic East 40,000 PL 1997 PL Vistula Large PL Odra Large PL Stochastic 35,000 Loss (EUR millions) 30,000 25,000 20,000 Stochastic 15,000 10,000 5,000 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. 68 A World Bank Study Figure 2.29. Summary of the Exceedance Curves for the Poland for Losses on Public Property 18,000 PL Vistula Upper PL Vistula Middle PL Vistula Lower PL Bug PL San PL Odra Upper 16,000 PL Odra Lower PL Warta PL Notec PL Nysa+Bobr PL Baltic West PL Baltic East PL 1997 PL Vistula Large PL Odra Large PL Stochastic 14,000 12,000 Loss (EUR millions) 10,000 8,000 Stochastic 6,000 4,000 2,000 0 0 100 200 300 400 500 Return period (years) Source: Intermap Technologies. Figure 2.30. LEC for Loss on Total Property for Each Country and the V-4 Group 50,000 CZ SK Sum HU 45,000 PL V-4 Sum over individual countries 40,000 V-4 Loss (EUR millions) 35,000 30,000 25,000 20,000 Poland 15,000 Slovak Republic 10,000 Hungary Czech 5,000 Republic 0 0 50 100 150 200 250 300 350 400 450 500 Return period (years) Source: Intermap Technologies. Note: Dashed line represents the sum of LEC of individual countries) and results of the extrapolated stochastic method. Financial and Fiscal Instruments for Catastrophe Risk Management 69 Figure 2.31. LEC for Losses on Public Property for Each Country and V-4 Group 18,000 CZ SK HU 16,000 PL Sum V-4 Sum over individual countries 14,000 Loss (EUR millions) 12,000 V-4 10,000 8,000 Poland 6,000 Slovak 4,000 Republic Czech 2,000 Republic Hungary 0 0 50 100 150 200 250 300 350 400 450 500 Return period (years) Source: Intermap Technologies. Note: Dashed line represents the sum of LEC of individual countries) and results of the extrapolated stochastic method. Conclusion Estimation of possible losses is a complex task and at times a convoluted exercise with many limitations in access to input data. The model is based on the assumption that the modeled event in the future should have similar impacts as those that occurred in the past. Extreme events occur very rarely, and the history of detailed measurements of flood characteristics is rather short, while the history of mapping the loss records is even shorter. Any estimation of the outcome of an event in the future is biased given insuf- ï¬?cient historical records, large changes in property distributions and characteristics and therefore the modeling is based only on a limited comparable experience. This causes a relatively high ratio of residuals in such models. A typical issue is in the construction of the vulnerability functions. Even if the prop- erty, its values and locations can be precisely mapped, it is usually very hard to compare them with the historical loss data that is rarely classiï¬?ed, precisely due to the speciï¬?c conditions when quick help to the flooded population and property is necessary and there is a lack of time and resources for accurate loss localization and evaluation. For this reason, simpliï¬?cations must be introduced to asset classiï¬?cations in order to obtain comparable total value and loss classes for vulnerability estimations. Another issue is the proper localization of properties. Usually, poor data on geo- graphic location of the property is available at both the state authority level and the insurance market. When a country-wide modeling is carried out, only a sample of prop- erties can be localized and then consequences are derived based on those samples. 70 A World Bank Study Therefore, the proxy distribution of property to distinct hazard zones as well as to regional geographic units, such as river catchments, districts, or zip code areas, is ap- plied despite the fact that those geographic units may be internally non-homogeneous in respect to the distribution of the potential flood behavior in such space. The approach in this analysis has been based on several simpliï¬?cations and the accuracy of the territo- rial distribution of property depends on the extent to which ancillary variables and the assumptions reflect the real distribution. As well, mapping of the hazard is quite challenging. The more precise the model- ing is executed, the more resources, effort, and time are required as well as as expensive input data such as gauging station records. In the case of accurate hazard zone delimita- tion, the geographic location of the property may be insufficient, and if accurate flood depth parameters are derived, there may be a lack of data on building heights or the storey on which the properties are located. Other issues pertain to the insufficiency of flood protection characteristics, the behavior of the population and the authorities before and during critical events, the sum of their experiences from the past, all of which reflect unknown parameters, just to mention a few. Based on the limitations mentioned above, several assumptions were made in this analysis. Some of the assets such as automobiles were not considered in this phase due to lack of data. The asset classiï¬?cations were simpliï¬?ed to ï¬?ve classes, each having distinct vulnerability functions. The precise spatial distribution of the property was replaced with regionalization to districts based on the assumptions that spatial distribution of proxy variables such as population and income distribution, land cover classes, and regional distribution of networks, are the leading parameters for these considerations. The hazard zones were computed based on the return period, as the value of delineation of flood depth is very ambiguous due to lack of digital elevation models with sufficient vertical resolution. The key input of the model is the property value data in the 3-dimensional split, the main sources of which are data of the national statistical authorities.129 The property values were re-classiï¬?ed and spatially re-distributed (regionalized) into the provinces and then into the districts.130 The property values were then re-distributed into the flood hazard zones within each district.131 The regionalized and re-classiï¬?ed property data show that the total property value in the whole V-4 Group is EUR 2,506 billion 132 of which 36 percent is public property, 37 percent property of private corporations133 and 27 percent property of households.134 According to the asset category135 classiï¬?cation, dwellings, buildings and structures rep- resent 72 percent, machinery and equipment 16 percent, and inventories and household equipment 12 percent of total property. As for the regional distribution of the property into the four countries, 50 percent of the total property value is located in Poland, 23 percent in the Czech Republic, 16 percent in Hungary, and 11 percent in the Slovak Republic. As for the property distribution into the flood hazard zones within the whole V-4 Group, 8 percent of the property value is located in floodplains with annual probability of flooding of at least 2 percent (that is, in the flood hazard zones with a return period of 20 and 50 years), 22 percent in floodplains with annual probability of flooding 0.2-2.0 percent (in flood hazard zones with a return period of 100, 250, and 500 years) while 70 percent of the property is located outside the flood prone areas.136 Financial and Fiscal Instruments for Catastrophe Risk Management 71 The property distribution into the flood hazard zones within the individual coun- tries is as follows: The proportion of the property value in all flood hazard zones (that is, in areas with at least 0.2 percent annual probability of flooding) is the highest in the Slo- vak Republic (54 percent), medium in Hungary and Poland (33 percent and 29 percent, respectively) and the lowest in the Czech Republic (20 percent).136 Based on the re-classiï¬?ed and regionalized property data redistributed into the flood hazard zones, plus the vulnerability functions137 calibrated according to historical loss data separately for each property class, the loss exceedance probabilities were predicted for different flood scenarios. Potential losses for distinct return periods of the flood loss were represented on the resulting dependence—the loss exceedance curve (LEC). In total, the LECs were calculated by using a scenario method138 for 45 distinct flood scenarios of 4 types, in particular: 31 scenarios based on large catchments, 3 scenarios based on recent major flood events, 8 scenarios based on the large complex geographical units, and 3 large cross-border scenarios.139 As well, 11 scenarios were deï¬?ned for the Czech Republic, 7 for the Slovak Republic, 9 for Hungary, 15 for Poland, and 3 for cross- border events. Moreover, in order to evaluate the possible loss of the whole countries as well as the V-4 Group, the loss exceedance curves were calculated for each country and the V-4 Group by using extrapolation of the stochastic method.140 According to the probable loss estimated by the extrapolated stochastic method characterizing the probable losses in the whole V-4 Group or in a particular country, the estimated probable loss on the total property for the V-4 Group is approximately EU 19 billion for the return period of 100 years (that is, the annual loss occurrence probability of 1 percent) and EUR 31 billion for the return period 250 years (annual occurrence prob- ability of 0.4 percent). A loss of EUR 11 billion occurs with the probability of 2 percent (a return period of 50 years) and a loss of EUR 5 billion with a 5 percent probability (a return period of 20 years). Classiï¬?cation of the average loss structure based on the scenario method results shows141 that the loss in the public institutional sector represents 31 percent,142 the loss in the private sector133 represents 42 percent, and the loss in the household sector repre- sents 25 percent of the total probable loss. As for the asset category135 classiï¬?cation, the average probable loss on dwellings, buildings and structures represents 68 percent, the loss on machinery and equipment 20 percent, and the loss on inventories and household equipment represents 12 percent of the total loss. These results represent an average structure of the loss distribution for the extreme flood events within the whole V-4 Group, however, the estimated loss structure slightly differs depending on the flood scenario.143 The probable loss144 on the total property in the particular countries related to the probable loss in the whole V-4 Group, is approximately 53 percent for Poland, 29 percent for the Slovak Republic, 23 percent for Hungary, and 18 percent for the Czech Republic. As the probable loss of the pool of countries calculated by the extrapolated stochastic method is not an additive function of probable losses in the individual countries,145 the probable loss (for a given return period) for the whole V-4 Group is lower than the sum of probable losses of the individual countries. For example, the probable loss on public property for the return period of 250 years for the V-4 Group is approximately EU 11 bil- lion while the sum of probable losses in the individual countries is approximately EUR 12.5 billion. 72 A World Bank Study In absolute values, for the return period of 250 years (0.4 percent occurrence proba- bility) the probable losses on total property in the particular countries are approximately EUR 16 billion EUR for Poland, EUR 9 billion for the Slovak Republic, EUR 7 billion for Hungary, and EUR 5.5 billion for the Czech Republic. The probable loss on public property146 in the particular countries for the return period of 250 years (0.4 occurrence probability) is approximately EUR 5.9 billion for Poland, EUR 3.1 billion for the Slovak Republic, and EUR 1.8 billion for Hungary as well as for the Czech Republic. All of the probable loss values mentioned above represent the mean values of the es- timates for a given flood return period and country. In addition, the conï¬?dence intervals of the probable loss were estimated.147 The estimated upper conï¬?dence limit of the prob- able loss on the total property in the whole V-4 Group is approximately 26 EUR billion for the return period of 100 years and EUR 43 billion for the return period of 250 years. For losses on public property, the estimated upper conï¬?dence limit is EUR 9 billion for the return period of 100 years and EUR 14 billion for the return period of 250 years. The estimated upper conï¬?dence limits for the probable loss on total property in the particular countries for the return period of 250 years are approximately EUR 23 billion for Poland, EUR 12.5 billion for the Slovak Republic, EUR 10 billion for Hungary, and EUR 8 billion EUR for the Czech Republic. For the probable loss on public property, the upper estimated limits are EUR 8 billion for Poland, EUR 4 billion for the Slovak Repub- lic, and EUR 2.5 billion for Hungary as well as for the Czech Republic. The detailed results of the loss modeling for all 45 scenarios are in appendix sec- tion 5.3. As an example, for the cross-border scenario for Odra, the probable loss for the return period of 100 years is approximately EUR 8.0 billion on total property (EUR 3.1 billion on public property) and for the return period of 250 years it is approximately EUR 13.5 billion on total property (EUR 5.2 billion on public property). Losses in the asset category of dwellings, buildings and structures are 65 percent, losses in the machinery and equipment are 22 percent, and losses in the inventories and household equipment represent 13 percent of total losses. The provinces with the highest loss values are DolnoÅ›lÄ…skie (Poland, 36 percent of total loss), Moravskoslezský kraj (Czech Republic, 18 percent), Opolskie (Poland, 11 percent), ÅšlÄ…skie and Lubuskie (Poland, each of them 10 percent of the total loss). T he provinces with the highest loss intensity148 are DolnoÅ›lÄ…skie (5 percent), Lubuskie (4.7 percent), Moravskoslezský kraj (4.5 percent), and Opolskie (4.2 percent of the province’s property). Possible improvements of the model for obtaining more precise results in the future could include: (a) use of the probabilistic method for all countries, which would enable a more precise flood loss evaluation for each of the countries or the V-4 Group (this approach would require historical discharge data for all countries); (b) use of a more detailed digital elevation model for obtaining more precise flood hazard zones (digital elevation data of the NEXTMap Europe are currently available for the Czech Republic and will be available for the other V-4 countries in the near future); and (c) creation of public sector-owned property registers, which would enable more precise flood hazard evaluation of public property. As well, additional model improvements might include: (d) use of flood depth in- stead of flood hazard zones, which would reï¬?ne the vulnerability functions (so far, the rough vertical resolution of the existing digital elevation models has not enabled gener- Financial and Fiscal Instruments for Catastrophe Risk Management 73 ating credible flood depth maps); and (e) consideration of the pluvial flood in addition to the river flood—be er resolution of digital elevation data, extensive precipitation gauge data as well as maps of soils and land-use with reasonable scales will be necessary for pluvial flood modeling. Notes 1. Antecedent Precipitation Index (APIn) is an index derived from rainfall depth for the antecedent n days (or hours); see bibliographic reference # [43]. 2. The team working on the Study included, ï¬?rst of all, the main contributor Jiří Vohlídal who led the collection of the asset data, probable flood loss modeling, producing the charts and tables, and the compilation of the ï¬?nal text. Other leading team members included Vladislav HanÄ?il who did most of the revisions of the texts in various phases of the study as well as contributed to several parts of the methodology; Jan Roubalík was responsible for flood hazard modeling; Lukáš Mako- viÄ?ka leading the effort for creating the maps in the text. The team was coordinated by Ladislav Garassy and the overall activity was managed by Ivo Bánovský. 3. The appendix is not included in this published World Bank Study. It can be accessed at h p:// documents.worldbank.org/curated/en/2012/01/16242871. 4. For more details, see Appendix section 5.1.1 5. Except of Poland where they correspond to the NUTS-2 level 6. For more details about NUTS and LAU classiï¬?cation see [3] 7. Formerly NUTS-4 8. Formerly NUTS-5 9. Administration, education, health, culture, transportation networks, and so forth. 10. Industry, ï¬?nancial, and so forth. 11. See sections 3.2.1 and 3.2.2 12. See sections 3.2.3 and 3.2.4 13. See sections 3.2.5 and 3.2.6 14. For more details, see section 3.3 15. For more details, see Appendix section 5.1.2 16. See [4] 17. See [8]. 18. Hiszi-Map, Corvin u. 3, Gyula, Hungary. Internet access: h p://www.hiszi-map.hu; see [7]. 19. See [10]. 20. See [4]. 21. Vertical RMS of 2 meters in flat region and 5 meters in hilly landscape; see [10]. 22. Vertical RMS of 3.5 meters in flat region and 7.5 meters in hilly landscape; see [10]. 23. Vertical RMS of 5 meters in flat region and 10 meters in hilly landscape; see [11]. 24. Vertical RMS of 20 meters; see [10]. 25. See [15]; for information about FRAT 1.0—joint venture application of GMR in CE Europe see [12]. 26. Swiss Reinsurance Company, Mythenquai 50/60, 8022 Zurich, Swi erland. Internet access: h p://www.swissre.com. 27. See appendix section 5.1.8. 28. Currently Prague Office of the Intermap Technologies. 29. Environmental Protection and Water Management Research Institute (VITUKI—Környezetvé- delmi és Vízgazdálkodási Kutató Intézet), Kvassay JenÅ‘ út 1, Budapest, Hungary. Internet access: h p://www.vituki.hu. 30. See [16]. The data set contains flood zones with return periods of 100 and 1,000 years and flood protection dykes of only the 20 major rivers of Hungary. 31. See section 3.8. 32. See section 2.2. 33. For more details about difference between ISIC and NACE, see [14]. 34. For complete list of all NACE codes see [2]. 74 A World Bank Study 35. See ï¬?gures 2.1–2.4. 36. See section 3.4. 37. See section 3.5. 38. See section 3.8. 39. For the complete ESA’95 institutional sectors classiï¬?cation See [5], Internet access: circa.europa. eu/irc/dsis/nfaccount/info/data/esa95/en/een00069.htm. 40. The reason for excluding the inventories in the industry branches A and B is that the losses on harvest and living stock, which is a major part of the inventories in these branches, are not consid- ered in this study, as they are mostly covered by commercial insurance or other resources. 41. A simpliï¬?ed reclassiï¬?cation of institutional sectors on “publicâ€? and “non-publicâ€? was used, where the “non-public“ class includes the “privateâ€? and “householdsâ€? classes deï¬?ned in section 3.2.6. 42. Examples of Infrastructure Public: public administration, education, healthcare, public trans- port infrastructure. 43. Examples of Infrastructure Non-public: non-public education, healthcare. 44. Examples of Enterprise Public: public industry, agriculture, real estate. 45. Examples of Enterprise Non-public: private industry, agriculture, real estate, transportation. 46. the property type dimensions are described in sections 3.2.1–3.2.6. 47. Data source of the ï¬?xed asset value and structure: [29], alternatively [30]. 48. Data source of the inventories values and structure: [30]. 49. Data source: [21], alternatively [23]. 50. Data source: [32], [33]. 51. Data source: [18], alternatively [35]. 52. Reported by the Central Statistical Office of Poland (that is, private + households sectors accord- ing to the terminology used in this study). 53. For example, the reported ï¬?xed asset value in Poland is lower than the ï¬?xed asset value re- ported in the Czech Republic, although the GDP of Poland is approximately 230 percent of the GDP of the Czech Republic. 54. The property type dimensions are described in the sections 3.2.1–3.2.6. 55. Data source: [29]. 56. Data source: [30]. 57. Data source: [31]. 58. The institutional sector versus industry structure for the Slovak Republic is not available; see also section 3.2.10 b). 59. Data source: [33]. 60. Data source: [34]. 61. Data Source: [32]. 62. Data source: [18], alternatively [35], p. 662–669; the adjustments described in paragraph 3.2.10 d) were used. 63. The information about the 2007 property structure was not available yet while the total prop- erty value already was available. 64. The Czech Republic, the Slovak Republic, Hungary. 65. Poland. 66. Resource of the Regional gross ï¬?xed asset balance data at NUTS-2 level for Poland: [18], Gross value of ï¬?xed assets by national economy sectors. 67. Resource of the Regional gross ï¬?xed asset balance data for the Czech Republic at NUTS-3 level: [19]. 68. Resource of the Regional gross ï¬?xed asset formation data for Hungary at NUTS-3 level: [20], table Gross ï¬?xed capital formation at NUTS level 3 (reg_e2gfcf). 69. Resource of the Regional gross ï¬?xed asset formation data for the Slovak Republic at NUTS-3 level: [21]. 70. The residential, industrial, and network property types were deï¬?ned in section 3.2.2. 71. Except for the Czech Republic—see [22]. 72. The Czech Republic, the Slovak Republic. Financial and Fiscal Instruments for Catastrophe Risk Management 75 73. Hungary, Poland. Even if the ï¬?rst level administrative units of Hungary (that is, provinces/“megyeâ€?) correspond to NUTS-3 level, property distribution is available only for groups of provinces, that is, NUTS-2 level. 74. See [22]. 75. Resource of salary and population data within the LAU-1 units in Poland: [18]. 76. Resource of salary and population data within LAU-1 units in the Slovak Republic: [24], tables 3–7. Average monthly wage of employees by economic activities and 2-1. Mid-year population and population change. 77. Resource of 2001 salary data within LAU-1 units in Hungary: [41], p.5 (chart Egy állandó la- kosra jutó szja-alapot képezô jövedelem 2001-ben (ezer Ft)). 78. For more about land cover classiï¬?cation, see [4] and paragraphs 3.4.4 and 5.1.11. 79. The lack of ï¬?t measured by the mean absolute error (MAE) for the industrial weights is approxi- mately twice as higher than for the residential weights. 80. As deï¬?ned in section 3.2.2 81. Sources of the transportation network reference data: • The Czech Republic. The transportation network of 1:25 000 Digital Model of the Territory (Digitální model území 1:25 000) vector data has been used (see [9]). • The Slovak Republic. The transportation network of the Digital Vector Map of the Slovak Re- public 1:50,000 of Mapa the Slovak Republic Plus has been used (see [8]). • Hungary. The transportation network of the Digital Vector Map of Hungary 1:10,000 of HISZI- Map has been used (see [7]). • Poland. The transportation network of the map of Europe 1:200,000 of Mapa the Slovak Repub- lic Plus has been used. 82. Roads and Motorways in the Czech Republic 2009, see [45]; Motorways and Speedways Back- bone Network in the Czech Republic, see [46]; Annual Report 2008, see [47]. 83. Annual Report 2008, see [48]. 84. Railway Transport, see [49]. 85. See section 3.2.2. 86. See section 3.2.4. 87. See [4]; for the complete CORINE classiï¬?cation, see Appendix section 5.1.11. 88. Based on the analysis of the reference address points CORINE classiï¬?cation, more than 20 percent of address points belong to the agriculture CORINE land cover category. Therefore the CORINE Land Cover data were not used for identiï¬?cation of the Residential property type. 89. For deï¬?nition of “industrialâ€? and “residentialâ€? property types, see paragraph 3.2.2. 90. Data source of the address points for the Czech Republic: [36] and [37]. 91. Data source of the address points for the Slovak Republic: [38]. 92. Data source of the address points for Hungary: [39]. 93. Data source of the se lement residential area polygons for Poland: [40]. 94. For more details see Appendix 5.1.9. 95. See [6]. 96. That is, the return period of a real flood event at the location of a particular property. 97. That is, the flood hazard zone in which the particular property is located. 98. See appendix 3.4.3. 99. For the aggregated real loss data, see appendix section 5.1.6. 100. Before the calibration, all the real loss values were recalculated from the historical prices into the current prices, that is, end of the year 2007. See also Sections 1.3.2 and 3.3. 101. For the list of scenarios see table 5.29. 102. Also called as “probabilistic method.â€? 103. Data source of the discharges for the Czech Republic: [27]. 104. For the map of the gauging stations, see ï¬?gure 5.22. 105. See section 3.10.4 106. In total, 5,481 grid cells are generated. 107. Moreover, sensitivity of the correlation structure on the period length was tested. According to results of this test, the correlation matrices calculated for 2-month maximums and quarterly maxi- 76 A World Bank Study mums do not show signiï¬?cant differences from the correlation matrix calculated for the monthly maximum data. 108. Data source of the N-years discharges for the Czech Republic: [27]. 109. Data source of the monthly maximum discharges: [26]. 110. This assumption was tested by Kolmogorov—Smirnov test. 111. Standardized random variable has mean=0 and standard deviation = 1. 112. For kriging method description, see [28]. 113. Note the difference between the flood return periods, which represent the return period of the discharge in the particular locations, and loss return period, which represents return period of the total loss within the whole territory. 114. For the flood scenarios see section 3.6. 115. The basic components of model which contribute to the total error are the following ones: • Loss reported during the historical events and subsequently calculated vulnerability: The data are dif- ï¬?cult to obtain from the national authorities, because either they are not registered or they are based partly on reported values and partly on estimates; for example, the loss reported by the district governments during the 1997 flood in the Czech Republic differs from the estimate of the total loss, according to national Water Research Institute. • Real allocation of the property into the flood hazard zones: Modeled property allocation is based on assumption that it follows either the spatial distribution of the address points in the case of RES type properties or the allocation of industrial class of the CORINE land cover in the case of IND property type. • Regional distribution of the property value balance into the NUTS-2, NUTS-3 or LAU-1 units: The distribution mentioned is available from the national statistical institute only for Poland and partly for the Czech Republic; for the other countries it is calculated by using the simpliï¬?ed ap- proach using compatible input data, such as regional distribution of the ï¬?xed asset formation; the regional distributions of the property value balances as well as formation provided by the national statistical authorities that do not contain the conï¬?dence intervals. • Property value and classiï¬?cation estimated from national accounts: The total property value as well as proportion of the individual property categories provided by the national statistical authorities do not contain any conï¬?dence intervals. 116. The ï¬?nal loss exceedance curve should be interpreted as a point estimate or estimate of the mean value (or the most probable value) of the loss for a given flood return period and scenario. 117. As the assumption of the 250-year flood is a non-realistic extreme event, only the loss structure as a percentage of the total loss is displayed in the outputs, and not the nominal values. 118. For industry branches deï¬?nition and reclassiï¬?cation, see paragraphs 3.2.1 and 3.2.2. 119. For asset category deï¬?nition, see paragraphs 3.2.3 and 3.2.4. 120. For asset institutional sectors deï¬?nition, see paragraphs 3.2.5 and 3.2.6. 121. Regional distribution of Czech Republic property is tabulated in appendix section 5.1.12a) where the regional split into Industry Branches, Asset Categories, and Institutional Sectors is also listed. 122. Regional distribution the Slovak Republic property is tabulated in appendix section 5.1.12b) where regional split to Industry Branches, Asset Categories, and Institutional Sectors is also listed. 123. Regional distribution of the property in Hungary is tabulated in appendix section 5.1.12c) also regional split to Industry Branches, Asset Categories, and Institutional Sectors is also listed. 124. Regional distribution of the property in Poland is tabulated in appendix section 5.1.12d) where regional split to Industry Branches, Asset Categories, and Institutional Sectors is also listed. 125. Property distribution to districts in the Czech Republic is tabulated in appendix section 5.1.13a), particularly in table 5.44 and ï¬?gure 5.58. 126. Property distribution to districts in the Slovak Republic is tabulated in appendix section 5.1.13b), particularly in table 5.45 and ï¬?gure 5.63. 127. Property distribution to districts in the Slovak Republic is tabulated in appendix section 5.1.13c), particularly in table 5.46 and ï¬?gure 5.68. 128. Property distribution to districts in the Slovak Republic is tabulated in appendix section 5.1.13d), particularly in table 5.47 and ï¬?gure 5.73. Financial and Fiscal Instruments for Catastrophe Risk Management 77 129. To obtain information on the household equipment value, which is not registered by the na- tional statistical authorities, data from the insurance industry were collected. 130. Property data sources, reclassiï¬?cation and missing values handling are described in section 3.2. 131. Property redistribution into the flood hazard zones is described in section 3.5. 132. End of the year 2007. 133. For detailed speciï¬?cation of the public, private and household institutional sectors see sections 3.2.5 and 3.2.6. 134. For more detailed property structure within the countries, see appendix section 5.1.5. F or the regional split of property value, see appendix sections 5.1.12 and 5.1.13. 135. For the asset categories deï¬?nition and reclassiï¬?cation see sections 3.2.3 and 3.2.4. 136. For the detailed property distribution into the flood hazard zones, see appendix section 5.1.8. 137. See section 3.8. 138. For the scenario method, see section 3.9. 139. See section 3.6 and appendix section 5.1.9. 140. For the description of the stochastic method, see section 3.10, for the detailed results, see ap- pendix section 5.2.1. 141. For the detailed results, see appendix section 5.2.5. For the methodology, see section 3.11.6. 142. For the return period of 100 years (1 percent occurrence probability), the loss on the public property is approximately EUR 6 billion, for the return period of 250 years (0.4 percent occurrence probability).the loss in the public property is EUR 10 billion. 143. For the differences in the individual countries as well as scenarios, see appendix sections 5.2.5 and 5.3. 144. For more detailed results, see section 4.2.1 and appendix section 5.2.1. 145. See the extrapolated stochastic method description, section 3.10. 146. For more detailed results, see appendix section 5.2.5. 147. For methodology, see section 3.11.3. For the detailed results, see appendix section 5.2.1. 148. Loss intensity = loss in a territory as a percentage of the total property value in the territory; see also section 3.11.4. CHAPTER 3 Modeling for Losses Correlated to Flood Magnitude Triggers T his chapter presents a model for establishing a dependency structure between flood water discharges from various river catchments in the four CEE countries analyzed, and extreme losses. The purpose is to derive mutual correlations between the two, so as to formulate parametric triggers for payments under such flood events. Development of parametric triggers are beneï¬?cial for national authorities since under these, payment execution is simpliï¬?ed and based solely on measured physical events rather than site- by-site assessment of losses. In this regard parametric-based payments are more akin to ï¬?nancial options contracts rather than traditional indemnity based insurance contracts. Data—Countries and River Catchments used in the Analysis It is important to stress that flood loss models are highly complex and the overall result is an aggregate of detailed smaller components. When identifying parametric triggers, the scale of the problem needs to be kept at a reasonable level. Therefore, one needs to accept necessary trade-offs between the level of detail of the flood analysis with the level of complexity of the interdependence structure (that is, the dimensions in the correlation matrix). The V-4 countries were split into several main catchments in which measuring stations selected as representative of a whole catchment were used. This means that the dependency structures are being dealt with on a subregional scale. Catchments were selected to capture the main rivers, and stations were usually at the border of the catch- ment. Some of the Hungarian discharge data sets were not fully satisfactory, and there- fore the last station in the neighboring country where sufficient data was present, was used as a proxy (for Hornad, Raab, and Leitha) (table 3.1). Table 3.1. List of Stations/Countries Station/Country Czech Republic Slovak Republic Poland Hungary Bohumin— Bratislava— Gozdowice— Nagymaros— 1 Odra Dunaj Odra Dunaj Straznice— Sala— Warszawa— Szeged— 2 Morava Vah Visla Tisza Breclav— Zdana— Ostroleka— Felsoezsolca— 3 Dyje Hornad Narew Salo Vranany— Streda nad Bodrogom— Zdana (SK)— 4 Vltava Bodrog Hornad Usti nad Labem— Balassagyarmat— 5 Labe Ipel Feldbach (AT)— 6 Raab Deutsch Brodersdorf (AT)— 7 Leitha Source: Aon Benï¬?eld. 79 80 A World Bank Study Historical Data on River Discharges Maximum monthly discharges were used for the selected stations. Observations were considered since January 1951 (the starting date of measurement for most time series). Some time series have longer periods of observation, in particular bigger rivers like Labe or Dunaj, but for the purpose of measuring simultaneous dependencies it was desirable to have data present within all “cellsâ€? of the observation matrix. Time series were taken from official hydro-meteorological institutions of the studied countries. According to these institutions, data was already adjusted for the effects of human changes in the catchment area (for example, building dams, effect of flood regulation, and so forth). Generation of Losses and Models Used A brief description of loss generation follows. Loss in a given catchment is calculated by the aggregation of losses originating from the tributaries of the main river. Losses are generated per postcodes, where each postcode is divided into a rectangular shaped grid. As the height of the river increases, a greater area is flooded. Based on the calculated depth of water in a location and on vulnerability curves (curves translating depth of wa- ter to losses) losses are calculated accordingly. Models in the Czech Republic, the Slovak Republic, and Poland generate 10,000 events, with a probability of each event equal to 1/10,000. The loss is calculated for each event. The Hungarian model has fewer events with unequal probabilities. The market portfolio (insured properties) was used for loss generation in all four countries. The portfolio was extrapolated up to 100 percent of the insurance market. Currently Aon Benï¬?eld models 86 percent of the Czech insurance market, 90 percent of the Slovak market, 71 percent of the Hungarian market and 93 percent of the Polish market, therefore the extrapolation/grossing up was minimal. The insurance penetration in the modeled countries is quite high (in the Czech Republic around 65 percent, the Slovak Republic around 60 percent, Hungary around 60 percent, PL 40 percent), so the insured losses quite accurately reflect the distribution of properties within the modeled countries. In terms of properties insured, the Czech Republic has together with Poland the highest total insured value (2.5 times larger than Hungary and 4 times larger than the Slovak Republic) but the total insured value in the Czech Republic is more concentrated than in Poland. For the purpose of the analysis, 50,000 trials with event sets as outputs from the above-mentioned models were generated. Losses were kept in original curren- cy after which exchange rates were applied (see tables 3.2 and 3.3, and ï¬?gures 3.1 to 3.4). Table 3.2. Exchange Rates Used CZK SKK HUF PLN per EUR 1 25.8 30.126 275 4.15 Source: Aon Benï¬?eld. Financial and Fiscal Instruments for Catastrophe Risk Management 81 Table 3.3. Total Insured Value (TIV) per Type of Risk and Area (EUR Million) Residential Residential Country Catchment Building Content Commercial Industrial Agro Total CZ Odra 12,408 2,291 7,253 24,239 46,192 CZ Morava 19,166 2,702 7,554 18,566 47,988 CZ Dyje 17,292 2,455 5,871 18,994 44,613 CZ Vltava 54,922 9,333 25,326 60,242 149,822 CZ Labe 44,596 7,112 15,690 49,181 116,578 CZ Total 148,385 23,893 61,694 171,222 N/A 405,194 SK Dunaj 14,140 2,889 9,480 16,919 43,428 SK Vah 11,631 2,193 6,636 9,674 30,134 SK Hornad 6,848 1,533 5,257 3,322 16,959 SK Bodrog 3,800 750 1,710 1,324 7,585 SK Total 36,418 7,365 23,083 31,239 N/A 98,105 HU Ipel 683 220 13 124 — 1,040 HU Dunaj 59,905 17,518 1,394 14,924 177 93,918 HU Salo 2,852 1,052 76 861 0.6 4,843 HU Hornad 460 152 19 40 2.8 674 HU Hornad-Salo 68 22 0.6 8.5 0.4 100 HU Tisza 26,698 8,587 1,020 6,868 271 43,444 HU Leitha 3,222 973 116 604 55 4,969 HU Raab 5,663 1,677 206 1,334 49 8,929 HU Raab-Leitha 544 146 28 567 1.0 1,287 HU Total 100,096 30,348 2,874 25,330 557 159,204 PL Odra 57,860 5,243 60,394 40,358 163,856 PL Wisla 83,211 6,766 64,706 59,309 213,991 PL Narew 13,929 1,104 8,056 3,397 26,486 PL Total 155,000 13,113 133,156 103,064 N/A 404,332 CEE Total 439,899 74,719 220,806 330,854 557 1,066,835 Source: Aon Benï¬?eld. Note: Hornad-Salo and Raab-Leitha are areas at confluences of these rivers where we could not appropri- ately assign TIV to a single river. 82 A World Bank Study Figure 3.1. Catchments, the Czech Republic Source: Aon Benï¬?eld. Figure 3.2. Catchments, the Slovak Republic Source: Aon Benï¬?eld. Correlation Matrices—Methodology Monthly discharge maxima were considered for creating the correlation matrices for the following reasons: One can assume that the sources of correlations are atmospheric events which affect areas larger than a single catchment. Therefore, using yearly dis- charge maxima this would mix events originating at different times of the year. Using monthly data removes, to a large extent, this possible source of bias. It is still possible Financial and Fiscal Instruments for Catastrophe Risk Management 83 Figure 3.3. Catchments, Poland Source: Aon Benï¬?eld. Figure 3.4. Catchments, Hungary Source: Aon Benï¬?eld. 84 A World Bank Study that two stations have large discharges and the discharges originate from two complete- ly different events in a given month; however, this is highly unlikely. River discharges in Central and Eastern Europe show seasonality. This seasonal- ity would increase the correlation of the data set when taking the series of observa- tions without any structuring. The same months of the year would be showing the same relative increases (or decreases) across all stations. Therefore, it is more appropriate to create twelve correlation matrices each representing one month of a year. The annex to this chapter shows a graphical representation of monthly correlations between stations. Correlation coefficients have medians and means close to each other (no high skewness), thus the average is taken. In other words, the correlations are considered as pseudo re- peated realizations of a stochastic variable. It should be pointed out the word ‘correlation’ does not consider a standard Pear- son’s correlation (covariance of two variables divided by their standard errors) which has some unpleasant characteristics. Pearson’s correlation should be considered when having normally distributed data, which is not the case for catastrophic events. There- fore, a Spearman’s rank correlation which is deï¬?ned as Pearson’s correlation of the ranks (or equivalently cumulative distribution functions) was used. Moreover, across catch- ments, the discharges rather than the losses were correlated, since loss correlation alone could yield strange results when, for example, a loss occurred in one country in a given period without a loss in another neighboring country. Subsequently, the correlated dis- charges were translated to losses using one-to-one mapping. Complete observations were used for calculating the correlation matrix. In case of missing data in one of the stations, all observations for that period were deleted. This was later checked with pair wise correlations, that is, where the correlation is calculated from pairs of variables and entered into the correlation matrix (each cell of the correla- tion matrix is thus based on a different data set). In a pair wise correlation matrix, if an observation was missing in one station, the paired observation of the second variable was deleted. As a result, only 3 percent of records in the correlation matrix differed in absolute value between 0.01 and 0.03, and the remaining records differed absolutely by less than 0.01. Therefore, it can be conï¬?rmed that both methods are nearly equivalent. Results of Correlation Matrices The average of the correlation matrices is shown in table 3.4. The pink color indicates strong correlation (>=0.7), pale blue represents mild correlation (between 0.35 and 0.7) and gray stands for weak correlation (<=0.35). Graphical representations are presented in the annex to this chapter. In general, the correlation between non neighboring regions is higher than original- ly expected around zero, and this can be a ributed to atmospheric effects. For example during a dry year, drought is highly likely to occur in the whole CEE region. Also, rainy weather would affect several countries as the frontal system moves across the region. It should be noted that records for Hornad in Hungary were crossed out because the same data for Hornad in the Slovak Republic were used. The entry in the Hungarian part was used only for the sake of completeness. Therefore, there is a black cell between Hornad the Slovak Republic and Hornad Hungary as there is 100 percent correlation there. Table 3.4. Average of Monthly Discharge Rank Correlation Matrices CZ SK HU HU HU HU(SK) HU HU(AT) HU(AT) PL Country / River Odra Morava Dyje Vltava Labe Dunaj Vah Hornad Bodrog Ipel Dunaj Salo Hornad Tisza Leitha Raab Odra Wisla Narew Odra 1.00 0.86 0.68 0.47 0.54 0.48 0.67 0.49 0.42 0.44 0.50 0.47 0.49 0.40 0.35 0.25 0.56 0.57 0.32 Morava 0.86 1.00 0.77 0.55 0.65 0.52 0.74 0.48 0.44 0.43 0.56 0.42 0.48 0.39 0.38 0.27 0.59 0.55 0.35 Financial and Fiscal Instruments for Catastrophe Risk Management CZ Dyje 0.68 0.77 1.00 0.59 0.63 0.46 0.56 0.40 0.32 0.35 0.47 0.40 0.40 0.33 0.35 0.22 0.57 0.47 0.26 Vltava 0.47 0.55 0.59 1.00 0.90 0.65 0.42 0.31 0.29 0.27 0.66 0.38 0.31 0.35 0.36 0.18 0.64 0.39 0.36 Labe 0.54 0.65 0.63 0.90 1.00 0.68 0.50 0.32 0.34 0.28 0.67 0.35 0.32 0.39 0.34 0.13 0.71 0.45 0.43 Dunaj 0.48 0.52 0.46 0.65 0.68 1.00 0.44 0.27 0.29 0.20 0.92 0.24 0.27 0.40 0.51 0.19 0.49 0.40 0.33 Vah 0.67 0.74 0.56 0.42 0.50 0.44 1.00 0.51 0.50 0.41 0.52 0.43 0.51 0.40 0.32 0.24 0.49 0.61 0.32 SK Hornad 0.49 0.48 0.40 0.31 0.32 0.27 0.51 1.00 0.68 0.66 0.36 0.79 1.00 0.49 0.30 0.33 0.37 0.61 0.28 Bodrog 0.42 0.44 0.32 0.29 0.34 0.29 0.50 0.68 1.00 0.44 0.35 0.62 0.68 0.69 0.21 0.22 0.33 0.61 0.32 HU Ipel 0.44 0.43 0.35 0.27 0.28 0.20 0.41 0.66 0.44 1.00 0.30 0.78 0.66 0.38 0.27 0.43 0.24 0.33 0.17 HU Dunaj 0.50 0.56 0.47 0.66 0.67 0.92 0.52 0.36 0.35 0.30 1.00 0.35 0.36 0.43 0.56 0.25 0.52 0.48 0.35 HU Salo 0.47 0.42 0.40 0.38 0.35 0.24 0.43 0.79 0.62 0.78 0.35 1.00 0.79 0.53 0.33 0.42 0.35 0.50 0.20 HU(SK) Hornad 0.49 0.48 0.40 0.31 0.32 0.27 0.51 1.00 0.68 0.66 0.36 0.79 1.00 0.49 0.30 0.33 0.37 0.61 0.28 HU Tisza 0.40 0.39 0.33 0.35 0.39 0.40 0.40 0.49 0.69 0.38 0.43 0.53 0.49 1.00 0.22 0.19 0.40 0.57 0.37 HU(AT) Leitha 0.35 0.38 0.35 0.36 0.34 0.51 0.32 0.30 0.21 0.27 0.56 0.33 0.30 0.22 1.00 0.45 0.26 0.31 0.10 HU(AT) Raab 0.25 0.27 0.22 0.18 0.13 0.19 0.24 0.33 0.22 0.43 0.25 0.42 0.33 0.19 0.45 1.00 0.08 0.18 -0.04 Odra 0.56 0.59 0.57 0.64 0.71 0.49 0.49 0.37 0.33 0.24 0.52 0.35 0.37 0.40 0.26 0.08 1.00 0.55 0.57 PL Wisla 0.57 0.55 0.47 0.39 0.45 0.40 0.61 0.61 0.61 0.33 0.48 0.50 0.61 0.57 0.31 0.18 0.55 1.00 0.46 Narew 0.32 0.35 0.26 0.36 0.43 0.33 0.32 0.28 0.32 0.17 0.35 0.20 0.28 0.37 0.10 -0.04 0.57 0.46 1.00 Source: Aon Benï¬?eld. Note: Some of the river discharge measurements a ributed to Hungary were taken where the river crossed the country's borders. In these cases, the actual measuring stations were located just across the border in the neighboring country (mentioned in parentheses). 85 86 A World Bank Study Dependency structure of discharges—Methodology Copulas were used to tie the random variables together. The only difference between a copula and a multivariate distribution is that the copula deals with percentiles and the multivariate distribution with original values of x. Thus: where X is the original random variable and x is a speciï¬?c value; H is the multivariate distribution and F is the cumulative distribution function for each random variable X. Thus, F(x) is the probability that a random variable X will be smaller or equal to x. C is the notation for a copula and u is a relevant percentile of x (again the probability that the random variable X will be smaller or equal to x), that is u = F(x). To deal with a dependency structure, a t-Copula was used. t-Copula is a method to express a multidimensional dependency in a slightly different way than the ordi- nary correlation matrix does. The “ordinaryâ€? in this sense means that the correlation is based on an assumption of a multivariate normal distribution. This is done using Iman- Conover method which transfers the ranks into percentiles of a normal distribution and then data is drawn together according to the given correlation matrix. The dependence structure is thus implicitly, a so called, Gaussian Copula. However, a Gaussian Copula has a property that yields asymptotically independent tails, that is, the joint probability of two extreme events is 0 which is somehow in con- tradiction with what is expected when dealing with flood events. Unlike the Gaussian Copula, the t-Copula does not have asymptotically independent tails and is similar to the Gaussian Copula (both use the same correlation matrix), (Embrechts 2002). Because of the above reasons, a t-Copula was used. The second parameter of the t-Copula, be- sides the correlation matrix, is the number of degrees of freedom. The more degrees of freedom, the more it resembles the Gaussian Copula. In this case, number of degrees of freedom was again calculated for twelve correla- tion matrices using the maximum likelihood method, having a ï¬?xed correlation matrix. (See Demarta, McNeil 2004). A ï¬?xing correlation matrix was chosen in order to simplify the computational aspects and, as per the mentioned authors’ studies, should lead to similar results. Degrees of freedom (df) for each month’s correlation matrix were calculated, and the median of these df were taken (this was preferred to the average because of the skewness, that is, some months had a very high number of df which, however, differed from much smaller amount of df in terms of maximum likelihood, by less than one percent). The correlation matrix that was used as a parameter was not the rank correlation but the linear correlation which can be obtained using following formula: Results of Dependency Structures of Discharges The resulting number of degrees of freedom, expressed as median of df of individual monthly correlation matrices, was df = 7.5 which was rounded up to df = 8. The linear correlation matrix of the t-Copula was then obtained by transforming Spearman rank correlation to a Pearson’s linear correlation using the formula above (table 3.5). Table 3.5. Linear Correlation Matrix as Input in t-Copula CZ SK HU HU HU HU(SK) HU HU(AT) HU(AT) PL Country / River Odra Morava Dyje Vltava Labe Dunaj Vah Hornad Bodrog Ipel Dunaj Salo Hornad Tisza Leitha Raab Odra Wisla Narew Odra 1.00 0.87 0.70 0.49 0.56 0.50 0.69 0.51 0.44 0.45 0.51 0.49 0.51 0.42 0.37 0.27 0.57 0.59 0.33 Financial and Fiscal Instruments for Catastrophe Risk Management Morava 0.87 1.00 0.78 0.56 0.67 0.54 0.76 0.50 0.46 0.45 0.57 0.44 0.50 0.41 0.40 0.28 0.61 0.57 0.37 CZ Dyje 0.70 0.78 1.00 0.61 0.65 0.48 0.58 0.42 0.33 0.36 0.48 0.42 0.42 0.34 0.36 0.23 0.58 0.48 0.27 Vltava 0.49 0.56 0.61 1.00 0.91 0.66 0.44 0.32 0.30 0.28 0.68 0.39 0.32 0.37 0.37 0.18 0.65 0.40 0.38 Labe 0.56 0.67 0.65 0.91 1.00 0.70 0.52 0.34 0.35 0.29 0.69 0.36 0.34 0.40 0.35 0.14 0.72 0.47 0.44 Dunaj 0.50 0.54 0.48 0.66 0.70 1.00 0.46 0.28 0.30 0.21 0.93 0.25 0.28 0.41 0.53 0.20 0.51 0.42 0.34 Vah 0.69 0.76 0.58 0.44 0.52 0.46 1.00 0.53 0.51 0.43 0.54 0.45 0.53 0.41 0.34 0.25 0.50 0.63 0.34 SK Hornad 0.51 0.50 0.42 0.32 0.34 0.28 0.53 1.00 0.70 0.68 0.38 0.80 1.00 0.51 0.31 0.34 0.38 0.63 0.29 Bodrog 0.44 0.46 0.33 0.30 0.35 0.30 0.51 0.70 1.00 0.46 0.37 0.64 0.70 0.70 0.22 0.23 0.34 0.63 0.34 HU Ipel 0.45 0.45 0.36 0.28 0.29 0.21 0.43 0.68 0.46 1.00 0.31 0.80 0.68 0.40 0.28 0.45 0.25 0.35 0.18 HU Dunaj 0.51 0.57 0.48 0.68 0.69 0.93 0.54 0.38 0.37 0.31 1.00 0.37 0.38 0.45 0.58 0.26 0.53 0.50 0.37 HU Salo 0.49 0.44 0.42 0.39 0.36 0.25 0.45 0.80 0.64 0.80 0.37 1.00 0.80 0.54 0.35 0.43 0.36 0.52 0.21 HU(SK) Hornad 0.51 0.50 0.42 0.32 0.34 0.28 0.53 1.00 0.70 0.68 0.38 0.80 1.00 0.51 0.31 0.34 0.38 0.63 0.29 HU Tisza 0.42 0.41 0.34 0.37 0.40 0.41 0.41 0.51 0.70 0.40 0.45 0.54 0.51 1.00 0.23 0.19 0.41 0.58 0.39 HU(AT) Leitha 0.37 0.40 0.36 0.37 0.35 0.53 0.34 0.31 0.22 0.28 0.58 0.35 0.31 0.23 1.00 0.47 0.28 0.32 0.11 HU(AT) Raab 0.27 0.28 0.23 0.18 0.14 0.20 0.25 0.34 0.23 0.45 0.26 0.43 0.34 0.19 0.47 1.00 0.08 0.18 -0.04 Odra 0.57 0.61 0.58 0.65 0.72 0.51 0.50 0.38 0.34 0.25 0.53 0.36 0.38 0.41 0.28 0.08 1.00 0.57 0.59 PL Wisla 0.59 0.57 0.48 0.40 0.47 0.42 0.63 0.63 0.63 0.35 0.50 0.52 0.63 0.58 0.32 0.18 0.57 1.00 0.48 Narew 0.33 0.37 0.27 0.38 0.44 0.34 0.34 0.29 0.34 0.18 0.37 0.21 0.29 0.39 0.11 -0.04 0.59 0.48 1.00 Source: Aon Benï¬?eld. Note: Some of the river discharge measurements a ributed to Hungary were taken where the river crossed the country's borders. In these cases, the actual measuring stations were located just across the border in the neighboring country (mentioned in parentheses). 87 88 A World Bank Study Marginal distributions—Methodology For the purpose of marginal distributions, maxima per year were also considered. This is because the model operates on a yearly basis, and the model design discharges (those obtained from a model, or estimates from the official institutions) are based as return pe- riods in years. In the case of the Czech Republic and the Slovak Republic the discharges as per the model outputs were used. In the case of Poland, empirical data set up to ap- proximate a 1in50 event (since we only have around 50 years of data) were used. The de- sign discharge estimates by the official institutions were used above this return period. In the case of Hungary, a methodology similar to Poland was applied with the difference that top part (above approximately 1/50) of the distribution was estimated solely from the model, as no such observations were present in the data set. Fi ing to the empirical values was not used and instead, an empirical cumulative distribution function (CDF; ogive) was applied. Relationship between discharges and losses—Methodology for loss generation The approach was to create a one-to-one link between the discharges and the losses. For the Czech Republic and the Slovak Republic, this is already provided in the event loss table. For Poland, the event loss table provides only the water level (stage) of the river at the station. Comonotonicity was assumed, that is, a 100 percent correlation between a percentile of water level and a discharge. In other words, the percentiles of water level provided in the event loss table were used and assumed that these percentiles exactly match the percentiles of the river discharge. For HU, the event loss table with discharges were provided only for events which generated a loss. Therefore, large discharges without any loss were not captured. To overcome such an obstacle an empirical CDF was taken for the observed historical dis- charges and compared with the discharges resulting from the event loss table. Then, a dummy discharge was entered between successive events with a probability set, in a way that each of these two observed events (with a loss and discharge) had a percentile corresponding to the percentile of the empirical CDF. Of course this did not yield an exact match, however the error was optimized using a minimum mean squared error. For example, Event 1 had a discharge of 400 (which corresponds to the 70th percen- tile of the empiric discharges) and event 2 had 410 (corresponding to the 72nd percentile). Event 1 had a probability of 1 percent. Therefore, a dummy event with discharge of 405 had to be entered with a probability of 1 percent so that the discharge of the second event matched the corresponding percentile of the empirical CDF. The only variable randomly generated was the percentile of the discharge which was then linked via the table on event loss, to an appropriate loss amount. Once the discharges per catchment were generated, the losses were assigned to them and aggregated on a country-wide basis for subsequent analysis. Results on Relationship between Losses and Discharges Modeling floods and related losses is highly complex and the resulting loss can be viewed as a sum of many nonidentical distributed random variables (that is, losses to individual insurance policies). Parametric functions transforming discharges to losses were not found to be very accurate; one of the reasons may be that using linear models in a highly nonlinear environment does not lead to the desired results. As can be seen Financial and Fiscal Instruments for Catastrophe Risk Management 89 from the following graphs, there is great variability of ï¬?nal losses even for events with nearly the same discharge level in a given station because one is dealing with a model on a subregional scale. Therefore, the relationship between discharges and losses was taken only from the empirical one, based on the models. It should be remembered that a total loss for each catchment is observed; therefore, the variability in losses arises out of various conditions of the catchments covered by the representative station. Various conditions could be represented by different sizes of the catchment area from individual rivers (for example, having Labe catchment split into 20 areas and Vltava into 100), the regulation and mitigation actions around rivers (water dams, defences), the number of tributaries (streams, springs and small rivers), and other exogenous factors. These factors are, by their nature, unique to each of the catchments. As a result, nearly the same discharges could have been generated by two com- pletely different events. As an example, two tributaries where one is larger and the other smaller can be examined. When two events with the same discharge at the station after confluence of these two rivers occurs, this discharge could have arrived (under normal conditions) from the smaller river, for which it is would be a 1–in-500 event causing mas- sive damages in the area of the smaller river. On the other hand, having a high discharge in a larger river can be nothing more than just an increased state of the water level, which may not causing any damage in the area of the bigger river. Sudden breakpoints in the trend of losses (for example, small losses disappear and an approximately linear positive relationship holds) at a certain level of discharge, re- flects a breach of flood defenses. This is best visible in case of Bratislava, city on Dunaj, that are heavily defended by a system of dams. Figures 3.5 to 3.8 show the relationship of losses within a catchment, related to discharges. 90 A World Bank Study Figure 3.5. The Czech Republic, Losses to Discharges Source: Aon Benï¬?eld. Figure 3.6. The Slovak Republic, Losses to Discharges Source: Aon Benï¬?eld. Financial and Fiscal Instruments for Catastrophe Risk Management 91 Figure 3.7. Hungary, Losses to Discharges Source: Aon Benï¬?eld. Figure 3.8. Poland, Losses to Discharges Source: Aon Benï¬?eld. 92 A World Bank Study Outputs per Gross Loss Return periods for each country were calculated as a simple empirical CDF of the simu- lated losses. Maxima per year were simulated; thus the distribution will be equal to the Occurrence Exceedance Probability (OEP) curve. Following that, the relative contribu- tion of each country within the CEE loss was calculated in order to provide an answer to question as to which catchments were the key contributors to the CEE losses. The correlation of floods between losses is shown in table 3.6. Table 3.6. Correlation of All Gross Losses between Countries CZ SK HU PL CZ 1.00 0.39 0.42 0.41 SK 0.39 1.00 0.59 0.30 HU 0.42 0.59 1.00 0.30 PL 0.41 0.30 0.30 1.00 Source: Aon Benï¬?eld. Outputs under Assumed Parametric Reinsurance Contracts The features under a parametric contract for this analysis, is considered to work as fol- lows: if a loss reaches or exceeds a pre-speciï¬?ed threshold, the contract recovers in full for that loss amount. However, if the loss is below the threshold, nothing is recovered. Scenarios which could be classiï¬?ed under two approaches were modeled: â–  The same loss thresholds for each country were used (thresholds being deï¬?ned in EUR billions in increments of EUR: 0.2 billion, 0.5 billion, 1.0 billion, 1.5 billion, 2.0 billion, 2.5 billion, 3.0 billion, and 4.0 billion), and also applied to the overall group CEE loss. â–  Thresholds were also set probabilistically at 1 in 10 year losses (10 percent prob- ability) and were used for each country as well. For each of these scenarios probabilities of events under individual country se ings were calculated (where a loss must exceed a given threshold in a country in order to be recovered) and under a pooled se ing (where the overall loss exceeds the threshold after which one calculates the probability that a country contributes to such loss) were calcu- lated. Table 3.7, showing the probabilities under pooled se ings with derived parametric triggers, was then calculated. For example, in the Czech Republic there may be a loss of 550 million, in the Slo- vak Republic 0, in Hungary 230, and in Poland 150, and the loss threshold is 500 mil- lion and above. On an individual basis, the Czech Republic loss would be recovered in full, thus the CEE unrecovered (retained) loss under the individual se ing would be 380 = 550 + 0 + 230 + 150 − 550. Under the pooled se ing the losses sum to 930 million which is above the CEE threshold of 500 million; thus, the loss in full would be recovered under pooled se ing. The variance and mean of retained and ceded losses were also measured. These statistics were then calculated for gross losses, losses under individual se ings, losses under pooled se ings and lastly under pooled se ings with parametric triggers, which are derived later in the paper. Table 3.7. Rank Correlation Matrix of Discharges when Overall CEE Loss Exceeds EUR 200 Million CZ SK HU HU HU HU(SK) HU HU(AT) HU(AT) PL Country / River Odra Morava Dyje Vltava Labe Dunaj Vah Hornad Bodrog Ipel Dunaj Salo Hornad Tisza Leitha Raab Odra Wisla Narew Odra 1.00 0.75 0.47 0.09 0.17 0.22 0.51 0.28 0.22 0.28 0.23 0.26 0.28 0.19 0.18 0.16 0.28 0.37 0.13 Morava 0.75 1.00 0.58 0.17 0.31 0.26 0.61 0.22 0.22 0.26 0.29 0.17 0.22 0.14 0.21 0.18 0.31 0.31 0.16 Financial and Fiscal Instruments for Catastrophe Risk Management CZ Dyje 0.47 0.58 1.00 0.30 0.33 0.20 0.34 0.12 0.06 0.15 0.19 0.16 0.12 0.08 0.16 0.11 0.30 0.19 0.05 Vltava 0.09 0.17 0.30 1.00 0.84 0.50 0.09 -0.04 -0.03 0.01 0.50 0.09 -0.04 0.09 0.18 0.02 0.45 0.06 0.20 Labe 0.17 0.31 0.33 0.84 1.00 0.55 0.19 -0.05 0.03 0.00 0.52 0.03 -0.05 0.14 0.15 -0.04 0.54 0.14 0.28 Dunaj 0.22 0.26 0.20 0.50 0.55 1.00 0.20 0.00 0.06 0.00 0.89 0.00 0.00 0.22 0.40 0.07 0.31 0.17 0.19 Vah 0.51 0.61 0.34 0.09 0.19 0.20 1.00 0.34 0.35 0.28 0.30 0.25 0.34 0.22 0.17 0.16 0.24 0.46 0.18 SK Hornad 0.28 0.22 0.12 -0.04 -0.05 0.00 0.34 1.00 0.62 0.62 0.12 0.75 1.00 0.38 0.17 0.29 0.09 0.48 0.12 Bodrog 0.22 0.22 0.06 -0.03 0.03 0.06 0.35 0.62 1.00 0.35 0.13 0.54 0.62 0.62 0.08 0.17 0.09 0.51 0.21 HU Ipel 0.28 0.26 0.15 0.01 0.00 0.00 0.28 0.62 0.35 1.00 0.11 0.75 0.62 0.27 0.16 0.40 0.02 0.20 0.03 HU Dunaj 0.23 0.29 0.19 0.50 0.52 0.89 0.30 0.12 0.13 0.11 1.00 0.13 0.12 0.26 0.46 0.14 0.33 0.26 0.21 HU Salo 0.26 0.17 0.16 0.09 0.03 0.00 0.25 0.75 0.54 0.75 0.13 1.00 0.75 0.41 0.21 0.39 0.09 0.35 0.05 HU(SK) Hornad 0.28 0.22 0.12 -0.04 -0.05 0.00 0.34 1.00 0.62 0.62 0.12 0.75 1.00 0.38 0.17 0.29 0.09 0.48 0.12 HU Tisza 0.19 0.14 0.08 0.09 0.14 0.22 0.22 0.38 0.62 0.27 0.26 0.41 0.38 1.00 0.09 0.10 0.19 0.45 0.27 HU(AT) Leitha 0.18 0.21 0.16 0.18 0.15 0.40 0.17 0.17 0.08 0.16 0.46 0.21 0.17 0.09 1.00 0.40 0.09 0.16 -0.03 HU(AT) Raab 0.16 0.18 0.11 0.02 -0.04 0.07 0.16 0.29 0.17 0.40 0.14 0.39 0.29 0.10 0.40 1.00 -0.07 0.08 -0.13 Odra 0.28 0.31 0.30 0.45 0.54 0.31 0.24 0.09 0.09 0.02 0.33 0.09 0.09 0.19 0.09 -0.07 1.00 0.34 0.50 PL Wisla 0.37 0.31 0.19 0.06 0.14 0.17 0.46 0.48 0.51 0.20 0.26 0.35 0.48 0.45 0.16 0.08 0.34 1.00 0.35 Narew 0.13 0.16 0.05 0.20 0.28 0.19 0.18 0.12 0.21 0.03 0.21 0.05 0.12 0.27 -0.03 -0.13 0.50 0.35 1.00 Source: Aon Benï¬?eld. Note: Some of the river discharge measurements a ributed to Hungary were taken where the river crossed the country's borders. In these cases, the actual measuring stations were located just across the border in the neighboring country (mentioned in parentheses). 93 94 A World Bank Study In addition to the previous approaches, in cases with the same threshold, the rank correlation of losses between countries was calculated when the overall loss exceeded the CEE group threshold. This is the only way a correlation can be calculated as in such a case equal sample sizes are present. The correlation of losses exceeding thresholds on a per country basis is not possible as this involves matching different return periods, thus pair wise samples cannot be generated. Scenario with threshold of EUR 500 million Tables 3.8, 3.9, and 3.10 show the results of se ing the loss level at EUR 500 million. Later in the report, this scenario is linked to flood discharge levels in order to establish the physical parameters associated with this level of loss per country. Table 3.8. Selected Options and Probabilities, Given a Loss Exceeding the EUR 500 million Threshold on an Individual Basis; and Given That the Overall CEE Loss Exceeds EUR 500 Million Pooled—optimal Individual Setting Pooled Setting trigger solution Prob. RTP Prob. RTP Prob. RTP CZ SK HU PL (%) RTP merged (%) RTP merged (%) RTP merged 0 0 0 0 90.50 87.73 89.33 0 0 0 1 0.35 287 0 0 0 0 1 0 0.17 581 0 0 0 0 1 1 0.02 5,000 0 0 0 1 0 0 0.09 1,087 149 0 0 0 1 0 1 0.01 7,143 0 0 0 1 1 0 0.02 5,000 0 0 0 1 1 1 0.00 25,000 0 0 1 0 0 0 7.67 13 13 0.12 820 0.16 633 1 0 0 1 0.41 243 0.15 658 0.13 781 1 0 1 0 0.14 725 0.39 256 0.42 240 66 71 1 0 1 1 0.01 12,500 0.49 202 0.40 253 1 1 0 0 0.34 292 87 0.15 658 0.15 649 1 1 0 1 0.15 685 0.20 510 0.16 641 1 1 1 0 0.05 1,923 3.16 32 32 2.86 35 35 1 1 1 1 0.06 1,786 7.60 13 13 6.41 16 16 12.27% 11.11% 11.64% 8.44% Pooled Setting—probability of recovering in each country Source: Aon Benï¬?eld. Note: 1 represents an event happening and 0 an event not happening; thus 1,0,0,0 means that only in CZ a loss was greater than the threshold (Individual Se ing) or CZ loss was the only contributor to a CEE loss exceeding the threshold (Pooled Se ing). Financial and Fiscal Instruments for Catastrophe Risk Management 95 Table 3.9. Rank Correlation of Losses between Countries—CEE Loss Exceeds EUR 500 Million CZ SK HU PL CZ 1.00 0.05 -0.07 0.09 SK 0.05 1.00 0.55 0.19 HU -0.07 0.55 1.00 0.14 PL 0.09 0.19 0.14 1.00 Source: Aon Benï¬?eld. Table 3.10. Ceded and Retained Losses under Pooled and Individual Setting Retained Losses Ceded Losses StDev of Mean Coefï¬?cient of StDev of Mean Coefï¬?cient of retained retained variation ceded ceded variation CEE Gross Losses 465,482,171 231,145,920 2.01 — — — CEE Individual 155,395,307 123,188,011 1.26 414,886,059 107,957,909 3.84 CEE Pooled 112,060,442 86,667,163 1.29 478,706,316 144,478,757 3.31 CEE Pooled—optimal trigger solution 149,139,341 101,647,125 1.47 469,848,907 129,498,794 3.63 Source: Aon Benï¬?eld. Note: — = not applicable. Scenario with Threshold of EUR 1.5 billion A more dramatic scenario, for the purposes of displaying exceedance probabilities, is run for losses of EUR 1.5 billion and above (tables 3.11–3.13). In this case, the trigger probabilities are lower and thus any pricing would be reduced given the lower likeli- hood of triggering or “a achment.â€? Results The previous tables showed that in all scenarios it is in general convenient for all coun- tries to be a part of a pool as it greatly increases their chances for a recovery. There is clear shift from the most probable event of only one country recovering under indi- vidual se ings, to recovering in all countries. Moreover, the tables show the fact, already visible. Under all scenarios the pooling effect greatly reduces the mean and volatility of retained losses. The pooled se ing under optimized triggers always performs slightly worse than in an ideal option. This reflects that the efficiency of the optimized triggers is not 100 percent, analogous to deciding whether to accept Type I or Type II errors. Trigger Methodology In the process of deï¬?ning the triggers two approaches were used. The ï¬?rst of them is relevant when looking at the losses on a CEE group basis. The other is similar; however, it looks at each country separately. Both used the logical OR between discharges in vari- ous catchments, and the trigger was deï¬?ned as a threshold that was exceeded based on aggregate annual country catchment triggering. Thresholds were translated from per- centiles in each catchment. In other words, the algorithm used only percentiles which were then described in terms of discharges in cubic meters per second (m3s-1). 96 A World Bank Study Table 3.11. Selected Options and Probabilities, Given Loss Exceeding EUR 1.5 Billion Threshold on an Individual Basis; and Given that the Overall CEE Loss Exceeds EUR 1.5 Billion Pooled—optimal Individual Setting Pooled Setting trigger solution Prob. RTP Prob. RTP Prob. RTP CZ SK HU PL (%) RTP merged (%) RTP merged (%) RTP merged 0 0 0 0 98.50 97.31 97.53 0 0 0 1 0.01 16,667 0 0 0 0 1 0 0.01 12,500 0 0 0 0 1 1 0 0 0 0 1 0 0 0.01 16,667 5,000 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1.44 69 69 0.004 25,000 0.01 10,000 1 0 0 1 0.01 7,143 0.02 6,250 0.02 5,556 1 0 1 0 0.01 16,667 0.02 4,545 0.05 2,083 658 424 1 0 1 1 0 0.07 1,351 0.10 1,000 1 1 0 0 0.02 6,250 2,381 0.01 12,500 0.02 5,000 1 1 0 1 0.00 25,000 0.03 3,571 0.04 2,500 1 1 1 0 0.00 50,000 0.24 424 424 0.31 325 325 1 1 1 1 0 2.30 43 43 1.92 52 52 2.69% 2.58% 2.64% 2.42% Pooled Setting - probability of recovering in each country Source: Aon Benï¬?eld. Note: 1 represents the event occurring and 0 for the event not occurring; thus 1,0,0,0 means that only in CZ a loss was greater than the threshold (Individual Se ing) or CZ loss was the only contributor to a CEE loss exceeding the threshold (Pooled Se ing). Table 3.12. Rank Correlation of Losses between Countries—CEE Loss Exceeds EUR 1.5 Billion CZ SK HU PL CZ 1.00 -0.10 -0.15 -0.10 SK -0.10 1.00 0.45 0.23 HU -0.15 0.45 1.00 0.16 PL -0.10 0.23 0.16 1.00 Source: Aon Benï¬?eld. Table 3.13. Ceded and Retained Losses under Pooled and Individual Settings Retained Losses Ceded Losses StDev of Mean Coefï¬?cient of StDev of Mean Coefï¬?cient of Retained Retained Variation Ceded Ceded Variation CEE Gross Losses 465,482,171 231,145,920 2.01 — — — CEE Individual 337,138,977 199,908,462 1.69 266,283,926 31,237,458 8.52 CEE Pooled 259,297,741 167,514,465 1.55 413,227,706 63,631,455 6.49 CEE Pooled—optimal trigger solution 290,663,617 175,497,179 1.66 389,513,924 55,648,741 7.00 Source: Aon Benï¬?eld. Note: — = not applicable. Financial and Fiscal Instruments for Catastrophe Risk Management 97 The Logical OR was necessary since having a trigger in one catchment for the whole CEE group would not allow capture of the majority of signiï¬?cant losses. â–  CEE approach: As there are 19 catchments, the parametric trigger will look like a polygon. Thus one requires an optimization criterion. The weight was set to 1 for correctly triggered losses and -1 for incorrectly triggered ones (Type I). Under this conï¬?guration a weight of -1 was assigned to Type II events (not triggered but should have been triggered) (ï¬?gure 3.9). This, in the insurance context refers to the “basis riskâ€? described in chapter 1. If a loss moves from a correctly triggered to a Type II, it results in decreasing the optimization criterion (table 3.8). â–  It was decided not to use other layouts as se ing 1 for correctly triggered losses and -1 for Type II events as this would ultimately lead to triggers which would start at minimum discharges (as they would tend to capture all the losses above the CEE threshold, even those with very small discharges in all catchments). â–  Also, it was decided not to set any weight to correctly non triggering events (bot- tom left) as a number of these events would outweigh those correctly triggered. There is also the possibility of assigning a broader weight set than just {-1, 1}. One could, for example, set weights according to the size of the loss or relative impor- tance of each participating country. â–  Individual country treatment: the threshold was set for each country separately and then the optimization was run in the same manner as in the CEE approach. Figures 3.10 to 3.13 represent the overall CEE aggregate losses given discharges in each catchment. Figures 3.14 to 3.17 capture losses in a particular country (for example, for Czech catchments these show only the total Czech loss) given discharges in each catchment. Figure 3.9. Type I and Type II Errors Source: Aon Benï¬?eld. 98 A World Bank Study Figure 3.10. CEE Loss from Czech Republic River Catchments Source: Aon Benï¬?eld. Figure 3.11. CEE Loss from Slovak River Catchments Source: Aon Benï¬?eld. Financial and Fiscal Instruments for Catastrophe Risk Management 99 Figure 3.12. CEE Loss from Hungarian River Catchments Source: Aon Benï¬?eld. Figure 3.13. CEE Loss from Polish River Catchments Source: Aon Benï¬?eld. 100 A World Bank Study Figure 3.14. Czech Republic—Per Country Loss Source: Aon Benï¬?eld. Note: Horizontal line corresponds to 1 in 10 RTP. Figure 3.15. Slovak Republic—Per Country Loss Source: Aon Benï¬?eld. Note: Horizontal line corresponds to 1 in 10 RTP. Financial and Fiscal Instruments for Catastrophe Risk Management 101 Figure 3.16. Hungary—Per Country Loss Source: Aon Benï¬?eld. Note: Horizontal line corresponds to 1 in 10 RTP. Figure 3.17. Poland—Per Country Loss Source: Aon Benï¬?eld. Note: Horizontal line corresponds to 1 in 10 RTP. 102 A World Bank Study Performance of optimized criteria Some important issues to be considered are the following: â–  The optimization algorithm generated a high number of local maxima. Therefore, one had to rerun it with various starting values in order to obtain the optimal value. Even then, when manually changing the values, it was still possible to ï¬?nd be er results. Therefore, this manual complement to the algorithm was per- formed, and the results are believed to be as close to optimal as possible. â–  It is possible to ï¬?nd hyper-planes deï¬?ning the event set with the same optimized criteria but with completely different triggers using linear programming. Un- fortunately, highly specialized software able to cope with millions of variables would be required. With software equipment available for the analysis, the trig- gers had to be manually adjusted in a way that kept the best obtained value of the criterion and return periods of triggers. For example, two options for Morava (CZ) were available for CEE thresholds of 200 million, 500 million, 1,000 million, and the trigger was as a return period of either 100, 1,000, 350 years or 100, 650, 1,000 years. In such a case, the la er option was preferred because the return pe- riods formed an increasing function of the CEE threshold. â–  One can always reach 100 percent of correctly triggered events; however, this is at the cost of signiï¬?cantly increasing the number of incorrectly triggered events. Setting Threshold Flood Triggers per Country for Parametric Contract Design This section describes the result of the model with different conditions related to the val- uation of assets and property. The previous model discussed earlier, was run on insured market data (and was based on existing insurance policies, thus allowing a more exact quantiï¬?cation of exposed values). Since Chapter 2 discussed the exposure model using all property and infrastructure, including insured plus uninsured private sector assets, this section applies those data values to the parametric trigger correlation analysis. Thus the property values in each region were modiï¬?ed accordingly to observe the behavior of losses and the flood triggering levels. The model was run witvh proportionally scaled value exposures and the adjusted losses thus changed proportionally. Table 3.14. Model for Exposure Using Total Insured and Uninsured Assets Value exposed Loss scale factor applied to every single loss, in EUR ‘000 compared to the results in the earlier section Czech Republic 241,245 60% Slovak Republic 120,379 123% Hungary 171,423 108% Poland 533,789 132% Total 1,066,835 — Source: Aon Benï¬?eld. Financial and Fiscal Instruments for Catastrophe Risk Management 103 Applying the above broader data, the model was run against the EUR 200 million and EUR 500 million levels of losses to determine the ‘trigger’ flood magnitudes (ex- pressed as measured water flow in cubic meters per second). It should be qualiï¬?ed that the model yet needs be er speciï¬?cation to include all public sector asset exposures in- cluding their locational and vulnerability functions, thus the ï¬?gures below are, while useful, still illustrative. Additional flood trigger modeling would be required, depend- ing on each government’s preferences for assets and locations to be most protected. As well, the ï¬?gures below imply event probabilities which provide some bias to- ward quicker triggering of losses in the Czech Republic. This probably occurs because the model was initially based on insured data and it appears that under such data a larger proportion of Czech private assets in the sample were insured against catastrophic flood risks. This therefore, appears to give the Czech Republic smaller (more frequent) return periods or a higher probability of triggering. However, this can be corrected through the application of a broader public and private sector exposure asset model which would apply values that are not necessarily insured against catastrophes (for example, some public sector infrastructure and some private household or business uninsured proper- ties). This would result in less of a bias toward loss triggering for those countries with the most private insurance against catastrophes speciï¬?cally. Table 3.15. List of Optimized Triggers as Return Period and Relevant Flow Discharge (in Cubic Meters per Second), for Loss Threshold of EUR 200 Million Czech Slovak Return Period/ Republic Republic Hungary Poland Discharge (200 mil) (200 mil) (200 mil) (200 mil) 49 833 — 100 Odra-CZ Dunaj-SK Ipel-HU Odra-PL 1,571 13,834 — 3,510 14 93 — 53 Morava-CZ Vah-SK Dunaj-HU Wisla-PL 565 1,964 — 6,284 40 125 333 1,000 Dyje-CZ Hornad-SK Salo-HU Narew-PL 632 1,038 534 1,670 16 833 143 Vltava-CZ Bodrog-SK Hornad-HU 2,452 1,877 635 19 233 Labe-CZ Tisza-HU 3,258 4,440 91 Leitha-HU 212 — Raab-HU — Source: Aon Benï¬?eld. 104 A World Bank Study Table 3.16. List of Optimized Triggers as Return Period and Relevant Discharge (in Cubic Meters per Second) for Loss Thresholds of EUR 500 Million Czech Slovak Return Period/ Republic Republic Hungary Poland Discharge (200 mil) (200 mil) (200 mil) (200 mil) 500 1,000 — 476 Odra-CZ Dunaj-SK Ipel-HU Odra-PL 2,410 14,014 — 4,344 125 127 — 263 Morava-CZ Vah-SK Dunaj-HU Wisla-PL 785 2,037 — 8,486 333 500 1,429 10,000 Dyje-CZ Hornad-SK Salo-HU Narew-PL 918 1,278 758 1,805 33 2,000 — Vltava-CZ Bodrog-SK Hornad-HU 2,997 2,032 — 39 5,000 Labe-CZ Tisza-HU 3,751 4,810 769 Leitha-HU 276 — Raab-HU — Source: Aon Benï¬?eld. Conclusion Losses on a country basis showed a much smaller correlation among each other, a posi- tive sign for risk pooling prospects. Only the Slovak Republic and Hungary showed a moderate correlation of losses. This is likely a result that all the Slovak Republic rivers direct to Hungary. Cross correlation for other countries were small (below a 0.35 correla- tion coefficient). Even though the rank correlation between countries is small, it may be worth con- sidering having a pooled se ing (where the sum of various country losses together, trig- ger the payment threshold, where compensation is then shared proportionately) versus individual countries triggers that would make individual country payments solely in terms of individual thresholds. A pooled se ing decreases the overall average and stan- dard deviation of retained losses and countries with small losses still beneï¬?t. In terms of relative efficiency (correctly triggered vs. incorrectly triggered events) the model varies across countries and catchments. For the purposes of designing parametric contracts with pooled se ings, additional work would be required to arrive at a high ratio of correctly triggered events as well as consistency in such ratios across the coun- tries. The percentage of correctly triggered events (out of all which exceed the thresh- old) is around 75 percent for the EUR 200 and 500 million thresholds. As the thresholds increase, the efficiency decreases to less than 50 percent for example, for EUR 4 billion. Financial and Fiscal Instruments for Catastrophe Risk Management 105 The reliability of the presented results, when applied to a decision making pro- cess on whether to buy alternative reinsurance covering uninsured public properties, depends on the similarity in behavior between such public properties and the private property data used in this chapter. Such behavior includes similarity in terms of the vul- nerability factors and size of the aggregate values at risk of loss. As the data in chapter 2 covered public properties, additional correlation tests (for the purposes of se ing trig- gers) would need to be tested to validate these triggers. Annex: Monthly Correlations between Catchments This annex illustrates matrices of rank correlation between discharges at a particular river catchment station, versus the rest of the stations. These are shown in ï¬?gures 3A.1– 3A.4. The Y axis represents the coefficient of correlation. The ï¬?gure M within the charts means median of the monthly values, and the ï¬?gure A is the simple average of the monthly correlation values. The subject station always has correlation one with itself. The numerical ï¬?gures within the graphs represent each month of the year. A smaller spread shows the same seasonality pa erns, for example, Vltava will have almost the same behaviour as Labe since Vltava contributes with a bigger inflow than Labe at the confluence at Melnik (in the Czech Republic). 106 A World Bank Study Figure 3A.1. Czech Republic Correlation of Odra.CZ with others Correlation of Morava.CZ with others 1.0 1.0 2A 1M 5 9 8 10 6 3 7 12 11 4 2A 1M 5 6 8 9 10 3 7 11 12 4 11 1 11 1 8 10 9 8 10 9 12 A 7M 12 A 7M 3 1 2 3 5 2 3 5 2 2 1 6 6 7 6 11 0.8 0.8 4 7 4 1 8M 2 4 10 4A 12 10 7 9 8 1 1 11 10 2 2 A 4M 4 10 12 12 9 10 12 3 10 2 1M 7 10 8 11 1 12 7 3 10 10 12 1 8 5 9 9 1 A 1 7 2 9 11 2 3 10 10 9 7 7 3 9 7 6 9 12 4M 2A 12 4 4 8 7 5 A M 10 3 6 8 8 3 11 8 4 7 12 8 9 12 7 12 9 9 11 10 6 4 7 8 8 3 8 12 1 10 1 9 8M 7M 0.6 0.6 10 8 10 8 9 7 7 2 11 71 1A 1 10 8M 3M 4A 10 8M 12 4 3 10 3 M 7 4 3 10 2 6 4 5 3 3 6 7 11 2 10 8 9 1 9 A 12 8 7 9 2 2 7 11 7 8 3 2 6A 11 M A 2 1 8A 4 9 3 3 A 10 11 8 3M 3 9 2 9 10 7 A 5M 8 2 5M 10 8 2M M 3 2 3 1 11 4 2 1 6 9 2 9 6 6 3 7 3 6 5 1M 5A A M 1M 11 12 5A 3M 1 1 4 12 12 2 2 4 11 2 1M 8 2 4 12 A 6 8 1 4 9A 5M A 11 12 3 11 2A 3M 11 10 10 11 1M A 11 2 2 9 10 9M 9 8 7 9 A 3 1 A 3 1 12 2 6 11 A M 10 1 9 6 5 5 4M 11 10 A 11 8 2 1 7 9 9 1 A M A 0.4 0.4 9 4 4 5 9 6 11 4 6 A 5 M 4 4 8 8 4M 9 11 4 6 8 6 5 1 10 4A 1 6 11 5 11 5 7 A 11 11 A 2 12 3 5 4A 5 5 5 6 5 4 12 8 M 3 10 9M 5 5 4 11 2 10 11 6 4 10 4A 7 4 5 5 1 5 7 7 8 12 5 12 A 2 7 5M 8 5 12 6 12 6 3 7 3 0.2 0.2 12 10 9 7 6 12 6 12 5 6 11 6 6 3 6 12 11 12 5 5 6 6 5 6 11 7 5 12 11 6 12 6 3 0.0 0.0 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL (Figure continues on next page) Figure 3A.1 (continued) Correlation of Dyje.CZ with others Correlation of Vltava.CZ with others 1.0 1.0 2A 1M 5 8 9 6 10 3 7 11 12 4 1M 2A 5 6 8 9 10 3 7 12 11 4 2 6 3M 5 4 10 7 1A 12 8 3 9 Financial and Fiscal Instruments for Catastrophe Risk Management 2 11 7 6 0.8 0.8 1 8M 3 2 10 4A 2 8 3 2 1 1 10 8 12 12 4 8 11 1M 7 12 2 12 12 7 1 3 10 11 3 6 7 7 1 3 4 3 12 7 A 9 2 10 12 9M 2 3 2 7 4 M 6 9 5 6 1 9 3 2 8 7 12 10 9 12 6 1 1M 12A 8 4 3 1 8 8 1 12 4A 2M 10 8 5A 11 7M 10 8A 7 10 7 2 8 7M 10 10 11 10 8 4 0.6 0.6 A 7 9 7 8 1 7 8 A 1 8 4 10 10 3 1 10 8 11 5 9 A 1 1M A 9 10 9 9 12 8 6 7 8 3 9 1 8 3M 7 3 2 7 M A 8 5 3 2 6 4 M 12 8 3 11 7 4 2 9 8 1 3 1 2 9 4 2 10 8 6 8 5 2 11 9 9 6 M 6 6 4 2 11 5 9M 9 8 7 3 1 1 11 A 3 8 2A 3 3A 10 2A 9 3M 5 1 10 3 10 2 2 9 9 11 11 6 4M 2 4 6 4 4M 8 4 9 11 5 11 9A 5 2 1M 5 8 A 9M 12 4 5A 3 8 7 9 12 0.4 0.4 12 6 12 A 2 M 2 12 5 10 11 2 1 9 11 5 7 6 A 3 12 4 7M 4M 9 A 12 12 12 4 4 11 10 7 3 2 M 10 A 7MA 10 105A 9 5 1M 8A 5 A 10 7 12 1 5 7 12 12 1M 7 11 3 6 5 1 A 1 A 11 10 8 4 6 6 9 8 5 M 7 12 M 7 5 A 3 1 2 3 A 10 9 9 2 1 10 2 6 9 7 1 5 12 8M 6M 11 A M 6 6M 7 11 11 6 2 7 12 5 6 1 M A 1 9 9 8 11 4 11 A 4 10 4 4 11 10 4 11 7 11 2 12 12 1M 11 11 6 4 6 11 5 11 A 5 11 2 9 8 2 10 5 3 4 0.2 0.2 6 6 4 5 5 5 5 3M 4 5 4 6 12 9 A 6 10 6 3 6 5 5 10 10 6 10 11 4 12 11 12 3 5 5 12 3 4 12 0.0 0.0 6 5 7 6 3 11 11 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL (Figure continues on next page) 107 108 Figure 3A.1 (continued) A World Bank Study Correlation of Labe.CZ with others 1.0 2A 1M 5 10 8 9 6 3 7 11 12 4 2 6 3M 5 4 10 7 1A 12 8 1 9 3 1 11 0.8 12 2 2 11 10 1 1 7 2 3 1 10 8 10 12 3 10 4 4 12 10 12 2 6 10 12 7 4M 3 8 1A 9M 2 5 1 9 9M 9A 7 8M A 4 1 A 11 8 M 8A 2 8 9 12 8 8 7 9 6 3 0.6 4 11 1 8 11 11 6 7 8 3 2 2 8 9 11 12 3 7 A 3 11 11 10M 5 8 1 2M 6 A 9 6 4 10 3 5 6 7 10 2 7 4 8 7 6 2 1 M 8 11 7 1 9 7 11 A 12 12 9 5 9 4M 7 9 5M 5 5 9 10 12 4A 0.4 4 7 11 9 4 2 A 3 2 7 9 5 M 3A 1A A 3 12 9A 6 5 4 4M 3 10 4M 10 A M 5 5 6 1 M 2M 1 11 10 2 4 11 11 5 A 7 3 7 5 5 12 3 1 5 6 9 2 8 2 8 6 10 6 0.2 10 5 5 4 1 6 6 6M 10 10 12 8A 11 12 6 12 3 11 0.0 7 3 4 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Source: Aon Benï¬?eld. Figure 3A.2. The Slovak Republic Correlation of Dunaj.SK with others Correlation of Vah.SK with others 1.0 1.0 1M 2A 5 3 8 6 10 9 7 4 12 11 2A 1M 5 6 3 10 9 8 7 12 4 11 1 10 9 8 5M 7 11 2A 4 12 3 6 3 Financial and Fiscal Instruments for Catastrophe Risk Management 2 1 11 0.8 0.8 3 2 7 2 7 9 1 1 1 11 A 4M 1 8 8 10 10 7 7 1 12 127 3 8 10 12 8 7 2 7 3 7 4M 9A 1 5 2 12 9A 2M 3 2 8 1M 11 4 6 4 1 4 4 10 10 7 3 4A 8 12 1 8 9 4 10 8 8 4 12 7 6 2 9 10 9 10 11 1 9 4 9 1 2 11 11 2 A M 0.6 0.6 7 1 3 6 8M 9 12 10 10 8 9 7 10 10 7 9 9 3 1 9 9 12 4 5 3 A 12 12 12 3 11 2 11 12 8 7 2 1 8 8 5 4 9 8M 7 2 5 12 7 M A 2M 8 8 7 8 2 1 1 12 11 3 3M 2 11 10M 8 7A 3 8 4 10 5M A 8M 11 12 9 4 8 3A 3 3A 11 11 1 2 3 6 6 9 7 9 11 2 A 5 5 11 A M 8A 9 M 1 M 1M 10 5 6 4 4 4 8 5 3 11 1 10 M 11 1 10 7 2 A 10 A A 5 3 11 9 9 8 3 11 2 3 1 9 2M 2M 7 9 7 7 3 4 11 1 7 9 10 10 12 6 5 A 10 A 11 2 9M 8 8 4 8 9 9A 9 6 2M A 9 M 10 7 10 7M 6 10 8 4 4 3 1 4 2 4A 10 5 12 0.4 11 0.4 4 4 6 12 6 9 9 7A 8 2 2 M 7 6 6 5 A 5 6 8A 8 6 11 10 7 5 6 12 10 2 10 5 5 4 11 6 10 6 10 5 A 5 4 2 M 2 5 6 6 6 12 A 1 5 A 4 M 11 3 6 9M 12 4 4M 7 9 4 11 11 11 2A 3 11 6 5 1 6 12 8 6 9M 5 A M 1 A M 11 11 10 1 6 10 6 3A 1 2M 10 11 2 6 5 3 7 4 7 1 1 7A 10 12 3 M 6 12 3 6 12 12 11 M 0.2 0.2 5 6 1 A 10 9A 5 6 5 7 5 4 4 8 3 3 12 12 5 12 5 5 3 2 8 2 12 1 5 5 12 5 5 11 6 11 0.0 0.0 12 12 7 6 1 4 6 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL (Figure continues on next page) 109 110 A World Bank Study Figure 3A.2 (continued) Correlation of Hornad.SK with others Correlation of Bodrog.SK with others 1.0 1.0 2A 1M 5 10 6 9 8 3 7 12 11 4 1M 2A 5 10 8 9 6 3 7 12 11 4 1M 2A 5 9 10 6 8 3 7 12 11 4 4 8 11 12 3 8 4 7 0.8 0.8 5 7 8 11 9M A 4 2 1 4 2 2 1 8 2 2 1 1 5 11 7 8 3 10 7 8 8 7 8 1 6 11 2M 4 6 4 7M 12 8 6 4 7M 6 4 7M 10 2 A 7 8 3A 9M 3 3A 4 1 3A 11 3 4 12 10 A 12 10 7 12 3 4 8 8 2 8 9 7 2 10 10 12M 8 9 2 9 11 1 3 8 2M 5A 9 11 A 11 12 9 6 A 0.6 0.6 10 8 7 7 8 7 2 3 4 3 10 10 3 10 3 10 8 5 1 7 8 7 4 5 9 11 5 5M 2 10 9 8 5 8 10 4M 9 2 7 8 9 3 9 10 8 5M 4 9 8 8 8 4 2 3A 9 3 9 6 5M 2 9 M 6 7 2A 3 7 8A 4 12 6 1A 9 7 6 1M 4 11 1 10 10 5 9A 3 11 4 1 10 8 4 7 3M 11 7 11 10 9 7 7 7 11 1 11 1M 3 5 3 1 11 4M 9 9 6 11 10 A 4M A 10 8 4 11 1A 9M 1 4 9 5A 8 8 1 10 1 2 0.4 0.4 7 12 1 11 6 10 5 4 4 2 10 7 11 9 4 9 5 12 11 11 12 12 3 6 1M 3 A 1 M A 8 2 3 9 9 2 A 1M 10 12 7 9 4 3 5 1 2 12 8 3A 6 4 10 10 12 A 2 6 12 12 11 9 3A 4 A 12 1 A 1M 6M 7A 2M 4 11 12 9 9M 12 A 3 M 1 M 1 3M 10 11 2 7M 1 A 2 11 11 11 6M 6 9 7 7A 11 6 1 12 A M 2A 3 2 8 9 5 2 1 10 A M 10 3 11 6 4 12 4 5 5 11 125 8 10 10 12 2 10 1 12 12 5 11 9 6 12 2M 5 11 2 6 6 1A A 6 6 12 5 10 4 10 5 0.2 0.2 6 12 6 5 5 1 2 5 5 6 3 6 6 6 6M 12 12 6 10 3 5 9 3 1 6 7 3 2 2 12 5 5 6 5 11 4 7 5 7 12 11 5 5 6 0.0 0.0 12 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Source: Aon Benï¬?eld. Figure 3A.3. Hungary Correlation of Ipel.HU with others Correlation of Dunaj.HU with others 1.0 1.0 2A 1M 5 8 9 6 10 3 7 12 11 4 1M 2A 5 8 10 9 6 3 7 11 12 4 1 10 9 8 12 5 2M 7 11 4A 4 12 1 1 6 3 11 Financial and Fiscal Instruments for Catastrophe Risk Management 3M 5 8 0.8 0.8 5 2 5 11 A 2 4 4 10 11 6 4 1 2 1 4 3 3 8 10 12 9 4 4 4 1 8 8 7 3 9 12 12 4 3 4 9M 9M 10 7 M 7M 12 9 9 A A 1 3 12A 8A 1 3 7 5 11 4 2 12 12 9 9 10 4 11 2 1 9 2 1 2 1 10 3 10 7 10 12 9 4 11 1 9 7 8 0.6 0.6 11 7 1 11 1 10 10 9 4 10 12 11 M 11 4 8 11 4 9 9 7 8A 6 11 7 9 5A 8 2 1 10 10 5 9 8M 8 8 8 9 5 9 3M 2 9 5 7 4 4 4 10 M 1 11 5 9 6 4 10 4 4 4 3A 9 7 8 9 7 4 9 A 10 4 2 6 6 1 11 2 1 A 4 8 6 3 3 10 1 9M 10 M 3M 11 5M 1 4 M 6 8 4 8 12 9A 10 10 6 1 10 7 3M 5 7 6 9 A 11 2A 2 11 7 11 3 9 11 2M 11 2 3 7 5 A 5 6M 2 1A 12 4 9 11 A 9 M A 2 8 11 6 4 M M 9 2 6 2 0.4 0.4 5 2 A 4 2 4 12 5 10 8 10 6 8 11 6 2 12 11 10 3 6 M 6 8 6 5 12 10 8 6 1M 3 12 1M 3 5 2 3A 7 9 M 6 11 A A 10 A A 1 7 12 12 4A 8 2M 10 12 3 8M 1A 1 6 A 10 5 10 1 12 10 5 7 6 9 M 2 8 5 2 12 5 3 7 11 8 7 5 1 6 A 8 7 6 2 6 9 M A A M M 2 A M 10 7 8 1 10 7 1 8 8 6A 6 5A 1 12 5 6 6A M 8 6 11 11 11 2 2 6 12 M 5 7 M 1 0.2 0.2 10 A 5 7 11 5 9 5 2 7M 2 2 3 6 12 1 6 9A 12 12 1 11 3 10 3 6 8 3 10 6 8 5 3 11 5 2 4 3 10 8 5 11 7 7 6 1 7 4 5 5 12 12 3 7 5 12 12 3 7 5 11 3 0.0 0.0 7 7 12 7 12 12 8 11 3 3 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL (Figure continues on next page) 111 112 Figure 3A.3 (continued) A World Bank Study Correlation of Salo.HU with others Correlation of Hornad.HU with others 1.0 1.0 2A 1M 5 10 6 9 8 3 7 12 11 4 1M 2A 5 10 8 9 6 3 7 12 11 4 1M 2A 5 9 8 10 6 3 7 12 11 4 12 4 4 4 4 8 1 8 8 11 12 11 11 12 11 12 3 3M 5 8 3 3 0.8 0.8 2 5 7 9M A A 9M A 11 9M A 4 2 8 2 10 2 2 1 4 2 5 6 5 5 10 7 10 7 8 3 10 7 1 6 1 6 7 8 1 6 11 8 2 11 6 4 7M 12 8 7 4 2 4 1 7 8 3A 9M 3 7 4 7 2 7 4 4 12 10 A 8 8 8 M 10 12 4 8 8 2 9 4 8 8 11 A 4 8 8 2 9 11 1 3 8 2M 5A 0.6 0.6 7 1 10 4 10 8 7 7 8 7 7 8 8 11 8 4 3 10 10 1 10 7 11 7 M 9 7 3 8 5 7 11 4 9 10 5M 3 7 7 2 3 10 8 5 8 4 4M 8 9 A 11 3 2 5M 3A 9 2 2 6 9 1M 11 4 4 4 9 3 2 A 5A 2 9 9 M 6 7 2A 3 M 9 6 5 4 11 1 10 10 A 9 M 9 3 1 9A 3 11 4 1 10 4 7 7 7 10 1 3M 5 9 A 9 11 12 7M 11 7 10 11 1 11 10 4M 9 9 9 11 6 1 11 A 4M 1 3 5 8 3A 8 9 5A 8 11 10 8 9 0.4 0.4 A 9 7 12 1 9 5 A 9 M 7 5 9 4 1 10 4 3 2 12 3 6 1M 3 A 1 M A 8 5 12 A A A 7 9 5 1 2 5 1M 5 A 2M M 8 3A 6 4 10 10 12 4 3 A 2 6 12 12 2 3 2M 11 12 12 12 11 10 6 3 1 A 1M 6M 7A 12 11 4M 9 9M 10 11 6 6 4 2 11 11 11 6M 6 2 7A 10 5 5 9 5 1 11 1 A 10 9 7 3 11 6 11 3 6 10 4 5 M 1 2A 3M 11 1 2 1 12 1 1 6 7 12 11 12 2 10 1 12 12 12 3 12 6 2 6 9M A 6 12 5 10 0.2 0.2 10 10 12 6 12 6 5 5 6 2 5 6 3 6 10 12 5 12 6 12 12 5 6 3 6 10 2 2 6 6 1 10 12 2 5 5 12 5 5 6 0.0 0.0 3 1 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL (Figure continues on next page) Figure 3A.3 (continued) Correlation of Leitha.HU with others Correlation of Raab.HU with others 1.0 1.0 2A 1M 5 6 8 10 9 3 7 11 12 4 2A 1M 5 6 10 9 8 3 7 12 11 4 Financial and Fiscal Instruments for Catastrophe Risk Management 0.8 0.8 12 1 3 1 3 1 3 7 12 7 7 10 2 10 0.6 0.6 1 1 9 10 9 3 7 6 12 11 6 2 1 3 2 8 9 8 3 12 5A 8 2 10 10 3 2 1 2 M 5M 11 1 12 8 A 4 9 10 11 1 2 11 12 8 2 4 10 1 10 2 9 4 3 1 9 11 2 M 1 2 M M 9 2 8 12 5A 1 3 4 8 1 A 1 12 11 5A 10 12 8 2 11 8 1 7M 8 10 2 12 8 M 12 12 12 8 8 8 3 2 7 10 2 3A 8 8 0.4 0.4 9 5 2 8 4 8 10 8 7 4 5 4 4A 8 4M 9 1 10 6 4 1 12 1 1 3 8 10 7A 7 3 3 2 10 7 12 2 A A 9A 9 4 9 5 8 5 7 10 M M 3 A 6M 4 A 4 2M 3 6M 18 12 8 8 5 5 2 10 5 2 1 A 5 7 A 5 M 8 6 12 11 10 6 2 5 6 9 10 11 4M 11 7 9 7A 8 11 7A 3 11 9 M A 4 8 10 9M 4M 12 4 4M 12 1 2 10 9 7 6 9 8 M 5 5 8M 9 10 4A 1 2 7 9 7 9M 29 7 3 7 9 2 9 7 3 7 10 10 10 9A 11 8 11 6A 5 5 4 11 2M 4 7 A 6 6A 6 9 2 6 10 12 10 12 7 4 1M 9 2 7A 12 M 8 2 1M 1 7 5M 6 A A 7 A 1 12 3 7 A 10 M 5 2 1 5 10 4 3 0.2 0.2 5 5 5 11 7 9 5 1 9A 4 9 7 6 6M A 10 4 4 5 5 6 2 3M 8 11 5 5 10 5 9 A 10 6M 6 6M 12 6 7 9A 4 6 1 11 M 8 6 1 11 8 11 6 8A 5 4 11 12 9 3 5 4 7 1 7 5 6 11 12 3 2 5 2 3 6 7A 4 3 5M 11 10 11 11 12 12 11 A 4 7 6 6 6 12 1 4 5 11 11 11 7 9M 12 12 6 4 11 10 1 6 12 11 3 0.0 0.0 3 9 3 6 12 11 4 3 6 3 4 11 A Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL (Figure continues on next page) 113 114 A World Bank Study Figure 3A.3 (continued) Correlation of Tisza.HU with others 1.0 2A 1M 5 10 8 6 9 3 7 12 11 4 8 4 7 0.8 1 11 8 7 2M 10 A 3 2 7 4 4 9 11 3M 3 8 6 12 11 8 3 8 10 4 0.6 10 1 9 1 11 1 8 7 9 3 3 7 10 9 9 M 10 7 A 8 2 9 3 3 8 8 3 4M 9 10 4 10 10 A 4M 3 1 10 10 9 9 M 2 2 10 11 11 2 1 2A 1 11 3 6 2A 2 11 5 3 11 111M 7 1 4 7 2 4 7 8 2 1 2 11 6 8 9 3 11 10 8 2M 7 11 3 10 2 3 9M 6 8 4M 7 A 6 9 5 12 9 9 10 M 4 M 10 2 7M M A 0.4 5 12 A 1M 12 4 A 7A 8A 8 8 5A 1 11 6 10 M A A 11 12 3A 7 1 7 12 A 11 12 12 12 12 1 8 2 10 3 1 4 7 9 1 4 9 6 5 1 5 5 6 2M 4 10 9 1 12 8 7 9 6 12 A 12 0.2 12 4 5 5 5 7 6M A 5 6 12 12 5 11 5 6 5 5 6 5 6 11 2 5 4 5 12 0.0 6 6 12 3 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Source: Aon Benï¬?eld. Figure 3A.4. Poland Correlation of Odra.PL with others Correlation of Wisla.PL with others 1.0 1.0 1M 2A 5 8 6 10 9 3 7 12 11 4 2A 1M 5 6 10 9 8 3 7 12 11 4 Financial and Fiscal Instruments for Catastrophe Risk Management 0.8 0.8 11 7 8 7 7 4 4 10 10 10 8 10 2 8 11 12 5 10 2 1 8 7 11 7 10 11 11 7 10 4 1 8 8 2 9A 12 9 7 12 7 1 2 11 2M 5 4 2 6 1 10 11 4 7 2 11 12 9 12 1 11 8 10 1 3 3 3 12 1 11 5M 10 8A 8 8 5 8 10 8 7 8 8 10 11 9 9 8 2 4 4 8 9 1 4 9 9 11 3 8 1 12 M 2M A 2M 4M 7 12 7 1 8 10 8M 4 6 3 4 7 M 11 1 1 7 2 A 5A 8 A M 0.6 0.6 1A 1 4 9 8 5 8M 2 3M 9 2 M 4M 11 6 9 10 10 10 6A 3 6 1A 9 5 3 2 7 8 8 9 4M 8A 3A 4 A 7 2 3 11 2M 9 5 9 7 A 9 2 11 6 1 8 8 3 7A 11 9 1 8 1 10 5 5M 7A 2 10 3 12 11 8M 1 4 A 11 3 7 M 11 10 12 10 4 11 8M 4 4 6 A 10M 3 A 4 4 1 10 11M 3 12 9 12 2 7 6 4 3M A 2 1 2 7 12 4 10 3 11 3A M 9 1 9A 1 1 2 A 3 11 12 11 6 3 11 7 11 9 1 3 3 A 12 10 5 7 10 5 3 3 4 9 9 7 12 9 1 11 12 4 10 3 10 2 10 3 7M 12 8 9 2 5 6 7M 2 4 4A 9 2 4 0.4 0.4 12 12 6 12 7 9 2 7 5A 11 12 6 6 A 7M 3 2 2 6 1 M 9 A 9 M 12 A 9 10 4 4 1 6 A M 1 12 3 6 M 12 6 12 3 9 2 A 12 3M 9 2 2 6 5 4 A 5 8 5 6 6 10 11 2 8 3 1 5 4 5 3 8 A M 2 5 5 6 5 5 5 6 12 7 5 8M 9 5 5 5A 1 9A 4 2 6 11 12 7 11 12 12 5 5 5 M 12 5 5 1 6 10 5 5 4 1 0.2 0.2 11 10 7 6 6 8 3 10 9A 7 6 5 M 1 3 6 4 6 11 10 2 7 1 6 6 6 10 11 A 4 9M 6 6 7 10 1 6 12 3 0.0 0.0 12 11 12 Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL (Figure continues on next page) 115 116 Figure 3A.4 (continued) A World Bank Study Correlation of Narew.PL with others 1.0 2A 1M 5 10 8 9 6 3 7 12 11 4 0.8 5 6 10 1 11 12 1 0.6 11 11 2 10 4M 8A 8 2 10 10 1 7 9 10 11 5 2 11 5 11 8M 12 12 2 1 4 7 12 9 10 1 8 9 5 8 A 2 6 11 12 1 4 3 3 9 1 5M 4A 6 9 2 8 1 4 M 7 9 0.4 M 4 1 4 1 12 11 12 2 6 4 11 12 4 10 11A 1 5M 9 10A 12 10 8 2 9 6 4M 8A 8 2 10 8 2 A 1 4 6 10 12 4A 6 A M 12 A 9 7A 10 12 3 6 M 1M 5 6 9 9M 2 M 4 7 12 9M 4 12 11 7A 5 6 9 7A 7 8 8M 3 7 8 11 6 2 11 11 6 1 2 A 7 3 3 10 1 8 2 11 7 1 1 6 9 5 3 6 7 12 6 10 7 9M A 12 10 4 0.2 7 4 4 7 6 8 7M 3 5 9A 5 4 8 5 3 3 3 8 5 3 2 11 7A 5M 10 2 7 3 5 5 5 12 3 3 0.0 5 3 3 9 8 A Odra.CZ Morava.CZ Dyje.CZ Vltava.CZ Labe.CZ Dunaj.SK Vah.SK Hornad.SK Bodrog.SK Ipel.HU Dunaj.HU Salo.HU Hornad.HU Tisza.HU Leitha.HU Raab.HU Odra.PL Wisla.PL Narew.PL Source: Aon Benï¬?eld. CHAPTER 4 Private Insurance Markets and Public Disaster Financing Mechanisms Overview Central Europe’s Risk Exposure to Natural Hazards T his chapter comprises a review of government post-disaster safety nets as well as those provided by the private insurance market in the Czech Republic, Hungary, Poland, and the Slovak Republic. As frequency and severity of major natural hazards and economic and insured loss- es caused by them have considerably increased world-wide, the countries of Central Europe have been no exception. Central Europe is vulnerable to a number of natural hazards, such as flood, landslide/mudslide/debris-flow, avalanche, windstorm, weight of snow, and fluctuations of extreme temperature. Between 1980 and 2006, Europe witnessed a major growth in the scale and frequen- cy of extreme weather events, which represented 89 per cent (238 billion of euro) out of the 366 billion of euro overall losses from disasters caused by the impact of natural hazards in this region. The European Environmental Agency’s current projections sug- gest that South Eastern, Mediterranean and Central Europe regions are among the most vulnerable to climate change. Considerable adverse impacts are expected to occur to natural and human systems that are already under pressure from changes in land use and se lement. The 1997, 2005, and 2006 floods in Central Europe demonstrated that large disasters caused by the impact of natural hazards can be very costly and can have major negative impacts on national budgets. For instance, the 1997 floods caused over EUR 5.6 billion in economic damages mainly to Poland (2.9 billion - 3.5 billion), the Czech Republic (1.8 billion) and the Slovak Republic (0.06 billion). The 1997 floods were followed by another devastating flood across the Central Eu- ropean region in 2002. In that year, total economic losses from floods in Central Europe exceeded EUR 3 billion During this event, the Vltava river exceeded the water level of the major 1890 floods in Prague. Some 200,000 Czech residents were evacuated during the flooding, which caused the total economic loss of EUR 2.3 billion in the Czech Republic alone. Later, the 2006 floods affected Hungary, Bulgaria and Romania with economic damages estimated in excess of EUR 0.5 billion Despite considerable economic loss potential from floods in the region, Central Eu- rope appears to fare much be er than most disaster prone areas in the world in terms of its ï¬?nancial preparedness for natural disasters. On average, over 50 percent of hom- 117 118 A World Bank Study eowners across the region are insured against natural disasters and governments, cog- nizant of devastating floods at the turn of the last century, have been allocating sig- niï¬?cant budgetary resources annually for emergency preparedness and post-disaster reconstruction and aid. Fiscal Disaster Risk Financing Mechanisms at the Country Level In all surveyed CEE V-4 countries, national annual budgetary allocations for emergen- cies are signiï¬?cant. All national emergency funds are annual non-accruing funds, mean- ing that they maintain the same statutory size in budget percentage terms and cannot be accumulated or carried forward from one year to another. In all surveyed CEE V-4 countries, the emergency assistance aid can be made avail- able to households, businesses, and local governments. None of the surveyed countries have a means testing requirement as a precondition for emergency assistance, although in Poland local officials have a considerable discretion in determining the eligibility for post-disaster aid. Overall, there is no clear delineation of government and private sector liabilities when it comes to funding economic damages in the aftermath of a disaster. In all four countries the government stands ready to provide post-disaster ï¬?nancial assistance to the victims of natural disasters both for emergency relief and reconstruc- tion purposes. While the former comes in the form of grants, the la er form of assistance in provided in the form of low interest reconstruction loans and reconstruction grants. The administrative process involved in mobilizing additional resources in cases of major disasters caused by the impact of natural hazards appears to rely by and large on the administrative capabilities of local governments, which are deeply involved in the whole process of post-disaster loss assessment and eventual distribution of the funds to beneï¬?ciaries. The emergency relief assistance appears to be delivered to victims of disasters within days. The Role of Private Catastrophe Insurance in Disaster Risk Financing in the CEE V-4 Countries Due to the very large floods at the turn of century the insurance industry in the CEE V-4 is well prepared to handle large catastrophic events. The big floods have also helped to increase the public awareness of risk which translated into a rather high level of prop- erty insurance penetration among households and SMEs. The unique feature of the the CEE V-4 markets is that traditionally catastrophe insurance perils have been included in the overall scope of coverage under the homeowners’ policy rather than being an option- al policy endorsement, which is the case in most other markets. As a result, almost all households with a ï¬?re policy are also automatically covered against flood, windstorm, landslides, hails and avalanches. Table 4.1 summarizes our estimates of catastrophe in- surance penetration in the CEE V-4 region by country. Table 4.1. Catastrophe Insurance Penetration in Central European Countries (Estimates) Country Homeowners with catastrophe insurance (%) Czech Republic 49–65 Poland 56–40 Slovak Republic 51–60 Hungary 60–73 Source: AXCO, Swiss Re, World Bank. Financial and Fiscal Instruments for Catastrophe Risk Management 119 The cost of catastrophe insurance coverage varies considerably across the region, not only because of different levels of disaster risk exposure in different countries but also because of varying levels of market discipline over risk pricing. For instance, if in the Czech Republic, most insurers charge adequate risk premium for the catastrophe portion of the risk, in Poland the situation is markedly different as the risk premium is driven more by the market competition than by the technical fundamentals. On the supply side, as the market is dominated predominantly by large internation- al insurance groups, except for Poland where the state-owned insurer PZU still holds the lion’s share of the market. Reinsurance for individual country subsidiaries is typically placed in a centralized fashion through reinsurance departments of mother companies, which in turn place the cover for the whole group. This approach allows realizing con- siderable savings which translates into lower premiums for homeowners. In the case of PZU, the sheer size of the company allows it to pool the risk country-wide which results in a well-diversiï¬?ed risk exposure and highly affordable pricing. Insurance regulators in CEE V-4 countries seem to be well aware of the importance of adequate catastrophe risk management in insurance companies. In the Czech Repub- lic, for instance, the Insurance Regulator requires companies to supply estimates of their overall Probable Maximum Loss from a 200-year event as well as the details of their re- insurance programs. In Poland, catastrophe risk scenarios have been incorporated into a planned stress testing for the whole market. Policy Recommendations Despite considerable risk exposure to natural disasters the existing risk ï¬?nancing mech- anisms in the countries of Central Europe are relatively well developed to address the consequences of large catastrophic events. The ï¬?nancial preparedness of the CEE V-4 countries to natural disasters manifests itself in rather high levels of catastrophe insur- ance coverage (well over 50 percent) as well as in the commitment of government re- sources in national budgets for emergency situations. Several recommendations emerge from this analysis. They are intended to guide government policy makers in developing and applying national and regional disaster risk ï¬?nancing strategies, suggest ways in which the Bank can be er address catastrophe risk ï¬?nancing in their dialogue with clients, and provide information and ideas that may be of value to other stakeholders, such as international donor organizations, NGOs, aca- demics, and the general public. Lessening the impact of natural disasters on government budgets. Despite a rela- tively high level of insurance penetration in the CEE V-4 countries, governments still carry a considerable budgetary exposure to catastrophic floods. The current regulatory frameworks in the Czech Republic and Poland, for instance, make it a government obli- gation to assist homeowners in post-disaster recovery and reconstruction efforts. More- over, in the case of 1997 flood, the Czech government paid an equal compensation to both insured and uninsured homeowners for equity reasons. The post-disaster compen- sation was funded by additional government borrowing. This approach to disaster compensation does not seem to optimal. In the case of di- saster compensation, governments should clearly ï¬?nd a way to separate between public and private liabilities. While the provision of disaster relief, reconstruction of national life-lines (for example, utilities, roads, schools and hospitals) is clearly the government responsibility, recovery of private assets should be funded by individual savings and in- 120 A World Bank Study surance. This is particularly the case when such insurance is widely available and quite affordable. To this end, the governments of the CEE V-4 countries may consider changing the existing post-disaster compensation policies for housing reconstruction by introducing a strong element of private responsibility for losses inflicted by natural disasters. Such a policy change is likely not only to considerably increase insurance penetration among homeowners but also will help signiï¬?cantly reducing government ï¬?scal exposures to natural disasters. Reducing the ï¬?nancial vulnerability of homeowners and SMEs to natural hazards. While the analysis documented rather high levels of catastrophe insurance penetration among homeowners and SMEs in Central Europe, large portions of population still re- mains uninsured. In this context, the governments should consider investing in increas- ing public risk awareness as well as changing the existing post-disaster compensation policy (see above). In addition, those countries of the region, which have a lower level of property insurance coverage among homeowners, should consider introducing a stand-alone catastrophe insurance coverage for homeowners and small business own- ers, which can be backed by a dedicated reinsurance capacity at the regional level. As has been demonstrated by international experience, such programs can provide highly af- fordable coverage by realizing the beneï¬?ts of region-wide risk diversiï¬?cation, economies of scale and the ability to obtain be er pricing terms from the global reinsurance market. Enhancing the ability of local regulators to assess the solvency implications of in- surers’ catastrophe risk exposures. Although the analysis documented a considerable level of technical sophistication on the part of CEE V-4 insurance supervisors in monitor- ing and regulating insurers’ risk exposures to natural disasters, the capacity of CEE V-4 insurance regulatory bodies in catastrophe risk management would beneï¬?t from further investments in regulatory (risk assessment and monitoring) tools and specialized staff training. The Impact of Natural Catastrophes on Central Europe This section includes a review of government post-disaster safety nets as well as those provided by the private insurance market in the CEE V-4 countries. Although several natural hazards affect the regions, most of disaster related losses in can be a ributed to the risk of flood. Objectives, Scope, and Methodology The analysis a empts to establish the extent of ï¬?nancial vulnerability of governments and households to natural hazards in four countries of the CEE V-4 by examining: â–  The ï¬?scal policy of the four Central European countries in the areas of post-disas- ter relief and reconstruction. â–  The extent of catastrophe insurance coverage provided by the private insurance industry in the region as well as the technical capacity of national insurance mar- kets to manage catastrophe insurance risk. Besides documenting the current state of government and market-based safety nets for homeowners and SMEs affected by natural disasters, the analysis also suggests a range of practical solutions and policy recommendations with the view of reducing Financial and Fiscal Instruments for Catastrophe Risk Management 121 the ï¬?nancial vulnerability of the region to natural disasters. The analysis was prepared based on a series of wri en surveys followed by interviews with key government offi- cials, government experts and insurers in the four countries. Survey of Catastrophe Insurance Markets in Central Europe Central Europe’s Risk Exposure to Natural Hazards The CEE V-4 countries are highly vulnerable to natural disasters such as flood, landslide, mudslide, debris-flow, windstorm, weight of snow, and extreme temperature fluctua- tions, with the risk of flood being the most signiï¬?cant. As shown in table 4.2, economic and insured losses in four countries of the region from the 1997 flood alone- which is considered to be the most catastrophic event in the region over the last 200 years—were in excess of US$5 billion. Table 4.2. Economic and Insured Losses from 1997 Flood in Central Europe Country Poland Czech Republic The Slovak Republic Economic loss (US$ million) 2,900 1,800 60 Insured loss (US$ million) 450 305 10 Source: Swiss Re and Munich Re (1997). Catastrophe Risk Policy Coverage Over the last 10 years, the non-life insurance industry in Central European countries has been transformed by the rapid consolidation, privatization and entrance in the market of large multinational insurance groups (such as Generali, Allianz, ERGO (Munich Re Group), and VIG), which currently control the lion’s share of the market in all countries of the region, except Poland where the state-owned PZU still remains the biggest player. As a result, the insurance terms and conditions offered by most companies to homeown- ers and SMEs in the market under the FLEXA1 cover are rather standard, with only slight variations across different companies. Natural Hazards Covered In all the CEE V-4 countries, insurers offer “all-risksâ€? homeowners coverage which in- surers property damage to private dwellings from FLEXA and all major natural perils such as flood, land-slide, windstorm, avalanche, hail, earthquake. Most FLEXA policies offered by the market in Central Europe do not allow the insured to decline natural haz- ards cover, which is an integral part of individual property insurance policy. Small busi- nesses, industrial and commercial customers however can choose perils to be covered by their insurance policy, which to a large extent explains why a considerably larger percent of homeowners is covered against natural disasters. Speciï¬?cally, while in the case of homeowners, almost 100 percent of those insured against the risk of ï¬?re are also insured against natural perils, only 40-50 percent of SMEs with a FLEXA cover have coverage against natural perils. Catastrophe Insurance Penetration In general, the CEE V-4 countries have a rather high level of insurance coverage for natu- ral perils—well over 50 percent, which is considerably higher than in most OECD coun- 122 A World Bank Study tries without a mandatory catastrophe insurance scheme. In comparison, catastrophe insurance penetration in Germany during the 2001 floods stood at a meager 7 percent. The main drivers of such a high level of catastrophe insurance coverage have been a heightened level of risk awareness by the public after the 1997 and 2001 major floods, the expansion of the mortgage lending industry (as lenders typically require a proof of property insurance), considerably improved distribution and marketing capabilities of local insurance companies and, ï¬?nally, rapid economic growth, which translated into stronger demand for insurance. Insured limits. Insured policy limits for natural perils are typically the same as the sum insured under the underlying FLEXA policy. The limits of coverage however vary signiï¬?cantly from one country to another. It is worth mentioning that in some Central European countries the insured limits remain artiï¬?cially low due to the fact that they are linked to the historic book property values, which have not updated since the early 1990s. Deductibles. As deductibles are not very popular with individuals and corporations in Central European countries, they rarely exceed two percent of sum insured or a few hundred euros. Many companies do not have any deductibles at all under their all-risk property policies. Premium rates. The pricing of ‘all-risk’ property coverage varies signiï¬?cantly throughout the region based on the local market conditions and the pricing sophistica- tion of insurers. The premiums for all-inclusive property coverage range from 1 to 4 per mille, for example, (0.01 percent to 0.04 percent). The variation in the rates can be mainly a ributed to the level of competition in each market rather than to risk characteristics of insured dwellings. In some countries however insurers refuse coverage to dwellings located in the most flood prone areas, for example, in Zone IV. Terms of coverage. The terms of coverage for catastrophic perils offered by the local market appear rather generous as insurance policies cover all risks (for example, FLEXA and natural hazards) with low deductibles. In most cases, FLEXA policies include pro- vide coverage for damage to the building structure alone, with contents of the building insured under a separate policy. Indemniï¬?cation basis. In covering catastrophic perils, insurers are often faced with the problem of underinsurance arising of policyholders buying less coverage than the replacement cost of their property. To deal with this problem, insurers include under- insurance penalties into the terms and conditions of the policy which have the effect of reducing the amount of indemnity paid in the aftermath of a disaster proportionately to the rate of underinsurance.2 However, as many insurance policies are sold in conjunc- tion with mortgage loans, the insured limits are typically set to cover the replacement cost of mortgaged property to protect mortgage lenders against the loss or damage to their collateral that may be caused by ï¬?res or natural perils. Claims se lement. In the CEE V-4 countries, loss adjustment is typically carried out by loss adjusters from insurance companies, although for complex and large com- mercial/industrial losses external professional loss adjusters may be engaged as well. Reinsurers may also be involved if losses exceed a pre-agreed value. Financial and Fiscal Instruments for Catastrophe Risk Management 123 In most countries of the region, claims se lement is typically done either on the new replacement cost or residual value basis. The residual value approach enables the insured to reduce the insured limit (initially set at a historic book value) by the amount of accrued depreciation and hence pay less for coverage which consequently, in the case of a loss, results in a corresponding reduction of the indemnity payment. Under the new replacement cost approach the insured limit is set based on the estimated current replacement cost of the dwelling, which is updated annually, which naturally results in higher insurance premiums as well as indemnity payments. Risk management. In all Central European markets, large insurance companies have the necessary risk management skills and expertise to adequately manage their catastrophic risk. As catastrophe risk accumulations of local insurers can be quite signiï¬?- cant relative to their capital base, large companies actively monitor their risk accumula- tions and buy considerable amounts of catastrophe excess-of-loss (XL) reinsurance to reduce their overall risk exposures to major floods. In the case of large insurance groups, reinsurance is typically placed at a group level for all country subsidiaries, which results in a considerably improved quality and amount of available reinsurance protection. Typically, reinsurance treaties are concluded with large reputable international re- insurance companies. The amount of reinsurance bought by large groups is sufficient to cover catastrophic events with a 250-year return period. Most of surveyed large in- surers had quantitative estimates of their probable maximum loss (PML) potentials for different return periods, as probabilistic commercial flood risk models have been made available by several European risk modeling companies (Prague-based Intermap, that conducted the analysis in chapter 2, is one example) as well as large reinsurers (Swiss Re and Munich Re) and commercial reinsurance brokers. As a result, several companies reported that they use at least 2-3 risk models before commi ing to a given estimate of PML for a chosen return period (typically 200-250 return periods are used). The above described level of sophistication in risk management is rarely the case for smaller companies, which do not have the necessary capital and human resources to af- ford sophisticated risk management systems. Those however, account for 10-15 percent of market share and hence are unlikely to pose a systemic threat to the market in case of a major flood event. Insurance laws and regulations. None of the countries of the region, except for Czech Republic have any speciï¬?c requirements for pricing, reserving, reinsuring or reporting catastrophe risk underwri en by local insurers. The Czech Insurance Regulator requires companies to regularly report their estimates of 250 year PMLs (based on their own risk models) as well as the details of their reinsurance treaties. In Poland, the monitoring of companies’ catastrophe risk exposures is viewed by the local regulator as an integral part of preparatory work for Solvency II. Hence, plans have been made to incorporate catastrophe risk scenarios into the stress testing of insurers scheduled for the next year. Poland is in the process of developing a mandatory catastrophe insurance law which would make catastrophe insurance obligatory for all homeowners. The law however is at the very early stages of development. Product distribution channels. In the CEE V-4 region, to distribute their products, insurers use mainly their own sales forces, and often tied agents. Bank-assurance is also becoming more common. 124 A World Bank Study Czech Republic—Catastrophe Insurance Market Overview Country Disaster Risk Proï¬?le Flood is by far the main natural hazard in the Czech Republic, causing insured losses of CZK 9.8 billion (US$305.8 billion) in 1997 and CZK 36.79 billion (US$1.13 billion) in 2002. Floods result from the limited drainage capacity and the lack of regulation of some of the country’s major rivers. Flooding normally occurs in the summer months when annual rainfall is at its highest but can also be caused by snow melt. The most hazardous areas are the river valleys of Moravia and East Bohemia, some of which form narrow, steep- sided channels that deeply cut through the surrounding plateau lands. As the July 1997 floods demonstrated, however, almost every part of the country is potentially exposed. Flooding is an annual occurrence in the Czech Republic, but was regarded as li le more than a nuisance until the catastrophic floods of July 1997, which inundated 35 percent of the country. This led to a huge increase in the penetration of industrial and residential flood insurance, with the result that further floods in August 2002 caused insured losses of CZK 36.79 billion (US$1.13 billion), up from CZK 9.78 billion (US$305.8 billion) in 1997. Events of this magnitude are reckoned to have a return period of 500 years on the Vltava and Labe rivers and 250 years on the Berounka, though the fact that two catastrophes occurred only ï¬?ve years apart must throw all such calculations into doubt. A list of major recent flood events in the Czech Republic is presented in table 4.3. Table 4.3. Historical Disaster Losses in the Czech Republic Date Area Losses December 1993 South and west Bohemia Insured losses CZK 750 mln to CZK 1 bln (US$26 mln to US$34 mln) June 1995 Central Bohemia Estimated loss CZK 200 mln (US$7.5 mln) May 1996 Central and south Bohemia, Estimated loss CZK 500 mln (US$18 mln) north and south Moravia September 1996 North Moravia Estimated loss CZK 220 mln (US$8 mln) July 1997 East Bohemia, north and south Worst floods since 1903 caused by rainfall of up to 300 litres per Moravia square metre of ground. One-third of the country under water. 60 fatalities. Total material damage estimated at CZK 63 bln (US$1.99 bln), of which insured losses were CZK 9.78 bln (US$305.8 mln) July 1998 East Bohemia Total economic losses CZK 1.8 bln (US$55.8 mln). 10 dead. Estimated insured losses CZK 569.5 mln (US$17.6 mln) including two commercial claims at CZK 100 mln each (US$3.1 mln) March 2000 North and east Bohemia Estimated losses CZK 2 bln (US$49.0 mln), including CZK 200 mln (US$4.9 mln) in respect of VW Skoda car plant April 2000 Flash flooding hit the Juta Insured loss CZK 400 mln (US$10.4 mln) textile mill August 2002 Catastrophic flooding affecting Estimated economic losses CZK 73 bln (US$2.23 bln). Estimated 15% of the land area, including insured losses CZK 36.79 bln (US$1.13 bln) the capital, Prague April 2006 Flooding caused by snow melt and rain. Estimated insured losses in excess of CZK 2.0 bln (US$88.5 mln) Source: AXCO Country Report, 2008. Note: mln = million; bln = billion. Financial and Fiscal Instruments for Catastrophe Risk Management 125 The country is also exposed to a greater or lesser extent to windstorm, hail, weight of snow, snow melt, and earthquake, although the la er is very insigniï¬?cant. The risks of windstorm and weight of snow, however, appear to be quite real. For instance, there have been two unexpected windstorm catastrophes in the last two years caused by “Ky- rillâ€? and “Emmaâ€? causing over EUR 100 million in damages. An atmospheric hazard which caused unexpectedly high losses in 2006 was weight of snow, followed by flood- ing caused by snow-melt. Contrary to initial indications, most of the damage was caused by flooding rather than weight of snow, which mainly affected disused buildings in the residential and small business sectors. There is no published estimate for the total market loss, though Ceska Pojistovna alone paid weather-related claims of CZK 1.5 billion (US$66.37 billion) in the ï¬?rst half of 2006, which would be an equivalent of about US$180 million for the whole market. Natural Hazards Insurance Currently, the local insurance market offers an all-risk inclusive property insurance policy, which besides traditional FLEXA perils also provides coverage against almost all known types of natural disasters. While in theory, homeowners can exclude natural perils from their coverage, in practice very few are doing so as the design of homeown- ers policies by most companies does not provide consumers with an option to opt out of natural perils coverage. There are no stand-alone catastrophe insurance policies in the market today. The scope of coverage under the homeowners’ policies includes damage to struc- tures and internal ï¬?xtures. House contents must be insured under a separate policy. A standard contents coverage includes a small insurance sublimit on the value of insured contents in house basements, which are the most vulnerable to floods. The sublimit can be increased for an additional premium. Due to the floods of 1997 and 2002 and the rapid growth the market has experi- enced ever since, the number of individual property policies at the end of 2007 stood at 1,875,523.3 This corresponds to about 49 percent of all residential dwellings in the Czech Republic, which is a rather high level of catastrophe insurance penetration by OECD standards. This high number however conceals the problem of obtaining coverage for people living in flood prone areas as most insurers do not provide the coverage. In contrast to Hungary, however, non-availability of flood cover has not become a political issue and there is no pressure for the establishment of a national flood insurance pool. The problem with insurability of houses located in close proximity to rivers is also being gradually addressed by the government investments in flood protection barriers as well as the government decision to prohibit housing reconstruction in the most flood prone areas. The main drivers behind such a high level of catastrophe insurance coverage among homeowners have been the tremendously increased risk awareness among the popula- tion after the two major consecutive floods, exponential growth in mortgage lending (as banks require proof of property insurance) and the overall economic expansion experi- enced by the Czech Republic over the last 10 years. The level of catastrophe insurance penetration among SMEs is considerably lower as enterprises can opt out of natural perils coverage and those located in relatively low flood exposure zones do so. 126 A World Bank Study Most property insurance policies are typically with a small deductible of about 2 percent. The sum insured for natural hazards is the same as for FLEXA perils and is typi- cally established at the time of policy issuance. However, the insured limits are typically small for most of the existing policies (around US$18,405–US$30,675)4 due to several fac- tors. First, non-life policies in the Czech Republic are commonly issued for an indeï¬?nite term or for an annual period with an automatic renewal. This means that the terms and conditions of coverage remain the same until the policy is cancelled, or a policy-holder requests a new insured limit, or agrees to switch from the currently prevalent book- value claims se lement to a replacement cost approach which will considerably increase the insured limit. The main reason behind homeowners’ reluctance to switch to a replacement cost approach is a considerable increase in annual insurance premiums entailed by such a change. Also, as a result of the August 2002 floods, some insurers limit flood coverage to 20-50 percent of the total sum insured, which limits the market average for overall individual coverage limits. While floods are unlikely to cause complete property loss for most homeowners, such low limits of coverage are likely to trigger considerable under- insurance penalties in case of a major claim, and in the case of loss, claims are se led by insurance companies’ own loss adjusters. As 75 percent of the residential property insurance market is controlled by only two companies—Generali and Kooperativa (VIG), competition on residential property rates for the existing business is rather weak, which enables insurers to charge actuari- ally sound rates for the coverage and remain proï¬?table. The situation with the rates was however dramatically different before the major floods of 1997 and 2002, which served as a major catalyst for re-pricing the flood risk by the market. After the 2002 floods, the premium rates have been increased by 400 percent and currently remain at around 250 percent level of the pre-2002 flood rates. A summary of flood premium rates charged by the market is presented in table 4.4. Despite the fact that at least 20–25 percent of risk premium is paid back to insurance agents in the form of sales commissions, these rates still compare rather well with other flood prone countries in Europe, such as Poland, where the premium rates are driven pre- dominantly by competition rather than by actuarial risk models. Virtually, all homeowners policies are sold through companies’ own sale force, agents, or bank-assurance channels. Since the 2002 floods the risk management capabilities of the local market have been transformed and are now on par with best international practice. Most companies adopt- Table 4.4. Flood Premium Rates in Different Flood Risk Zones HHs and SMEs premium Commercial and industrial Flood zones (return periods in years) rates (per mille) premium ratesa (per mille) Zone I (risk of flood is highly remote) 0.4 0.7 Zone II (50 or above) 0.8 1.4 Zone III (20–50) 2.0 3.0 Zone IV (<20) — — Source: Country regulators and industry; V-4 countries. Note: — = not applicable. a. Commercial and industrial are deï¬?ned as policies with insured limit in excess of Kcz 100 million. Financial and Fiscal Instruments for Catastrophe Risk Management 127 ed highly sophisticated risk accumulation control systems that enable them to link each insured property to a geo-code and hence trace their risk accumulations in real time. In addition, due to the efforts of large international reinsurance brokers (Aon Benï¬?eld) and reinsurers (Swiss Re and Munich Re), the local insurance market now has rather accurate flood risk modeling tools which are used to determine companies’ probable maximum loss (PML) potentials by risk accumulation and at the portfolio level. Model generated estimates of risk are used by companies to determine the amount of reinsurance they need to protect themselves against severe catastrophic events. Cur- rently, most companies buy reinsurance protection with the view of surviving a 1-in-200 or 250 loss scenario. This roughly comes to about a EUR 1.5 billion insured loss for the whole Czech market. In comparison, the 1997 and 2002 floods caused insured losses of US$305 billion and US$1.13 billion, respectively. To reduce the uncertainty of risk mod- eling estimates large companies use at least 2 external models, as well as their own. One company recently hired the Prague University to review major risk models currently in use by the market to validate the assumptions and methodology employed by the external modelers. However, the above description of risk management capabilities of the Czech mar- ket applies only to large international insurance groups as the remaining few small do- mestically owned companies still do business the old-fashioned way. The problem of inadequate risk management capabilities in small insurance companies is further exacer- bated by the fact that they must aggressively compete on price with large players, which leaves them with insufficient premium for placing adequate reinsurance protection (for example, with reputable reinsurers and of sufficient quantity). The insurance market is regulated by the Czech National Bank (CNB). Despite the fact that the insurance supervision of catastrophe risk management in the market began only in 2006 it is currently rather advanced by international standards. In brief, compa- nies are required to submit their reinsurance programs along with estimates of net catas- trophe risk exposure derived from internal risk models. The regulator requires at least a 200-300 year PML estimate for the whole risk portfolio along with the calculation of ag- gregate company’s risk retentions under all reinsurance treaties. A ratio of net retentions arising from a 200-250 year flood event to solvency capital should not exceed 2-5 percent. From this year onwards, besides the aggregate retentions, the CNB will also be request- ing all sums insured by flood risk zones that were covered by reinsurance treaties. The CNB appears to be cognizant of the possibility of larger size catastrophe events that may threaten insurers’ solvency but deems it impractical to require the level of claims paying capacity in excess of those required by 1-in-250 year events. The govern- ment appears to be clearly commi ed to provide necessary ï¬?nancial assistance to the insurance industry in the case of such an unlikely catastrophic scenario. During the 1997 floods, for instance, the government paid claims for an insolvent locally owned insurer, whose claims paying capacity was clearly insufficient to survive such a catastrophic event. The CNB also plans in the very near future to have the technical capacity to validate internal companies’ catastrophe risk models. To this effect, the CNB is in the process of establishing a special risk-based solvency group with advanced quantitative capabilities. 128 A World Bank Study Government Post-disaster Safety Nets According to the Czech Ministry of Finance, the existing regulatory framework provides for two types of budgetary emergency allocations in the aftermath of natural disasters. These are as follows: â–  Budgetary allocations for emergency and immediate measures aimed at rescue and health protection of affected population. To this effect, according to Regula- tory Acts No. 239/2000 Sb. and No.240/2000 Sb, the government makes an annual CZK 100 million allocation in the state budget under the chapter of Public Trea- sury Administration. Mentioned ï¬?nancial aid can reach ultimate beneï¬?ciaries within hours if necessary, as was demonstrated during the 2002 flood. A similar structure of emergency ï¬?nancial aid exists at the level of local administrations. â–  Budgetary allocations for property reconstruction and revitalization. There are several short to medium time programs managed by the Ministry of Industry and Trade and the Ministry for Regional Development which allocate govern- ment post-disaster ï¬?nancial aid for the purposes of reconstruction of destroyed property in the form of interest-free loans to municipalities, ï¬?rms and house- holds. This type of assistance is based on Acts No. 12/200 Sb and No.186/2002 Sb.—titled “State Aid for Territorial Restoration.â€? This assistance is funded by a 0.3 percent annual state budget allocation. In 2009, this amount was CZK 3.5 billion. The speed with which assistance is delivered to ultimate beneï¬?ciaries depends on individual case by case evaluations of the needs of aid applicants.5 Disaster reconstruction can also be funded from the following additional sources: â–  Insurance indemnities (Act No.: 218/2000 on Budget Rules explicitly provides for the possibility of insuring state property); â–  Local and regional budgets; â–  State budget chapters (that is, government departments) by the savings or inhibi- tion of some governmental expenditure of the current year; â–  State budget by the global or selective reduction of the expenditures of state bud- get chapters of the current year based on the government decision and Parlia- ment approval in certain cases (so called government packages); â–  Proceeds from the privatization of state property (for example, in 2005, the priva- tization of ÄŒEZ and OSINEK generated CZK 5 billion for the state treasury); â–  Issuance of state bonds as stipulated by Act No.: 163/1997. For instance, in the aftermath of the 1997 flood, in 1997–98, the Ministry of Finance issued CZK 5 bil- lion of bonds to ï¬?nance reconstruction and rehabilitation efforts; â–  Issuance of bonds and other debt obligations by the regions and local authorities; â–  Commercial loans. Poland—Catastrophe Insurance Market Overview Country Disaster Risk Proï¬?le Although the country is exposed to a variety of natural perils, flood is by far the most common and signiï¬?cant. Of all historical events, the 1997 Odra river flood was the most devastating to the country and the insurance industry. The overall economic losses caused by the event were US$3.7 billion, with most of them uninsured. Although large Financial and Fiscal Instruments for Catastrophe Risk Management 129 floods in the Odra River and its tributaries are rather frequent—in the nineteenth centu- ry, four major floods were recorded in 1813, 1829, 1854 and 1880, while in the twentieth century 12 large floods were recorded—the 1997 event was the biggest on record. The July 1997 flood was caused by extremely heavy rain, with some meteorological stations recording as much as 400 millimeters over a four-day period, which was four times of the long-term average. A brief summary of the most recent floods is presented in table 4.5. Table 4.5. Most Signiï¬?cant Floods in Poland (1997–2008) Year Date Description of event Economic impact 1997 July/August Devastating floods in July and August 1997 affected large The economic loss was areas of central Europe. In Poland, a total area of 31,000 estimated at US$3.7 bln in square kilometers, about 10 percent of the country, was Poland, and the insurance affected, with the loss of 2,000 km of railway line, 3,000 km of industry bore losses of over roads, 900 bridges and 100,000 houses. More than 50 people US$225 mln. died. The flooded areas extended along the rivers Odra, Nysa and Wisla together with their catchment areas. 2001 July/August Beginning 9 July, torrential rainfall and consequent flooding The economic loss was affected southern Poland. The hardest hit areas were the estimated at US$700 mln. cities and towns along the Vistula river, where 300 hectares of land were flooded. Bridges, sewage systems, water and gas supplies, houses and livelihoods were destroyed. Some 25 people died. 2005 March Flooding caused by melting snow and heavy rainfall. No signiï¬?cant damage was Tributaries of the Odra river burst their banks near the south- reported. western city of Wroclaw, and sections of the main road from Warsaw to Gdansk were cut. 2006 March The central European floods of March 2006 mainly affected No information. agricultural land. 2006 August Rain caused severe flooding in the south-west. Insurance market losses were estimated to be below PLN 40 mln (US$12.8 mln). Source: AXCO Country Report, 2008. Note: bln = billion; mln = million; km = kilometers. The existing hazard risk models offered by major reinsurance brokers imply an in- sured market loss of about US$2 billion from a 1-in-200-year event—an event of severity of the 1997 flood. Poland is also exposed to wind storms, although the risk is relatively low compared to that of floods. In comparison, a 1-in-200 wind storm is likely to cause only 1/10 of economic damage expected from a similar frequency flood. Hazard Insurance All natural perils, including flood, are automatically covered by a homeowners’ policy offered by most local insurers. The most common design of the insurance policy simply does not provide for an option to opt out of natural hazards coverage. For agricultural buildings, property insurance cover (including that for flood cover and other acts of god) is obligatory,6 but the legislation is not well enforced and many risks remain unin- sured. As a result of increased flood risk awareness and a growing number of mortgage- ï¬?nanced properties among newly constructed dwellings, 56 percent of homes in Poland are insured today against natural perils. This can be considered quite an achievement for a country where insurance coverage was quite low even 15 years ago. 130 A World Bank Study Figure 4.1. Catastrophe Insurance Coverage Penetration among Homeowners Catastrophe Insurance Penetration 1996–2008 9,000,000 60 8,000,000 50 7,000,000 Insured dwellings (%) Insurance policies 6,000,000 40 5,000,000 30 4,000,000 3,000,000 20 2,000,000 10 1,000,000 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year Source: Authors, based on the data provided by the Polish Insurance Regulator, 2009 The potential for expansion is noteworthy due to the growing segment of mortgage- ï¬?nanced construction, which is 100 percent insured due to the banks’ requirements. In 2007 alone, Poland’s central bank (NBP) reported that mortgage lending increased by 50.4 percent to PLN 116.84 billion (US$41.1 billion), which mirrors the signiï¬?cant growth experienced in prior years. As can be seen from ï¬?gure 4.1, today 7.7 million Polish households (or 56 percent of homeowners) have property flood coverage. This compares to less than 2.5 million policies in 1996. Typically, companies do not limit sums insured for flood coverage under the of- fered policies. As a result, most of homeowners’ policies do not have flood sublimits, except for the areas that are highly exposed to floods. Due to the increasing competi- tion for the residential property business, which still remains highly proï¬?table despite a considerable increase in the number of market players, premium rates are rather low by international or even Central European standards. The basic additional premium rate for flood cover historically has been 20 percent of the basic ï¬?re rate, but in the current extreme competitive climate coverage is often included in a global rate. Nevertheless, with an average loss ratio of 40 percent, the residential property business remains highly proï¬?table. For instance, PZU, the biggest player in the market has been offering all-perils prop- erty covers (for example, FLEXA and NATCAT) for less than 0.1 percent of insured value, as compared to 0.25 percent in the neighboring Czech Republic. The average premium generated per risk is around PLN 99 (US$44) each for buildings and contents. Despite Financial and Fiscal Instruments for Catastrophe Risk Management 131 the low pricing, the company remains highly proï¬?table and appears to have sufficient claims paying capacity (comprising reinsurance, own reserves and surplus) to survive a 1-in-200 year flood. PZU’s ability to maintain such low pricing without losing its proï¬?t- ability can be mainly explained by its country-wide diversiï¬?cation of the risk and a very large share of the market it commands. Also, for reasons of competition, most policies in Poland have very low deductibles. In the case of PZU, for instance, deductibles on a residential property insurance policy are EUR 100, but even then, they are applied only if the loss is below that amount. De- ductibles for flood and other natural perils are usually the same as for the main FLEXA policy. Perhaps, one of the key limitations of the current insurance practice is the low in- sured limits that rarely approximate the true value of insured dwellings and contents. The average amount of cover for contents is PLN 15,000 (US$6,700) and for buildings PLN 130,000 (US$57,800). Insured limits under most existing policies are set based on the depreciated book value. However, newly issued policies use the current market val- ue rather than the historic replacement cost as a basis for claims se lement. These terms of coverage cannot be changed unless a policy-holder requests it, which most policy- holders are reluctant to do because of a potential increase in premium. Hence, in the case of complete loss of a dwelling, most homeowners will face severe underinsurance penalties—as the rule of “averagingâ€? is quite common. As opposed to the residential property segment, most Polish owned SMEs do not buy any insurance, though awareness is increasing. Companies with foreign capital are generally be er insured and protected. Government property risks in many cases are insured on wri en-down book values, and not on current or replacement values. Although the Polish insurance market does not have a commonly shared flood risk model or high resolution flood risk maps, most companies use their own proprietary accumulation control systems, which enables them to underwrite, price and monitor residential risk accumulations in flood-prone areas by postal code. For corporations, address based monitoring is used. The PZU, for instance, uses a customized version of MapInfo program for monitoring its own risk aggregates. For instance, according to S&P’s 2006 credit report, the PZU estimates its probable maximum loss to be between PLN 860 billion (US$382 billion) and PLN 920 billion (US$408 billion) based on a one-in- 200-year event. There are several commercial flood risk models in the market which are offered by large reinsurance brokers (Aon Benï¬?eld, Guy Carpenter), reinsurers (Swiss Re), and risk modeling companies (RMS). All these models allow monitoring flood risk aggregates at least by postcode and making estimates of probable maximum loss from events with dif- ferent return periods. It is not uncommon for large companies to use several models at the same time to reduce the level of uncertainty in their estimates of the required claims paying capacity, and ultimately, the amount of reinsurance protection needed. Local companies’ risk management efforts are further stimulated by the local regulatory re- quirements to report the 10 biggest risks as well as by the accumulation control require- ments from the international rating agencies, which are concerned about companies’ excessive risk exposure to catastrophic risks. Due to the growing risk retention capabilities of the local market—mainly due to the market consolidation and buy-outs of locally owned companies by large foreign 132 A World Bank Study insurers—the amount of reinsurance placed with foreign reinsurers has been declining in relative terms (for example, as a percent of gross premium wri en). Nevertheless, for smaller size insurers, reinsurance provides most of claims paying capacity in catastroph- ic events as their retentions remain rather insigniï¬?cant—5-10 percent, vs. 98.5 percent in the case of the PZU. The growing risk retentions by larger local companies are partially driven by the regulatory allowance for claims equalization reserves. The reserves must be set up by all companies in operation for ï¬?ve years and over. Retentions on any one risk must not exceed 25 percent of total technical reserves and own capital, although the regulator can approve a higher ï¬?gure in justiï¬?able cases. While there are no speciï¬?c catastrophe risk related regulatory requirements; as part of Solvency II calibration tests (QIS IV), the Insurance Regulator asked companies to include in their solvency calculations catastrophe risk scenarios for flood and hurricanes with a 200-year return period. Further market stress tests are planned by the Regulator this year, which inter-alia will include major catastrophe risk scenarios and validation of the modeling methodology used by insurers. Since the 1997 floods, the government has been trying to limit land development in flood prone areas and has been investing heavily into flood protection infrastructure. For instance, in March 2007, the country borrowed US$189 million from the World Bank for the Odra River Basin Flood Protection Project. The main development objective of the project was to protect the population in the Odra basin against loss of life and damage to property caused by severe flooding. This will be achieved by reducing the extreme flood peaks through storage in a dry polder on the Odra river, just upstream of Raciborz town, enabling a reduction of the flood peak downstream of the reservoir, and by increas- ing the flood carrying capacity of the Odra river channels through and around Wro- claw. The project would protect more than 2.5 million people in towns such as Raciborz, Kedzierzyn, Kozle, Krapkowice, Opole, Brzeg, Olawa and Wroclaw, and se lements in the three vovoidships of Slaskie, Opolskie and Dolnoslaskie. Government Post-disaster Safety Nets Since 1997, the government has allocated PZN 753 million (EUR 171 million) in its an- nual budget for emergencies. The funds can be used for (a) disaster risk prevention ac- tivities, such as flood protection works, (b) liquidation of property damages caused by natural disasters through ï¬?nancial assistance to local governments for housing or infra- structure reconstruction, and (c) post-disaster assistance to individuals. The la er can be eligible for small grants (up to EUR 1,500) to pay for living expenses incurred due to natural disasters or housing reconstruction grants. Although there is no official limit to the size of individual reconstruction grants, the ultimate decision on the individual eligibility and the amount of reconstruction as- sistance rests with local government officials. There appears to be a considerable room for government discretion in making such decisions as the eligibility for government assistance is linked to the demonstrable deterioration of living conditions as a conse- quence of a natural disaster. On average it takes up to 1 month for the funds to reach the beneï¬?ciaries, which is relatively quick by international standards. Currently, the Ministry of Interior, which is in charge of allocating post-disaster aid in Poland, is working on the ï¬?rst draft of a compulsory catastrophe insurance law, which will make flood insurance compulsory for all homeowners in the country. At the mo- ment, only rural dwellings are subject to this requirement. Under the proposed plan, the Financial and Fiscal Instruments for Catastrophe Risk Management 133 two state-owned companies—PZU and Warta- will offer a stand-alone flood insurance policy to homeowners, which will be made compulsory by the proposed catastrophe insurance law. The risk will be then partially retained by the companies and partially reinsured in the international reinsurance market. No special risk pooling mechanism is envisaged under the plan. The Slovak Republic—Catastrophe Insurance Market Overview Country Disaster Risk Proï¬?le The Slovak Republic is subject to flood risk. It has minimal exposure to earthquake risk, and is not considered to be an area for that particular hazard. The four seismic stations in the Slovak Republic associated with the Geophysics Institute occasionally record weak tremors below Richter level 4. The latest recorded earthquake occurrence was registered in the eastern Slovak Republic on 21 May 2003. The tremor measured 4.2 Richter and damaged more than 120 of the 135 houses in one village. The insured damage is not known, but Allianz Slovenska stated that some policies only pay when the tremor ex- ceeds Richter level 5 or 6. Traditionally the Slovak Republic was considered a greater flood risk than the Czech Republic. Floods occurred when rivers overflowed due to water from melting snow or exceptional rainfall and endangered areas along the river valleys in the lowlands. How- ever, after the disastrous floods in 1964, a system of dams and dykes was built on the Danube in the Slovak Republic and Hungary in order to prevent the recurrence of a simi- lar event. The small losses in the Slovak Republic in the summer of 2002 seem to indicate that the flood control system is now quite effective and that the country’s hazard risk proï¬?le has been reduced considerably. Floods following heavy rain have occurred in recent years, but damage has been signiï¬?cantly lower than in Austria or the Czech Republic. In July 1998, flooding rivers devastated villages and Romany camps in both the Czech and the Slovak Republics. In the Slovak Republic, this resulted in the loss of at least 44 lives. Around 2,500 insurance claims were ï¬?led and approximately SKK 54 million (US$2.81 million) claims were paid. In 1999 and 2000, there were more occurrences of floods and severe rain damage. These events were less severe than the 1998 floods. Areas in the eastern Slovak Republic with low concentration of domestic or commercial risks were the most affected. The di- sastrous floods of the summer of 2002 caused greater devastation in the Czech Republic and other countries than in the Slovak Republic. Bratislava was in a state of emergency for eight days in August, however. Preventive measures including sandbags and evacu- ation were effective, and there was only one fatality. It was the highest water level in Bratislava for 65 years. The Slovak government estimate of total economic damage from that event and ear- lier floods in March and July was SKK 1.8 billion (US$93.654 billion). Insured damage was relatively light as many affected dwellings were not insured: the insurance associa- tion estimated a market loss of SKK 140 billion (US$7.28 billion). In 2004, the National Contact Centre for Civilian Security registered nine flood events, the biggest ones in July in the Kosice and Presov areas. The insured loss was insigniï¬?cant. Aon Benï¬?eld (a reinsurance broker) has the only fully probabilistic model in the country, which has been available since 2003. They also help clients to calculate their probable maximum losses for insured portfolios for the purpose of placing reinsurance 134 A World Bank Study coverage. Despite the increasing competition, so far the average market loss ratio for property has been rather low as the Slovak Republic typically remains unaffected by windstorms such as Emma which frequently cause considerable property damages in Austria and the Czech Republic. A full probabilistic flood model has existed. Hazard Insurance In contrast to Poland and the Czech Republic, where property owners cannot opt out of natural perils coverage, in the Slovak Republic coverage for all major natural perils, including flood, earthquake, hail, wind, burden of slow and landslide is optional and can be obtained for an additional premium in addition to the standard insurance pack- age of FLEXA perils. As a result of this consumer discretion over the scope of coverage, over 30 percent of insured homeowners opt out of the catastrophe insurance coverage. A similar percentage of SMEs—30 percent—with general property insurance coverage do not have optional flood and earthquake endorsements. Yet, the estimated level of catastrophe insurance penetration in the Slovak Republic is still relatively high—about 51 percent of all homes (for example, 0.952 million dwell- ings) are covered against catastrophic perils. This can be considered quite an achieve- ment for a country where insurance coverage was quite low even 20 years ago. There is potential for further expansion due to the growing segment of mortgage-ï¬?nanced construction, which is 100 percent insured due to lenders’ requirements. Deductibles for natural perils are virtually non-existent or very small. Instead, since the floods of August 2002, insurers typically use sublimits of about of 20-30 percent of sums insured under the FLEXA policy. Some insurers check the loss history for new risks and exclude flood altogether if a risk had a flood loss within the last 10 years. Replacement is the basis for indemnity of property damage most of the time; though very occasionally cover is placed on a “book valueâ€? basis. No indications of premium rates are available, since they can vary widely depending on the area where flood coverage is requested. Insurers track the flood risk mainly for insured properties in low-lying areas close to rivers and for the earthquake risk by Catastrophe Risk Evaluating and Standardizing Target (CRESTA) zone. Following the 2002 floods, many companies are now looking into acquiring more sophisticated flood risk accumulation control tools. In response to the growing interest from the market in flood modeling tools, Aon Benï¬?eld has produced a flood reinsurance model mainly to calculate aggregate risk exposures and probable maximum losses from events with different return periods. Swiss Re has been work- ing with the Slovak Insurance Association and a software company to develop a rating model for floods in the Slovak Republic, which can also allow monitoring aggregate risk exposures and estimating probable maximum portfolio losses. The model is very similar to that developed by the company for the Czech Republic. Large companies appear to track their aggregate accumulations and use modeling tools to determine their PMLs. A return period of 250-years is used for internal risk man- agement purposes in calculating insurers’ own risk exposures. The existing Aquarius model now allows calculating probable maximum portfolio losses in each of four flood zones for different return periods. It has been almost 50 years since there was a major flooding incident when the Danube burst its banks. Since then a dam has been built to contain any future loss exposure. In response to the flood management regulations, a national program has been set up to reinforce flood prone areas and build dykes and dams where necessary. Financial and Fiscal Instruments for Catastrophe Risk Management 135 Government Risk Financing The Ministry of Environment of the Slovak Republic is the main government body re- sponsible for flood control. The budgetary appropriations for flood management and post-flood recovery are made under the budgetary chapter VÅ¡eobecná pokladniÄ?ná správa (General Treasury Administration) which is administered by the Ministry of Fi- nance. The Law of the National Council of the Slovak Republic Nr. 42/1994 Z.z. (and in the wording of subsequent directives) regarding the civil protection of the population deï¬?nes an extraordinary event as natural disaster, accident, catastrophe or terrorist at- tack. Since the time the law has been adopted, flood is always classiï¬?ed as a natural disaster. The Ministry of Environment is responsible for preparing an annual flood dam- age report. The report serves as a basis for budgetary allocations for rescue and relief work as well as for post-disaster reconstruction and prevention. The government disas- ter risk ï¬?nancing mechanism typically involves a budgetary transfer from the Ministry of Finance to the Ministry of Environment (or other government agencies involved in disaster relief or rehabilitation work), which in turn make the funds available to the ï¬?- nal beneï¬?ciaries: municipalities, regional environment administration offices, and state- owned hydropower stations. The regional offices of the Ministry of Environment further allocate the funds to villages and private citizens. The existing national legislative framework for disaster risk management and ï¬?- nancing is described in the following legislation: â–  Law Nr. 666/2004 on flood prevention; â–  Decree Nr. 386/2005 issued by the Ministry of Environment on monitoring flood related damages and undertaken response measures; â–  Decree Nr. 387/2005 by the Ministry of Environment evaluation of damages and compensation of flood-related damages. Typically, it takes at least half a year to get the aid to the flood victims. The Minis- try of Environment does not provide any additional resources besides those envisaged under the above described budgetary allocation from the Ministry of Finance. The provi- sion of post-disaster subsidies is not contingent upon availability of private insurance. Also, there is no maximum pre-set in advance amount of ï¬?nancial assistance to victims of disasters and no means testing requirements. Hungary—Catastrophe Insurance Market Overview Country Disaster Risk Proï¬?le Hungary’s risk exposure to natural perils is by-and-large limited to flood and earth- quake. However, the country’s overall economic exposure to these perils is rather mod- erate. Hungary’s exposure to earthquake results from the compressive motions of the Eurasian and African plates, which elevated the Carpathian Mountains to the north and east of the great Hungarian Plain (see table 4.6 and ï¬?gure 4.2). A relic of past crustal movements can also be seen in the long-extinct volcanoes on the northern shores of Lake Balaton. Earthquake is regarded as a minor hazard by most of the local companies, though the risk appears to be under-estimated and therefore under-rated. The local market works on the assumption of a moderate earthquake every 30 to 35 years and a severe earthquake every 90 years. 136 A World Bank Study Table 4.6. History of Sizeable Earthquakes in Hungary Date Area Magnitude Intensity 1978 Bekes — 6.5 1956 Dunaharaszti 5.1 8.0 1956 Pakscz 4.2 6.0 1953 Ukkturje 4.0 6.5 1951 Tereske 4.4 7.0 1942 Bekonybel 3.6 6.0 1942 Tapiosuly 4.1 6.0 1939 Almoso 4.5 5.5 1939 Eger 3.0 6.0 1937 Tarcal 4.2 6.0 1934 Bucsuszentlaszlg 4.5 6.5 1931 Beregdaroc 4.0 6.0 1930 Cserharsurame 3.9 6.0 1927 Varpalcta 3.4 7.0 1927 Varpalcta 3.7 7.0 1925 Eser 5.2 8.5 1925 Naskanizsa 4.3 6.5 1922 Pecs 3.8 5.5 1917 Gasztcny 4.0 6.0 Source: AXCO, 2009. Note: — = not applicable. There is an earthquake construction code which applies to concrete slab and steel frame and concrete buildings. Most buildings in Hungary either pre-date the code, how- ever, or are not covered by it. There is said to be a Richter 5 event every year, but dam- age is usually negligible. The last signiï¬?cant event was in 1985 in the area north of Lake Balaton. The estimated loss was HUF 200 billion (approximately US$2.4 billion) falling mainly on the household account. Recorded events in excess of intensity 5 from 1917 onwards are listed below: Hungary’s risk exposure to floods arises from the presence of two main rivers on its territory—Tisza flowing through Szeged, and the Danube that flows through Budapest. The country’s flood plain areas are estimated at 5,450 square kilometers, out of which 900 square kilometers were flooded in 2006 (table 4.7). In the more distant past Hungary suffered a number of catastrophic floods, two of the most notable being the Danube flood of 1838 which destroyed Pest and the Tisza flood of 1879 which destroyed Szeged. Such major events have been largely eradicated, however, by river improvement schemes and the building of dikes to create flood basins along the courses of the major rivers. The success of these measures was dramatically proven in April 2006 when the Danube rose to its highest level for nearly 150 years but caused almost no insured flood losses. The river Tisza in the east of the country pro- duced severe floods in March 1999, 2000, and 2001 but insured losses were low. Financial and Fiscal Instruments for Catastrophe Risk Management 137 Figure 4.2. Earthquake Map of South Eastern Europe Source: Swiss Re. Table 4.7. Hungary’s Flood Exposure Hungary (km2) Size of morphological plaina 5,450 Size of recent floodplain 900 (loss in %) (85%) Flooded in 2006 882 Artiï¬?cial polder opening 0 Potential restoration area 700b; 80c Flooded potential restoration area 60d (% of total potential restoration area flooded) (80%) Source: AXCO Country Report, 2009. Note: km2 = square kilometers. a. Floodplain only, without groundwater influenced areas. b. Vasarhelyi Plan. c. Bodrog mouth. d. Novi Becej, and Bodrog mouth identiï¬?ed in GEF study. 138 A World Bank Study Hungary’s floods are normally caused by rainfall and snow-melt in the Carpathian and Tatra mountains, outside Hungary itself. The most exposed areas are the northern reaches of the Tisza where it enters Hungary from the Ukraine, the Koros where it en- ters Hungary from Romania, and the southern reaches of the Tisza after its confluence with the Koros. There is also a risk of flooding from the Danube south of Budapest. Al- together, an estimated two million hectares of land are exposed to 1:100 year flooding. Between 1999 and 2001 there was a marked increase in severe flood incidents in the east of the country. Flooding on the upper reaches of the Tisza in 1999 was regarded as a 1:100 year event, and yet the flood level in March 2000 was nearly 20 centimeters higher. In a fur- ther incident in March 2001 the level of the Tisza rose by 8 meters in a day as a result of melt-water from the Carpathians coinciding with unusually heavy rains. This increase in flood incidents is said to be the result of deforestation in the Tatra Mountains increasing the amount of rainwater run-off. Flood waters draining from central Europe in August 2002 and April 2006 raised the Danube to dangerously high levels, but the embankments in Budapest held and insured flood losses further down-river were negligible. As a re- sult of recent incidents the government has initiated a 10-year flood improvement plan for the Tisza. Overflow channels are being dug and dikes heightened to 1m above the level of the highest recorded flood. The local insurance market however does not see the flood PML as being signiï¬?cant, even in the event of the Danube and Tisza rivers flooding simultaneously. One reason for a low PML is the fact that the most flood-prone rivers, the Tisza and the Koros, flow through agricultural areas where insurance penetration is low and there is relatively li le industrial development. Insurers have also taken comfort from the fact that recent floods have been relatively localized and have affected different stretches of the Tisza valley each year. In 2001, for example, only four villages were inundated. Insurers are also protected by restrictive policy wordings, which exclude non-flood-protected prop- erties, and by speciï¬?c underwriting measures, such as the exclusion of properties of mud brick construction. The overall insured loss statistics for the last decade are summarized in table 4.8 below. Table 4.8. Insured Flood Losses Flood date Total losses Insured losses March 1999 HUF 82 bln/US$345.8 mln Not known March 2000 Not known HUF 500 mln/US$1.8 mln March 2001 HUF 23 bln/US$80.3 mln HUF 3.2 bln/US$11.2 mln Source: AXCO Report, 2009. Note: bln = billion; mln = million; km = kilometers. Most damage was caused to roads, bridges and village houses. A surprising feature of the floods was the number of total loss buildings claims. These arose because many rural homes are built of mud brick which can become saturated and unstable after pro- longed exposure to standing water. Hazard Insurance At the time this analysis was being prepared there were 30 insurance companies su- pervised in Hungary, of which 10 were non-life, 10 life and 10 composite. A total of 11 Financial and Fiscal Instruments for Catastrophe Risk Management 139 branches have been established by EU insurers on a freedom of establishment basis, though most of these are inactive. Non-life insurance is also wri en by 34 insurance as- sociations which are mainly active in agriculture and motor. Every insurance company except the state export credit insurer is foreign-owned. The market is dominated by Al- lianz Hungaria, which retains a market share of around 35 percent. An unusual feature of the Hungarian property insurance market is the high level of penetration of household business. According to our estimates, in 2009 the local insurers held around 2.9 million residential property policies in their portfolios, which represents a penetration rate of over 70 percent of households. OTP Garancia, Generali-Providen- cia, Aegon and Allianz Hungaria have all in excess of 500,000 household policies on their books, which accounts for a high proportion of their catastrophe accumulations. As catastrophe insurance coverage is typically offered by the local market as part of the all-risk property policy at a very competitive rate, very few homeowners (around 5 percent) opt out of it. In fact, it appears that such opting out of the natural hazards coverage is only possible in the case of smaller insurance companies which use this fea- ture to remain competitive. Such a high level of property insurance coverage among households can at least be partially explained by the mandatory insurance coverage re- quirement introduced by local banks for all new mortgage borrowers. In response to the government interest rate subsidies for new mortgage borrowers, the mortgage lending market grew rapidly between 2002 and 2006, hence driving up the level of property insurance penetration among the Hungarian homeowners. The recent phasing out of mortgage subsidies has reduced the amount of household new business, and insurers are now trying to increase their premiums per policy by selling assistance, legal protec- tion, life and personal accident riders. A typical comprehensive policy includes the perils of ï¬?re, lightning, explosion, air- craft, storm, hail, weight of snow, landslide, collapse of underground cavities, impact by vehicles, cloudburst, flood, earthquake, burst pipes, burglary and glass breakage. Flood wordings generally exclude damage to houses built of mud brick and houses situ- ated between river banks and flood protection dikes. Policies are normally issued on a reinstatement basis and sums insured are index-linked. Many policies specify the level of security protections required for varying sums insured. “Condominiumâ€? policies are available to provide collective insurance on the buildings of apartment blocks. The earthquake peril includes earthquake ï¬?re, landslip and collapse following sub- sidence. Most companies deï¬?ne earthquake as “shaking reaching the ï¬?fth degree on the MSK-64 scale,â€? and thus avoid liability for damage to buildings with inadequate earth- quake resistance. Although there are virtually no deductibles, earthquake is insured ei- ther on a replacement or cash value basis and with a sublimit above the sum insured speciï¬?ed in the contract—typically up to US$25,000, for example, if aggregate losses from flood or earthquake exceed that amount, individual property damage claims are scaled down proportionately, based on sum insured. While earthquake insurance is almost universal for foreign-invested enterprises and householders, penetration is lower for Hungarian enterprises, but it is still estimated that 80 percent of industrial risks are covered against earthquake. The survey of the market for this analysis indicates that there are about 300,000 all-risk property insurance covers for SMEs in Hungary. Household earthquake rates are the same across Hungary, whereas industrial rates can vary by region. Because of intense competition, most insur- 140 A World Bank Study ers charge inclusive ï¬?re and perils rates with only a minimal allocation of premium for earthquake. In addition to earthquake, all-risk residential property policies cover two types of flood, namely cloudburst and flood. Cloudburst is deï¬?ned as damage caused by stand- ing water resulting from excessive rainfall, excluding the effects of rising ground water. Flood is deï¬?ned as the “overflow of any permanent or seasonal, natural or artiï¬?cial waterways, lakes, ponds or reservoirs which inundates flood-protected areas.â€? The limi- tation to flood-protected areas effectively excludes properties located in the flood basins between riverbanks and dikes, as well as properties which are not protected by dikes or embankments. Insurers generally survey flood-exposed properties within 15 km of a river and ex- clude those for which the risk is unacceptably high. Some companies automatically de- cline risks in areas around the upper reaches of the Tisza which were affected by flood- ing in 1999 to 2001. Others take the view that since the floods occurred in different places in different years, the average incidence of risk along the Tisza remains acceptable. Some insurers decline mud-brick houses in flood-prone areas because prolonged exposure to standing water can lead to structural failure. Similarly to earthquake, flood is insured on a replacement value basis, with losses subject to a sublimit above the maximum loss threshold speciï¬?ed in the insurance policy. It is estimated that approximately 95 percent of household policies and 80 percent of commercial policies are extended to include flood. Flood is included in the overall special perils rate, which is the same across the country. A Law on Social Flood Insurance (popularly known as the Miklos Wesselenyi Law after a nineteenth century Hungarian river regulator) was passed in August 2003. The law authorized the establishment of a state-backed flood insurance fund for the beneï¬?t of homeowners who are unable to obtain commercial flood insurance because their proper- ties are situated in unprotected flood plains. The fund provides maximum cover of HUF 15 billion (US$87,057) per property and compensation is available to any eligible hom- eowner who elects to pay premiums. The fund is administered by the state and there is no involvement by the commercial insurance sector. The main purpose of the Miklos Wesselenyi Flood Protection Fund is to save the government from having to make ex gratia payments to uninsured flood victims. Be- cause many of those who live on unprotected flood plains are poor farmers with low insurance consciousness and below-average incomes, the number of people buying pro- tection from the fund is virtually zero. It therefore seems that the main beneï¬?ciaries of the scheme are the insurance companies, which will no longer be under political pres- sure to underwrite flood risks in exposed areas. Rating levels are low, largely because of the need to remain competitive. A typical buildings rate for an all-perils-policy, which would be applied throughout Hungary, is 1.06 percent. Some companies however make an effort to charge more. Contents rates are 1.5 percent in the country but up to 3.6 percent in Budapest because of the higher burglary risk. Policies are normally issued with nil deductibles. The average household premium in 2006 was HUF 25,000 (US$125) and of earthquake cover HUF 2,000 (about US$10). Average limits reach US$100,000 for residential and US$400,000 for commercial properties. Financial and Fiscal Instruments for Catastrophe Risk Management 141 Risk accumulations are monitored on a postcode basis. All companies are said to base their catastrophe PMLs on a severe earthquake affecting Budapest, though there is no generally accepted earthquake model for Hungary and therefore no consensus about the appropriate level of catastrophe protection. Some models produce PMLs which seem unrealistically high, while others produce PMLs which are unaccountably lower. Com- panies which have either produced or are working on Budapest earthquake models in- clude Munich Re, Equecat, Benï¬?eld Group and Aon Re. The companies are also required to report their risk aggregates for earthquake and flood to the local insurance regulator. Since most of the local companies are foreign owned, most of reinsurance is placed with the parent companies. Government Risk Financing According to the Ministry of Local Governments and the National Directorate General for Disaster Management, the government annually allocates HUF 3-4 billion (US$15–20 million equivalent) to the national Force Majeure Fund. The fund proceeds are used for the purposes of reconstructing government owned assets destroyed by natural disasters. The size of the fund can be increased by a government decree. In addition to the central FMF, all local and regional governments must be allocated at least 2-3 percent of their annual budgets for emergency situations. There are 19 counties and 3200 Local Govern- ment offices. Interestingly, government ï¬?nancial assistance to homeowners that have been adversely affected by natural disasters is not predicated upon proof of insurance. Typically, it takes 2–3 weeks from the declaration of a national disaster by the gov- ernment for the aid from the Calamity fund to reach disaster victims. Local govern- ment offices perform the role of a payment agent. The County Disaster management Office and insurance companies are involved in evaluating the extent of loss. There is no statutory maximum amount of ï¬?nancial aid to be paid per household and amount of assistance may vary signiï¬?cantly from one catastrophe event to another based on the government assessment of overall economic damages from a disaster. Unused funds in the National Calamity Fund can be carried forward in addition to the new budget. Conclusion Despite considerable risk exposure to natural disasters, the existing private risk ï¬?nanc- ing mechanisms in the countries of Central Europe are relatively well advanced to miti- gate the consequences of large catastrophic events. Several recommendations emerge from this analysis. They are intended to guide government policymakers in developing and applying national and regional disaster risk ï¬?nancing strategies, suggest ways in which specialists can be er address catastrophe risk ï¬?nancing in their dialogue with clients, and provide information and ideas that may be of value to other stakeholders, such as international donor organizations, NGOs, academics, and the general public. Lessening the impact of natural disasters on government budgets. Despite a rela- tively high level of insurance penetration in the CEE V-4 countries, governments still carry a considerable budgetary exposure to catastrophic floods. The current regulatory frameworks in the Czech Republic and Poland, for instance, make it a government obli- gation to assist homeowners in post-disaster recovery and reconstruction efforts. More- over, in the case of 1997 flood, the Czech government paid an equal compensation to 142 A World Bank Study both insured and uninsured homeowners for equity reasons. The post-disaster compen- sation was funded by additional government borrowing. This approach to disaster compensation does not seem optimal. In the case of di- saster compensation, governments should clearly ï¬?nd a way to separate between public and private liabilities. While the reconstruction of the former is clearly the government responsibility, the la er should be covered by private insurance. This is particularly the case when such insurance is widely available and quite affordable. To this end, the gov- ernments of the CEE V-4 countries may consider changing the existing post-disaster compensation policies for housing reconstruction by introducing a strong element of private responsibility for losses inflicted by natural disasters. Such a policy change is likely to not only considerably increase insurance penetration among homeowners but also will help signiï¬?cantly reducing government ï¬?scal exposures to natural disasters. As well, for purely public sector losses, governments should consider risk transfer mecha- nisms to provide additional budgetary support for mega disasters. Reducing the ï¬?nancial vulnerability of homeowners and SMEs to natural hazards. While this analysis documented rather high levels of catastrophe insurance penetration among homeowners and SMEs in the CEE V-4 countries, up to possibly 50 percent of the population still remains uninsured. In this context, the governments of CEE V-4 should consider investing in increasing public risk awareness as well as changing the post-di- saster compensation policy. In addition, those CEE V-4 countries which have a lower level of property insurance coverage among homeowners should consider introducing a stand-alone catastrophe insurance to homeowners and small business owners, backed by a dedicated reinsurance capacity at the country or in the case of smaller economies (such as the Slovak Republic) at the regional level. As has been demonstrated by the international experience, programs can provide highly affordable coverage by realizing the beneï¬?ts of country-wide risk diversiï¬?cation, economies of scale and the ability to obtain be er pricing terms from the global reinsur- ance market. The ï¬?rst country wide catastrophe risk pool in an emerging market known as the Turkish Catastrophe Insurance Pool (TCIP) was pioneered and successfully launched with Bank assistance in Turkey in 2000. As well, a Caribbean Catastrophe Risk Financing Facility (CCRIF) was established in 2007 to provide government ï¬?scal budget- ary support in the event of hurricanes or earthquakes for 16 country governments and territories in the Caribbean region. Work on a similar program for the countries of South Eastern Europe and the Caucauses has reached a fairly advanced stage, and this initia- tive, the SECE CRIF, may enable insurers selling a stand-alone catastrophe insurance cover to receive access to a dedicated reinsurance capacity on highly a ractive terms. Enhancing the ability of local regulators to assess the solvency implications of in- surers’ catastrophe risk exposures. Although this analysis documented a considerable level of technical sophistication on the part of CEE V-4 insurance supervisors in moni- toring and regulating insurers’ risk exposures to natural disasters, the capacity of CEE V-4 insurance regulatory bodies in catastrophe risk management would beneï¬?t from further investments in regulatory risk assessment and monitoring tools and specialized staff training. Financial and Fiscal Instruments for Catastrophe Risk Management 143 Notes 1. FLEXA is an abbreviation for the following ï¬?ve insured perils, which are typically covered under one insurance policy: ï¬?re, lightning, explosion, and aviation. 2. In the insurance industry, this approach is known as a rule of averaging or the average clause. 3. See Annual Report of the Czech Insurance Association, 2007, p. 62. 4. See AXCO Country Report for the Czech Republic, 2008. 5. For more detailed information there is a link on MRD web pages: h p://www.mmr.cz/Regionalni- politika/Programy-Dotace/Poskytovani-statni-pomoci-po-zivelni-nebo-jine-poh. 6. Act of 22 May 2003 on Compulsory Insurance, the Insurance Guarantee Fund and the Motor Insurers’ Bureau CHAPTER 5 Fiscal Sustainability Effects of Natural Disaster Shocks N atural disasters constitute a major shock to public ï¬?nances and debt sustainability because of their impact on output and the need for reconstruction and relief ex- penses. The analysis in this chapter uses a Panel Vector Auto Regression (PVAR) model to systematically estimate the impact of geological, climatic, and other types of natural disasters on government expenditures and revenues using annual data for high- and middle-income countries over 1975–2008. The analysis ï¬?nds that all disasters cause declines in real GDP. For the average coun- try, budget deï¬?cits increase after climatic disasters—for lower-middle-income countries the increase in deï¬?cits is widespread. Disasters do not lead to larger output declines in countries with higher initial government debt possibly because their access to capital markets allow them to smooth their long-term funding programs. Countries with high ï¬?nancial development suffer smaller real consequences from disasters, but deï¬?cits ex- pand further in these countries likely due to increases in debt given their easier access to capital markets. Disasters in countries with high insurance penetration also have smaller real consequences and do not result in deï¬?cit expansions. From an ex post perspective, the availability of insurance seems to offer the best combination of real and ï¬?scal conse- quences from catastrophic natural disasters. Background and Context Recent observations suggest that natural catastrophes, especially climatic ones, are in- creasing both in intensity and frequency. UNEP (2005) stresses that the world is facing an increasing frequency and intensity of disasters that have had devastating impacts based on ï¬?gures reported by the secretariat of the International Strategy for Disaster Re- duction where the 10 years prior to 2005 have seen 478,100 people killed, more than 2.5 billion people affected, and about US$690 billion in economic losses. Hoppe and Grimm (2008), form the Geo Risks Research Department of Munich Re, document that there have been increasing signs that the steady advance of global warming is progressively affecting the frequency and intensity of natural catastrophes. In addition to their direct costs, usually measured in terms of damages, casualties, and output losses (Radda , 2009; Rasmussen, 2004), natural disasters have the potential to constitute a major issue for public ï¬?nances, and debt sustainability in particular (Bo- rensztein et al., 2008; Rasmussen, 2004; International Monetary Fund, 2009; Inter Ameri- can Development Bank, 2009; World Bank, 2003; World Bank, 2001). The reconstruction of public infrastructure destroyed by a disaster requires increases in government expen- ditures at the same time that the contraction in economic activity may reduce the gov- ernment’s ability to gather resources from standard tax collections. Furthermore, gov- ernments facing large disasters may need to mobilize resources to provide emergency 145 146 A World Bank Study relief, aid, and social safety nets to those individuals directly affected by these catastro- phes. While international aid may help mitigate some of the immediate consequences of disasters, the amounts involved are usually smaller than the tens of billions that a large disaster may cost, and are not promptly available. The consequences of disasters for public ï¬?nance and debt sustainability will depend on the nature of the government’s reaction to the disasters. Whether governments re- spond to disasters by increasing expenditures to provide reconstruction and relief after a natural disaster will depend on their capacity to gather resources by increasing ï¬?scal revenues or borrow resources from domestic or international sources, or beneï¬?t from previously contracted ï¬?scal policy insurance or other hedges. In absence of these ï¬?nanc- ing options, the governments’ only option would be to maintain or even decrease the level of expenditures, limiting its ability to provide reconstruction and relief and poten- tially increasing the economic consequences of the disaster. The route followed by different governments concerning the combination of expen- ditures, revenues, and borrowing will likely depend to the access to lending, its cost, and on the demand for government services. For instance, countries that can borrow at low cost and face the burden of reconstruction and relief may prefer that route to increas- ing revenues through taxation or restraining expenditures. And countries where private insurance markets share a large fraction of the reconstruction costs (for example, by ï¬?- nancing the reconstruction of private and public capital) may focus on emergency relief, face smaller funding requirements, and expanding expenditures moderately. This chapter estimates the impact of natural disasters on ï¬?scal sustainability by characterizing how government expenditures and revenues typically respond to dif- ferent types of disasters, and how these responses relate to a government’s ability to borrow and to the availability of private sources of ï¬?nancing for private and public re- construction. Following Radda (2009), the analysis does this by estimating the param- eters of a PVAR model that includes real output, government expenditures, government revenues, measures of the occurrence of geological, climatic, and other disasters, as well as other external shocks and standard macroeconomic variables like inflation and inter- est rates.1 The three categories of natural disasters considered follow Skidmore and Toya (2002) and are deï¬?ned as follows: geological disasters including earthquakes, landslides, volcano eruptions, and tidal waves; climatic disasters including floods, droughts, extreme temperatures, and windstorms; and other disasters including famines, epidemics, insect plagues, wild ï¬?res, miscellaneous accidents, industrial accidents, and transport accidents. Using the parameters of the model one can predict the dynamic response of each of the variables of interest to the occurrence of any type of disaster the same year the disaster occurs and in the years following the disaster. The model is estimated using an- nual data for high and middle-income countries during the period 1975-2008. While low income countries are also of interest, data availability and the importance of aid flows for government ï¬?nancing makes them hard to compare to countries that participate more actively in international ï¬?nancial markets. The response of all variables in the model are identiï¬?ed against the occurrence of each type of natural disaster by assuming that these disasters are acts of God whose occurrence is exogenous to a country’s economic condition. After estimating the aver- age ï¬?scal responses to disasters of all countries in the sample, the responses of differ- ent country groups are contrasted based on income levels, ï¬?nancial development, and Financial and Fiscal Instruments for Catastrophe Risk Management 147 insurance penetration. The contrasts allow to test whether differences in these country characteristics that proxy for a country’s ability to borrow, and the availability of non- governmental sources of funds for reconstruction, are associated with different ï¬?scal behaviors and macroeconomic costs of disasters. Crucially, when comparing the re- sponses of countries across groups, differences in income levels across these groups are controlled for.2 For middle- and high-income countries, it is found that all three types of disasters cause unambiguous declines in GDP of about 1 percent for a climatic disaster, 7 percent for a geological disaster, and 5 percent for other disasters. However, clear budget con- sequences are observed more prominently after climatic disasters. While not conï¬?rmed, this could possibly be due to the wider area scope of such disasters, for example, when rains, windstorms, or hurricanes cover large swathes of territory thus causing multi- jurisdictional damages. The ï¬?nancial consequences thus occur due to expanding expen- diture (by 15 percent) and declining revenue (by 10 percent) after these episodes. While governments try to proactively a enuate the impact of climatic disasters, they incur signiï¬?cant budget deï¬?cits (increases by 25 percent from initial levels). The GDP impact of climatic shocks, however, is the smallest as a result of theses government ï¬?s- cal injections. For geological disasters, governments appear to respond less with deï¬?cit ï¬?nancing to achieve a ï¬?scal impulse, and this seems to result in higher real consequences for these disasters. This lack of an offse ing ï¬?scal impulse could be driven by govern- ment preferences or simply a constrained ï¬?scal space,3 and the analysis tries to shed some light on the merit of these two interpretations by further controlling for initial debt levels and ï¬?nancial market development. It appears that initial debt levels do not constrain a government’s ï¬?scal space avail- able for disaster response in the sample, for which it is conjectured that in this sample high initial debt levels proxy for a be er access to capital markets. Furthermore, ï¬?nan- cially developed countries are found to always strongly increase government expendi- tures after disasters (by 55 percent). While deï¬?cits increase relatively more in ï¬?nancially developed countries (by 75 percent as opposed to 10 percent in less ï¬?nancially devel- oped countries), the resources that an efficient ï¬?nancial system can mobilize may help dealing with the economic consequences of disasters more effectively. The output loss for ï¬?nancially less developed countries appear to be 2 to 10 percent of GDP versus on average, no signiï¬?cant loss for ï¬?nancially more developed countries. In contrast, coun- tries with high levels of insurance penetration can deal with the economic consequences of disasters without engaging in deï¬?cit ï¬?nancing of expenditures. In addition to quantifying the impact of natural disasters on output and ï¬?scal vari- ables for different groups of countries, this analysis leaves three main messages concern- ing the use of ï¬?scal-policy ï¬?nancial instruments. First, one needs to be careful when associating high debt levels with a government’s limited ability to borrow. A country’s stock of debt is the equilibrium outcome of supply and demand factors. Countries with high debt levels may be those that face a larger supply of loans. For those countries, debt levels proxy for a good access to credit rather than a tighter credit constraint. In the sam- ple of high- and middle-income countries, this seems to be the case. Second, countries with more developed credit and bond markets or more developed insurance markets suffer less from disasters (smaller output declines). However, the way they achieve it differs in both cases. 148 A World Bank Study In ï¬?nancially developed credit and bond markets, governments are able to raise funds and increase deï¬?cits. Presumably, this response helps alleviate the impact of the disasters. Thus, it seems that governments in such ï¬?nancially developed countries have be er access to debt markets to a enuate shocks. In contrast, in countries with high insurance penetration, the smaller impact of disasters occurs without an important ï¬?s- cal expansion. Countries with smaller insurance markets expand deï¬?cits more, yet still suffer more from disasters in terms of the effect on GDP. The availability of insurance seems to reduce the real consequences without requiring an increase in ï¬?scal burdens. It seems, therefore, that while overall ï¬?nancial development helps deal with disasters, the prevalence of insurance does it in a more efficient ex post manner. Of course, properly weighting these two options requires an explicit consideration of the costs of both strate- gies: the net present value of interest costs associated with further borrowing from the ï¬?nancial system versus insurance premium costs. Given the recent emphasis on the use of insurance related strategies to deal with disasters (catastrophe insurance); it is useful to discuss the implications of the results of this strategy. Although the results here relate to insurance penetration in the private sector, ï¬?scal insurance policies could have a similar positive hedging effect and help enhance the disaster relief response and reconstruction, and further diminish the real consequences of disasters in a ï¬?scally sustainable manner. The reason is that, based on the analysis results, the availability of insurance seems to dampen the impact of disasters by taking some of the losses and helping the government to focus ï¬?scal expenses on the remaining un-hedged risks. This mechanism should also apply to ï¬?scal insurance. If this is the case, governments could avoid jeopardizing ï¬?scal sustainability after natural disasters by purchasing ï¬?nancial products that transfer and disperse some of the ï¬?nancial risks from the natural disasters into ï¬?nancial markets. However, challenges in pricing and cost-beneï¬?t analysis concerning these products often leave countries hesi- tant to use them, assuming they will be able to meet the ï¬?nancial costs of disasters with their current expenditures and the help of official aid. Nevertheless, recent experience suggests that, despite these challenges, countries would like to arrange for some risk transfer mechanism as part of their climate-change risk mitigation strategies (Borensz- tein et al., 2008). The remainder of the chapter is structured as follows: section 2 de- scribes the data and section 3 explains the estimation methodology. Section 4 presents and discusses the estimation results including for subgroups of countries based on in- come levels, regional location, and ï¬?nancial deepening. Section 5 concludes. Methodological Approach The analysis estimates the impact of natural disasters on output and ï¬?scal variables across countries using a PVAR model that relates the variables of interest to its lagged values, and to contemporaneous and lagged indicators of the occurrence of various types of natural disasters. For a given country, the baseline speciï¬?cation of the model corresponds to (1) Financial and Fiscal Instruments for Catastrophe Risk Management 149 where xi,t = (TTi,t, yi,t)’, TTi,t is the (growth of) a terms-of-trade index, and yi,t = (EXPi,t, GDPi,t, INFi,t, Ri,t, REVi,t)’ is a vector of endogenous variables that includes the (log of) real government expendi- tures (EXP), GDP per capita (in constant 2000 US dollars) (GDP), the inflation rate (INF), nominal interest rate (R), and government revenues (REV). The main focus of the analysis is on EXP, GDP, and REV, but inflation and interest rates are included in the y vector as controls for other macroeconomic conditions. This set includes all the conventional macroeconomic variables typically included in macro models (see Monacelli (2005), Linde et al (2008), and Adolfson (2001), among others). The vector Di,t = (GEOi,t, CLIMi,t, OTHi,t)’ includes variables capturing the occurrence of geological, climatic, or other disasters, as described in the next section. The parameters θi and θt are country and year ï¬?xed-effects that capture long run differences in all the variables across countries, and the impact of global factors that are common to all countries in the sample and can be understood as the world business cycle. The coefficient gi captures a country-speciï¬?c trend and is included when the model is estimated in levels only (see below). The residual term á½€it corresponds to an error term that is assumed i.i.d. The number of lags, q, is assumed to be equal in both summatories. Relaxing this assumption does not importantly change the results. The parameters of the model are matrices, denoted by Aj, and the structural interpretation of the results depends on the identiï¬?cation of the parameters of the con- temporaneous matrix A0. Data To conduct the analysis, data was collected on the incidence of disasters and several measures of macroeconomic and ï¬?scal performance for middle- and high-income coun- tries (see table 5A.1). Low-income countries are not included because their ï¬?scal expen- ditures, revenues, and overall debt are typically related to official and multilateral aid support. Therefore, the ï¬?scal responses to shocks are likely to differ qualitatively from those of other countries and depend on exogenous aid allocation. Data for natural disasters were obtained from the Emergency Disasters Database (EM-DAT) maintained by the Center for Research on the Epidemiology of Disasters (CRED, 2008). This is a comprehensive database that includes data on the occurrence and effects of over 12,800 mass-disasters in the world since 1900, and is compiled from a diversity of sources. As a general principle, to enter into the database an event has to meet any of the following conditions: there are ten or more people reported killed; there are 100 or more people reported affected; a state of emergency is declared; or there is a call for international assistance. The data contain information on various types of disasters that, following Skidmore and Toya (2002) are classiï¬?ed in three broad categories. Geological disasters include earthquakes, landslides, volcano eruptions, and tidal waves. An important character- 150 A World Bank Study istic of this type of events is their unpredictability and relatively fast onset. The second category is climatic disasters. This category includes floods, droughts, extreme tempera- tures, and windstorms (for example, hurricanes). Compared to the previous category, some of these disasters can be forecasted well in advance (so precautions can be under- taken) and some have a relatively long onset. The ï¬?nal category is a residual group that includes famines, epidemics, insect plagues, wild ï¬?res, miscellaneous accidents, indus- trial accidents, and transport accidents. In each category, the incidence of disasters is measured by counting the annual number of events that classify as large disasters according to the following criteria es- tablished by the International Monetary Fund (see Fund (2003)); that is, the event either affects at least half a percent of a country’s population, or causes damages to the capital stock, housing, human lives, other, of at least half a percent of national GDP; or results in more than one fatality for every 10,000 people. Starting from this variable, a different measure is constructed that not only counts the number of disasters, but also takes into account the month of the year when the disaster occurs, in a manner similar to Noy (2009). This allows disasters occurring early in the year to have a different contemporaneous impact that those that happen near the end of the year. Taking into account the date of occurrence, produces an estimate of the output cost of a disaster occurring January 1st. Data on macroeconomic performance, ï¬?scal stance, and other types of external shocks (used as controls in part of the analysis) come from various sources. Real GDP per-capita is measured in constant 2000 US dollars and obtained from the World Bank’s (2008) World Development Indicators (WDI). The terms-of-trade index is the ratio of export prices to import prices computed using the current and constant price values of exports and imports from the national accounts component of the Penn World Tables (version 6.1) and updated using the terms-of-trade data from WDI. Data on government expenditures and revenues came from WDI, IFS, and EIU. Data on total government debt came mainly from Panizza et al. (2008), complemented with data from WDI, IFS, and EIU. Government expenses are cash payments for goods and services incurred by the government, including wages compensation and interest pay- ments. Revenues include receipts from taxes, social contributions, and fees, excluding grants. Data on a country’s CPI and inflation rate came from WDI. Finally, data on money market, discount, and deposit interest rates came from the International Monetary Fund’s (2010) International Financial Statistics. To increase the cross-country coverage of the sample the three deï¬?nitions above the interest rate series with the longest spell during the sample period were selected, with preference for the money market rates when two or more series had the same coverage. Summary statistics for these variables for the sample of countries during the period of analysis are present- ed in table 5A.2. To improve coverage on all macroeconomic and disaster variables, the ï¬?nal sample used in the econometric analysis below was restricted to the post Bre on Woods, 1975–2006 period. Table 5A.3 takes a ï¬?rst look at the data by comparing within the sample, the average macroeconomic performance for years with and without disasters. The results show that expenditures grow slightly faster in years with Geological and Climatic disasters, but not signiï¬?cantly so. In the year of a geological disaster, expenditures grow 5.6 percent on average, compared to only 2.6 percent for the remaining years. However, both averages Financial and Fiscal Instruments for Catastrophe Risk Management 151 have wide dispersion and a two-sided test rejects the hypothesis that those two averages are identical, only at the 12 percent level. The differences are much smaller and also insigniï¬?cant for climatic disasters, which result in expenditure growth of 2.7 percent, compared to 2.6 percent for the average year without a climatic disaster. On the revenue side, revenue growth is also higher in the year of a geological di- saster than in other years (4.4 versus 3.1 percent, respectively), but is lower in the year of a climatic disaster than in a normal year (2.4 versus 3.3 percent). These unconditional comparisons show only a small increase in the ï¬?scal deï¬?cit during a disaster. However, a proper estimation of the impact of a disaster on any macroeconomic variable requires conditioning on the behavior of other variables, as well as global fluctuations in eco- nomic activity. The methodological approach outlined in section 2 takes care of that. The Impact of Disasters on Expenditures and Output The impulse responses that are presented in the next section summarize the response of the key variables included in the VAR (output, government expenses, and revenues) against the occurrence of a large natural disaster. As such, each one of them conveys information on the evolution of the whole system of variables after a shock, and on the full set of relations among variables. These interactions may lead to some apparently unintuitive results that are useful to discuss at this stage. For instance, the response to a disaster of a simple system that includes only output, ï¬?scal expenditure, and ï¬?scal revenue, is considered. One can assume that initially the di- saster leads to a decline in output, an increase in expenditure, and that revenue passively follows output. After the initial impact, the evolution of each of these variables will de- pend on their contemporaneous and lagged relations. In particular, in this example the sign and magnitude of the expenditure multiplier will play a crucial role. If an increase in expenditures leads to an increase in output, this multiplier effect will dampen the ini- tial output decline resulting from the disaster. If the multiplier is large enough, output may actually end up increasing shortly after the disaster instead of declining. Thus, in the above example, it is possible to obtain small and even positive respons- es of output to disasters depending on the impact of the disaster on expenditures and the relation between expenditures and output. It is also possible that a disaster will not lead to an increase in government expenditures if a government does not have the ï¬?scal space for deï¬?cit ï¬?nancing. In such a case, expenditures will not react immediately to the shock but follow the declining revenues. Depending on the sign and magnitude of the ï¬?scal multiplier, this may reinforce or dampen the response of output. Of course, if rev- enues do not follow output passively, the ï¬?nal behavior of all variables will also depend on the impact of a disaster on revenues and the relation between revenues and output and expenditures. Also, if other variables are added to the PVAR their behavior should be considered when tracing down the impact of a shock. These simple examples highlight that one must be careful when interpreting the results of the impulse-response functions because they do not only convey isolated re- lations among pairs of variables. One could in principle trace down the transmission mechanism looking at the full set of IRF for each of the structural shocks. For instance, in the example above one could look at the IRF of output to an expenditure shock, to gauge the sign and signiï¬?cance of the multiplier and decompose the direct and indirect trans- mission of a disaster to output. However, as discussed above, while the assumptions for 152 A World Bank Study the identiï¬?cation of the impact of disasters and other exogenous variables are relatively uncontroversial, identifying ï¬?scal shocks from causal ordering using annual data has many pitfalls. Thus, the impulse responses to structural shocks to endogenous variables must be taken with caution. Results This section presents and discusses in detail the estimated impact of natural disasters on output, ï¬?scal expenditures, ï¬?scal revenues, and the deï¬?cit. Other macroeconomic vari- ables, like inflation and interest rates, are included in the estimation to control for their behavior around disasters but their response to disasters is not discussed, for reasons of space. For the baseline estimation, the annex to this chapter reports on the full set of impulse-response functions. First discussed are the baseline results for the full sample of countries included in the analysis. Then the differential responses across income lev- els are documented, as well as proxies for the ï¬?scal space, and the development of the ï¬?nancial and insurance markets. The annex presents a detailed discussion of the impact of disasters for different regions. Baseline Results Figure 5A.1 shows the cumulative impulse response functions of real per capita GDP, government deï¬?cits, government expenditures, and government revenues. Since the variables are expressed in logarithms, the non-cumulative IRFs show the percentage deviation of the variable with respect to its trend level at each point in time, and the cu- mulative IRFs displayed show the cumulative percentage deviation of a variable at each moment. In the long-run, the cumulative IRFs show the total percentage deviation of the variable from its trend resulting from a shock. In this and most of the analysis, government expenditures and revenues are ex- pressed as a fraction of government deï¬?cit using the sample average shares of each deï¬?cit component. This means that the evolution of the deï¬?cit can be directly obtained by subtracting the evolution of expenditures and revenues.4 This evolution is the one shown in the second column of graphs, because the deï¬?cit is not directly part of the model speciï¬?ed in equation (1). Obtaining the evolution of deï¬?cit as a fraction of GDP only requires subtracting the evolution of real GDP from the evolution of the deï¬?cit. For the average middle- and high-income country, all three types of disasters have a signiï¬?cantly negative impact on GDP (ï¬?gure 5A.1, ï¬?rst column). The cumulative output decline is about 1 percent for a climatic disaster, 7 percent for a geological disaster, and 5 percent for other disasters (the residual category). As mentioned above, the residual disaster category is qualitatively different from the other two, so the impact of these disasters must be taken with caution. Henceforth, more emphasis will be placed in the discussion, of the be er-deï¬?ned geological and climatic disasters. Fiscal variables respond to disasters. The impulse responses reported in ï¬?gure 5A.1 show the evolution of government expenditures and revenues as a share of the govern- ment deï¬?cit, so that the difference between these two series measures the impact of the shock on the deï¬?cit. The evolution of the deï¬?cit computed in this way is also reported in the second column of the ï¬?gure. Government expenditures increase in response to cli- matic and geological disasters, although only the la er cumulative response reaches sig- niï¬?cance two years after the shock. On the contrary, expenditures contract strongly after a residual disaster. Revenues decline strongly after a climatic disaster (a decline cor- Financial and Fiscal Instruments for Catastrophe Risk Management 153 responding to 20 percent of the deï¬?cit), but experience an insigniï¬?cant increase after a geological disaster. After other types of disasters, revenues decline but not signiï¬?cantly. The combination of the increase in expenditures and the decline in revenues after a climatic shock leads to an important increase in the government deï¬?cit (20 percent increase in real terms). After a geological disaster, the increases in expenditures and rev- enues cancel out, resulting in an insigniï¬?cant movement in the level of the deï¬?cit. How- ever, the large simultaneous decline in output implies that even in this case the deï¬?cit is increasing as a share of GDP. Somewhat surprisingly, deï¬?cits decline in real per capita terms after other types of disasters. This decline is larger than the decline in GDP, so that the deï¬?cit declines relative to GDP as well. As mentioned above, this may just reflect the heterogeneity and sparsity of the disasters included in this category. Several of these results are similar to those obtained using a speciï¬?cation in differ- ences (ï¬?gure 5A.2). In this case, the responses of expenditures to climatic and geological disasters are smaller and less signiï¬?cant. The conclusions regarding the deï¬?cit, however, are largely unaffected: the deï¬?cit increases after a climatic disaster, fluctuates very li le after a geological disaster (sometimes with the opposite sign, showing a contraction of the deï¬?cit), and is insigniï¬?cant but changes sign after other disasters. Overall, the baseline results show unambiguous GDP declines after each type of disaster, but clearer budget consequences following climatic disasters. These conse- quences come from an expansion of the expenditure and a decline of the revenue after these episodes. It seems that governments actively try to a enuate the impact of these disasters (possibly due to their broad geographical impact) by incurring deï¬?cit ï¬?nanc- ing. Coincidentally, the output impact of climatic shocks is the smallest. Following a geological disaster, expenditures and revenues move in similar directions, resulting in a small budget adjustment. After a typical geological disaster, fluctuations in expenses are highly correlated with ï¬?scal revenues. Governments do not massively resort to deï¬?cit ï¬?nancing after a geologi- cal disaster and this seems to end in higher real consequences for these disasters. This lack of deï¬?cit ï¬?nancing may be due to demand factors (government choice) or because of a small ï¬?scal space. Results below controlling for the level of initial debt and ï¬?nancial market development will shed more light on the merit of these two interpretations. Robustness There were several modeling choices made in the estimation of the baseline results. This section briefly explores the robustness of the results to these choices. The discussion above already showed that the use of a model in levels or differences does not impor- tantly affect the results. In what follows the role of the number of lags, the measure of disasters, the measure of output, and the order of the variables in the VAR, are explored. The results of each of these exercises, reported in ï¬?gures 5A.3 to 5A.7, show that the ï¬?ndings discussed above are not crucially driven by these modeling choices. Adding a third lag turns positive the point estimate for the GDP impact of climatic disasters, but as in the previous case, the impact is not statistically signiï¬?cant (ï¬?gure 5A.3). The conclu- sions regarding deï¬?cits, expenditures, and revenues are largely unaffected. Two different indicators of the occurrence of disasters are used. First, a simple index that takes the value of 1 if at least one disaster of each category took place in a given year (ï¬?gure 5A.4, panel A). Second, a more complex index takes into account the month when the disaster occurs, thus reporting the impact of a disaster occurring January 1st 154 A World Bank Study (ï¬?gure 5A.4, panel B). In both cases, the output and ï¬?scal impacts of disasters are similar to those reported in the baseline results. Similar results are also obtained when using the PWT measure of real per capita GDP that adjusts for purchasing power parity instead of the measure in constant dollars (ï¬?gure 5A.5). As explained in section 3, the baseline estimation in levels, included the changes in the (log) interest rate instead of the level of this variable because in some cases its impulse responses suggested non-stationary behavior. While this choice makes a differ- ence for the estimated responses of this variable, it does not importantly affect the esti- mated responses of output and the ï¬?scal variables to disasters, as shown in ï¬?gure 5A.6. Finally, changing the order of variables in the VAR, so that expenditures are located after output inflation, and interest rates, and just before revenues, does not change the main results either (ï¬?gure 5A.7). Overall, these exercises indicate that the broad pa erns documented above are robust features of the data and do not depend crucially on spe- ciï¬?c modeling choices. In what follows, the focus is only on the baseline model estimated in levels, because of its precision relative to the model in differences. The Impact of Disasters across Income Levels The baseline results group all middle and high-income countries together. As discussed in section 3, this increases the number of disasters included in the sample, raising the statistical power of the procedure. The cost is that assuming homogeneity in the pa- rameters may signiï¬?cantly bias the estimates. A possible way of advancing, in allowing heterogeneity while retaining statistical power, is to estimate separately the model for groups of relatively homogeneous countries. One straightforward manner of grouping countries is according to their per-capita income level, which proxies for their overall level of development. The results of this exercise are reported below. Climatic and geological disasters have a smaller output impact among high-income countries than in the whole sample (ï¬?gure 5A.8, ï¬?rst column). Climatic disasters induce a small contemporaneous decline of a few basis points that quickly reverses and become close to zero (and insigniï¬?cant) from a statistical perspective. Geological disasters have a cumulative output effect of about 3 percent (half that of the baseline) that is not sig- niï¬?cant either. The only large signiï¬?cant impact is that of other disasters. However, as shown in table 5A.1, there are very few and concentrated episodes of Geological and Other disasters among high-income countries. The only country in this group that has experienced large geological disasters is Greece, on three occasions, and the only coun- try affected by other disasters is Barbados. Only for climatic disasters is there enough statistical variation for identiï¬?cation (27 disasters spread across several countries). Thus, the results for Geological and Other disasters in this group of countries are unlikely to be reliable and therefore what follows, focuses on Climatic disasters. On the ï¬?scal side, both expenditures and revenues increase after a climatic disaster (annex table 5A.1, columns (3) and (4)). This comovement results in insigniï¬?cant impacts on the budget deï¬?cit. This suggests that high-income countries increase their expenditures and revenues in response to such disasters. They can mitigate the impact of these shocks without going into deï¬?cit ï¬?nancing, presumably due to a positive multiplier effect of public expenditures. The situation is different for middle-income countries (ï¬?gure 5A.9). The output im- pact of disasters is much larger in this group, with a cumulative output decline of about Financial and Fiscal Instruments for Catastrophe Risk Management 155 1.5 percent for climatic disasters and about 7 percent for Geological disasters. Contrary to high-income countries, in this (larger) group of countries there are many episodes of disasters across several countries, so the results are not driven by a single country or a cluster of episodes. On the ï¬?scal side, disasters are typically associated with increases in expenditures. These increases reach about 10 and 50 percent of the average budget deï¬?cit after a cli- matic and geological disaster, respectively. On the revenue side, there are important differences between climatic and geological disasters. While revenues decline by about 30 percent of the deï¬?cit after a climatic disaster, they increase by a similar magnitude after a geological disaster. As a result, the cumulative budget deï¬?cit increases by about 20 percent following a climatic or geological disaster, but only after a climatic disaster is this increase statistically signiï¬?cant. Of course, given the decline in output, the cumula- tive deï¬?cit increase as a fraction of GDP would be higher. Overall, governments in middle-income countries react to disasters by increasing expenditures and relying on deï¬?cit ï¬?nancing, thus increasing their overall debt levels. However, despite these a empts, the disasters still result in important output costs that further reduce their ability to service debt, presumably due to a small ï¬?scal multiplier and a larger direct impact of disasters on economic activity. In the sample used, the group of middle-income countries encompasses 73 coun- tries. It is thus possible that the group is still too heterogeneous and that the responses discussed above may be contaminated by this heterogeneity. To further check for this possibility, this group was separated into two sub-groups of lower- and higher-middle- income countries, again following the World Bank’s classiï¬?cation. The results are re- ported in ï¬?gures 5A.10 and 5A.11. Lower-middle-income countries are shown to be much more heavily affected by di- sasters than higher-middle-income ones. In the former group, a climatic disaster results in a 4 percent cumulative output decline, while in the la er it leads to a similar output increase. Similarly, geological disasters lead to an 11 percent cumulative output decline among lower-middle-income countries and to a negligible decline among higher mid- dle-income countries. The small decline in higher-middle-income countries following a geological disaster is not very robust and when looking at the speciï¬?cation in differ- ences there is a similar decline to that for lower-middle-income countries. However, the increase following a climatic disaster in higher-middle-income countries persists across speciï¬?cations and is unlikely to be driven by speciï¬?c episodes because there are 77 epi- sodes of climatic disasters among the 28 countries in this group. Although it may initially look contradictory, it is worth reminding that from a theo- retical point of view, the impact of a disaster on economic activity is ambiguous. A disas- ter may destroy capital and other factors of production reducing the amount of output that can be produced with a given amount of labor. However, it also makes people inter- temporally poorer, increasing the incentives to work through a standard wealth effect. The ï¬?nal response of output depends on which of these effects dominate. Thus, one pos- sible interpretation of these ï¬?ndings is that, among higher-middle-income countries the wealth effect associated with a disaster dominates the factor destruction effect, leading to a slowly accumulating increase in output. On the ï¬?scal side, there are completely opposite responses to disasters between these two groups of middle-income countries. Lower-middle-income countries reduce 156 A World Bank Study (increase) expenditure and revenue after a climatic (geological) disaster. Higher-middle- income countries follow the exactly opposite pa ern for climatic disasters. However, these different pa erns yield more similar results for the behavior of the budget deï¬?cit. In both groups of countries, the deï¬?cit increases after a climatic disaster, although the increase is larger and more signiï¬?cant among lower-middle income ones (30 percent versus 20 percent). The increase in the deï¬?cit after a geological disaster is not signiï¬?cant in both cases, although the point estimate is also considerably higher among lower-mid- dle-income countries (50 percent increase versus 10 percent decline). Overall, these results suggest that most of the previous conclusions regarding mid- dle-income countries are driven by the behavior of lower-middle income ones. Among these countries, governments react to disasters by engaging in deï¬?cit ï¬?nancing and in- creasing debt, but are still more affected by the disasters on the real side, further reduc- ing their ability to repay. This coincides with the common observation that relatively poorer countries have lower capacity to efficiently and effectively execute government expenditures. Of course, another possibility is that the direct output impact of disasters could be higher among these countries. For instance, a smaller stock of capital in lower-middle- income countries could be associated with a higher marginal product of capital, so the output losses associated with a decline in the capital stock would be higher. Another possibility is that the wealth effects that push for an increase in output after a disaster are smaller among these countries. Indebtedness and the Effect of a Country’s Response to Disasters The previous results suggest that middle-income countries, especially the poorer ones engage in deï¬?cit ï¬?nancing after a disaster without being able to mitigate the impact of these events on the real side of the economy. However, even the ability to engage in deï¬?cit ï¬?nancing of expenditure will likely depend on a country’s debt level, its access to domestic or international debt markets, and the ability to raise revenues through taxa- tion. In this section some insights are shed on the role of initial debt on a country’s ability to engage in deï¬?cit ï¬?nancing, by comparing the output and ï¬?scal response to disasters of countries with different initial levels of total government debt. Despite government debt being an important macroeconomic variable, data on to- tal debt levels is relatively scarce and available for few countries in recent years. Thus, looking at the role of debt severely reduces the sample of countries under consideration. With this caveat in mind, the results of this exercise are reported in ï¬?gure 5A.12. Contrary to expectations, countries with high levels of initial debt (panel B) do not suffer more from disasters than those with low levels of debt (panel A). Climatic shocks induce similar output declines in the two groups, and Geological disasters have larger impact on countries with lower initial debt levels (panel A). Also, despite similar de- clines in revenue after a climatic disaster, countries with higher initial levels of debt expand government expenditures relatively more and run higher increases in the deï¬?- cit. Only for Geological Disasters there is a larger deï¬?cit increase among countries with lower debt levels, but this larger increase is associated with a larger decline in revenue relative to countries with higher debt levels. At least in this sample, it seems that initial debt levels do not constrain a government’s ï¬?scal space. This apparent paradox is partly explained by the composition of countries in the sample with high and low debt levels. Financial and Fiscal Instruments for Catastrophe Risk Management 157 There are many more high-income countries among those with high initial debt levels than among those with low initial debt levels. Also, among upper middle-income countries, those with higher initial debt levels have higher income per capita than those with lower debt. The average GDP per capita (PPP adjusted) among countries with high debt is about US$9,900 but only US$8,600 dollars for countries with lower initial debt. In this sample, governments of relatively richer countries have enjoyed be er access to debt. This access seems to be serially correlated, so that good access in the past, signals good access in the future rather than a reduced ï¬?scal space. To check to what extent these differences are driven by income levels an estimation was made of a variation of the model described in equation (1), that, instead of spli ing the sample in two groups, allows the impact of external shocks to vary parametrically with the initial level of debt and a country’s level of income. This means that the Bj ma- trices in equation (1), and the block of the Aj matrices associated with the terms-of-trade fluctuations will vary with the levels of debt and income. After estimating this model, it is possible to construct the IRFs for countries with high and low levels of debt controlling for differences in income. Figure 5A.13 reports these IRFs. Each of the panels in the ï¬?gure reports the impact of a type of disaster on output and ï¬?scal variables for hypothetical countries with low and high debt levels (25th and 75th percentile of the debt to GDP ratios across sample countries), along their one standard deviation conï¬?dence bands. These ï¬?gures show that the pa erns documented above survive controlling for differences in average income levels. Countries with higher initial debt levels experience a smaller decline in GDP after a geological disaster, a larger expansion of government expenditures and a smaller con- traction of revenues after all types of disasters. In sum, the hypothesis that high initial debt levels are proxying for be er access to funds in this sample, is not rejected by con- trolling for differences in average GDP per capita. Financial Development and Insurance Penetration A disaster typically affects a country’s productive capacity by destroying physical and human capital. Replacing that capital is costly and may take time (especially in the case of damages to infrastructure). While there is no way around the time required to rebuild capital and infrastructure; and human capital lost may never be replaced; having quick access to ï¬?nancial resources will certainly reduce the time it takes to reconstruct a coun- try’s productive capacity. Even though governments may try to provide relief and resources for this recon- struction, a large part of it will likely come from market sources. Therefore, having a well-developed ï¬?nancial system that can ï¬?nance the reconstruction ex post or that can gather and price the risks ex-ante through insurance schemes, may substantially reduce the need for government ï¬?nancing in the aftermath of a disaster, and make government spending more productive.5 Next, the relation between ï¬?nancial and insurance market development is observed, as well as the consequences of disasters in relation to government ï¬?nancing and output (GDP), by grouping countries according to the development of these markets and com- paring the impact of disasters across these groups. To maintain as many observations and disasters as possible in each group, the sample between countries is ï¬?rst divided 158 A World Bank Study among those with measures of ï¬?nancial development and insurance penetration above and below the sample median respectively. The analysis shows that climatic and geological shocks have a large negative out- put impact on countries with low levels of ï¬?nancial development, as measured by the average ratio of private credit to GDP from 1975 to 2008 (ï¬?gure 5A.14). Among these countries, a climatic shock results in a cumulative output decline of almost 2 percent, and a geological disaster results in a decline of about 9 percent. In contrast, among more ï¬?nancially developed countries a climatic disaster has rather positive impact on output while a geological disaster has no impact on output.6 Government expenditure does not increase after climatic disasters in ï¬?nancially un- derdeveloped countries, but a large signiï¬?cant increase of 60 percent of the average bud- get deï¬?cit occurs among more ï¬?nancially developed ones (ï¬?gure 5A.14, panel B, column 3). The la er occurs despite an important contraction of revenue of about 30 percent of the average deï¬?cit. As a result, the budget deï¬?cit increases importantly in ï¬?nancially developed countries, and only modestly and not signiï¬?cantly among ï¬?nancially under- developed ones. Controlling for income does not change the conclusions. The comparison of the re- sponses to disasters of GDP and ï¬?scal variables in countries with high and low levels of ï¬?nancial development (ï¬?gure 5A.15) (25th and 75th sample percentiles, respectively) conï¬?rms that more ï¬?nancially developed countries suffer smaller output contractions after disasters, although the differences are not signiï¬?cant. The ï¬?gure also conï¬?rms that expenditures always expand in ï¬?nancially developed countries, and revenues expand after geological disasters and contracts after climatic disasters. As before, deï¬?cits always increase relatively more in ï¬?nancially developed countries. These results indicate that governments can borrow more easily in ï¬?nancially de- veloped countries, and that the real consequences of shocks, at least for the more fre- quent climatic ones, are smaller. This is consistent with the ï¬?nancial system facilitat- ing resources both for government ï¬?nancing (for example, by allowing the issuance of domestic debt) and for private reconstruction. Having access to the resources, which can be mobilized by an efficient ï¬?nancial system, helps dealing with disasters. This is conï¬?rmed by unreported results that interest rates also decline in ï¬?nancially developed countries following a climatic shock (while they remain unaltered among ï¬?nancially underdeveloped countries), and suggests that the larger deï¬?cit expansion in these coun- tries does not necessarily lead to a larger increase in government debt burdens or con- cerns about an excessive debt burden that would signiï¬?cantly increase the interest rate risk premium for the governments. The results are different when countries are compared according to the degree of in- surance penetration, as measured by the total value of premiums to GDP (ï¬?gure 5A.16). It is important to keep in mind that data on insurance penetration is not widely available, so the subset of countries with data is biased toward higher income countries. Thus, the important aspect of this exercise is the comparison between the two groups rather than the estimated responses for each individual group. Comparing the real consequences of shocks, countries with relatively low insurance penetration (panel A) suffer larger output declines in response to climatic and geological disasters than countries with high insurance penetration (panel B). At the same time, deï¬?cits increase considerably more in countries with low insurance penetration. In countries with high insurance penetration, expenditures and revenues move together resulting in a small change in the ï¬?scal deï¬?cit. Financial and Fiscal Instruments for Catastrophe Risk Management 159 Most of these pa erns survive controlling for differences in income (ï¬?gure 5A.17). Countries with low insurance penetration suffer signiï¬?cantly more after disasters (ï¬?rst column) and expand expenses relatively more (although this difference is not signiï¬?- cant). The only difference is that while revenues decline relatively more for countries with low insurance penetration, they move similarly in both groups after a geological di- saster. As a result, deï¬?cits increase relatively more after a climatic disaster for countries with low insurance penetration, but increase relatively less after a geological disaster. Nonetheless, when computed as a fraction of GDP, deï¬?cits always increase relatively more for countries with low insurance penetration. Overall, countries with low insurance penetration expand their government deï¬?cits after disasters but do not manage to reduce the negative consequences of disasters as much as in those countries with high insurance penetration. One likely interpretation of these ï¬?ndings is that countries with high insurance penetration quickly allocate the resources from existing insurance coverage to recover productive capacity, and li le ï¬?s- cal effort is required to dampen the macro consequences of these events. Fiscal resources can then be devoted to relief, and the simultaneous increase in expenditures and reve- nues suggests that the ï¬?scal effort is mainly redistributive (for example, providing relief to those affected by increasing revenues from those not affected by the disaster). Finally, a comparison of these results with those obtained when comparing coun- tries with different levels of ï¬?nancial development show that these two dimensions play different roles in the transmission of disasters to the ï¬?scal purse. While countries with high ï¬?nancial development or high insurance penetration suffer relatively less from di- sasters in terms of output decline, a developed ï¬?nancial system allows governments to borrow and ï¬?nance a deï¬?cit possibly at low interest rates to reduce the real conse- quences of disasters. In contrast, countries with high levels of insurance penetration can deal with these real macro consequences without engaging in deï¬?cit ï¬?nancing of the expenditures. It seems, therefore, that while overall ï¬?nancial development helps deal with disasters, the prevalence of insurance does it in a more efficient ex post manner. Of course, insurance has an ex-ante cost that must be considered for welfare comparisons, another dimension to consider and as discussed earlier in chapter 1. Conclusion This chapter estimated the implications of natural disasters for public ï¬?nances by ana- lyzing the cumulative responses of government expenditures, revenues, and ï¬?scal deï¬?- cit to disaster shocks. The analysis found that climatic, geological, and other disasters have an important negative impact on the ï¬?scal stance by decreasing output and increas- ing deï¬?cits, especially in lower middle-income countries. When controlling for income, there is no clear relation between initial debt and the ï¬?scal impact of disasters. In the sample used, countries that were more indebted seem to be those with be er access to debt, so that debt levels proxy for be er access to capi- tal markets rather than constrained ï¬?scal space. Furthermore, countries with more de- veloped ï¬?nancial or insurance markets suffered less from disasters in terms of output declines. The way this was achieved differed in each case, however. In ï¬?nancially devel- oped markets, governments are able to raise funds and increase deï¬?cits. And presum- ably this response helps alleviate the impact of the disasters. In contrast, in countries 160 A World Bank Study with high insurance penetration, the smaller impact of disasters on GDP occurs without an important ï¬?scal expansion. Countries with smaller insurance markets expand deï¬?cits more, and still suffer more from disasters. Thus, it seems that the availability of insurance reduces the real consequences without requiring an increase in ï¬?scal burdens. By extending the implica- tion of this ï¬?nding, ï¬?nancial markets and development institutions could help in the development and more in depth use of ï¬?scal insurance policies or hedging instruments to further diminish disaster consequences. The future research could focus on be er identiï¬?cation of the ï¬?scal responses to disasters and the implied consequences for ï¬?scal stances, by employing higher frequency (quarterly) data and increasing the homogene- ity of countries in the analyzed sample. This could exploit the potential efficiency gains through the use of appropriate estimation methods. Notes 1. These types of models use the cross-country dimension of the data to increase the power of the estimation of time series models, and have been routinely used when short time series data is avail- able, as it is the case in this paper. 2. This analysis also characterizes the different responses across regions in additional results. 3. Pero i (2007) puts forth two essential features of ï¬?scal space that are used in the discussions henceforth. First, ï¬?scal pace is determined by the inter-temporal government budget constraint and some notion of ï¬?scal sustainability. This means that in order to increase some type of govern- ment expenditures at present one needs to either reduce other expenditures now or in the future, or increase current or future revenues or inflate away existing nominal debt. The ability to increase debt levels in a sustainable manner is thus consistent with having ï¬?scal space available. Second, if one type of expenditure has a higher social marginal return than another and the same cost, re- sources should be moved from the second to the ï¬?rst type of expenditure. 4. By deï¬?nition, the deï¬?cit is the difference between expenditures and revenues: . Log-linearizing this expression, the log deviations of deï¬?cit correspond to , where and are the shares of expendi- tures and revenues on deï¬?cit: and the lowercase le ers with hats represent the log deviation of a variable with respect to its trend. 5. For instance, this may happen by allowing the government to focus on relief and public good provision instead of providing subsidized credits for the private sector. 6. This result is not robust to changes in the variable used for interest rates. When using only the money market rates (with the corresponding reduction in the sample), there is a decline in output as a result of a geological disaster, and only a small impact for climatic disasters. Table 5A.1. Summary Statistics Annex: Impact of Disasters for Different Regions Mean Number of Events Expenditures/ Revenues/ Deï¬?cit/ Number of Country GDP per capita GDP GDP GDP Geological Climatic Other Observations East Asia Paciï¬?c and South Asia China 901 0.156 0.144 −0.011 2 65 0 22 Fiji 1,803 0.285 0.244 −0.042 0 17 0 24 Indonesia 640 0.192 0.185 −0.007 2 1 3 30 Korea, Rep. of 5,704 0.162 0.175 0.013 0 1 0 23 Financial and Fiscal Instruments for Catastrophe Risk Management Malaysia 2,545 0.291 0.243 −0.048 0 0 0 25 Philippines 942 0.166 0.153 −0.014 2 58 0 29 Sri Lanka 568 0.297 0.197 −0.100 0 29 0 26 Thailand 1,404 0.168 0.161 −0.007 0 20 0 27 Total 1,734 0.214 0.187 −0.027 6 191 3 206 Europe and Central Asia Albania 1,303 0.303 0.232 −0.071 0 3 0 14 Azerbaijan 933 0.261 0.217 −0.044 1 4 0 15 Belarus 1,462 0.293 0.322 0.028 0 1 0 16 Bulgaria 1,754 0.351 0.349 −0.002 0 2 0 17 Croatia 5,171 0.363 0.361 −0.001 0 3 0 13 Czech Republic 5,827 0.341 0.317 −0.024 0 2 0 14 Georgia 842 0.250 0.208 −0.042 1 3 0 12 Hungary 4,399 0.499 0.456 −0.043 0 3 0 26 Kazakhstan 1,402 0.238 0.222 −0.016 0 1 0 13 Latvia 3,741 0.373 0.359 −0.014 0 1 0 14 (Table continues on next page) 161 Table 5A.1 (continued) 162 Mean Number of Events Expenditures/ Revenues/ Deï¬?cit/ Number of Country GDP per capita GDP GDP GDP Geological Climatic Other Observations A World Bank Study Lithuania 3,814 0.346 0.317 −0.030 0 1 0 13 Macedonia, FYR 1,775 0.372 0.360 −0.012 0 2 1 15 Moldova 410 0.318 0.307 −0.011 0 3 0 12 Poland 4,034 0.334 0.301 −0.034 0 1 0 17 Romania 1,705 0.324 0.286 −0.038 0 2 0 7 Russian Federation 2,101 0.169 0.183 0.014 0 1 0 13 Slovak Republic 4,222 0.438 0.380 −0.058 0 2 0 12 Slovenia 10,388 0.471 0.462 −0.009 0 2 0 13 Turkey 3,559 0.237 0.175 −0.062 3 2 0 15 Ukraine 795 0.334 0.309 −0.025 0 3 0 15 Total 3,039 0.338 0.313 −0.025 5 42 1 286 Western Europe and North America Austria 17,636 0.372 0.333 −0.039 0 0 0 22 Belgium 17,256 0.493 0.429 −0.064 0 0 0 24 Denmark 23,307 0.381 0.368 −0.013 0 2 0 26 France 20,372 0.521 0.490 −0.032 0 2 0 25 Greece 9,497 0.289 0.201 −0.088 3 2 0 22 Luxembourg 29,981 0.407 0.431 0.024 0 4 0 17 Netherlands 20,934 0.513 0.487 −0.026 0 1 0 25 Portugal 7,939 0.406 0.301 −0.105 0 0 0 19 Sweden 23,397 0.371 0.355 −0.016 0 1 0 32 United States 29,635 0.205 0.180 −0.025 0 3 0 27 Total 20,331 0.394 0.357 −0.037 3 15 0 239 (Table continues on next page) Table 5A.1 (continued) Mean Number of Events Expenditures/ Revenues/ Deï¬?cit/ Number of Country GDP per capita GDP GDP GDP Geological Climatic Other Observations Middle East, North Africa, and Sub-Saharan Africa Algeria 1,783 0.311 0.308 −0.003 0 1 0 17 Botswana 2,575 0.395 0.462 0.067 0 9 2 29 Cameroon 721 0.174 0.186 0.012 1 2 0 28 Financial and Fiscal Instruments for Catastrophe Risk Management Cape Verde 1,280 0.365 0.301 −0.064 0 2 1 13 Egypt, Arab Rep. 1,099 0.376 0.342 −0.034 1 0 0 28 Iran, Islamic Rep. 2,025 0.455 0.410 −0.045 2 0 0 5 Israel 16,212 0.523 0.425 −0.097 0 1 0 19 Jordan 1,748 0.368 0.240 −0.128 0 4 0 27 Lebanon 3,707 0.294 0.116 −0.178 0 1 0 5 Lesotho 383 0.437 0.443 0.006 0 6 0 16 Mauritius 2,849 0.237 0.211 −0.025 0 6 1 26 Morocco 1,191 0.320 0.251 −0.069 1 3 0 22 Namibia 2,508 0.299 0.298 −0.002 0 5 0 6 Seychelles 6,106 0.538 0.473 −0.065 1 1 0 19 South Africa 3,218 0.267 0.237 −0.031 0 6 1 29 Swaziland 1,079 0.281 0.276 −0.005 0 12 0 27 Syrian Arab Republic 1,042 0.314 0.253 −0.061 0 1 0 21 Tunisia 1,595 0.340 0.309 −0.031 0 1 0 16 Total 2,747 0.340 0.306 −0.034 6 61 5 354 (Table continues on next page) 163 164 Table 5A.1 (continued) A World Bank Study Mean Number of Events Expenditures/ Revenues/ Deï¬?cit/ Number of Country GDP per capita GDP GDP GDP Geological Climatic Other Observations Latin America and Caribbean Argentina 7,692 0.204 0.200 −0.004 0 3 0 15 Bahamas, The 15,611 0.188 0.174 −0.014 0 5 0 15 Barbados 8,304 0.320 0.285 −0.035 0 3 1 25 Belize 2,844 0.293 0.244 −0.049 0 7 0 25 Bolivia 1,039 0.255 0.165 −0.090 0 10 0 19 Brazil 3,408 0.276 0.264 −0.012 0 4 0 16 Chile 3,271 0.246 0.249 0.003 2 7 0 23 Colombia 2,393 0.277 0.263 −0.014 3 7 0 24 Costa Rica 3,281 0.163 0.134 −0.029 2 8 0 28 Dominican Republic 2,502 0.130 0.130 0.000 0 3 0 14 El Salvador 2,326 0.181 0.160 −0.021 2 4 1 10 Grenada 2,998 0.294 0.263 −0.031 0 0 0 5 Guatemala 1,607 0.120 0.100 −0.020 0 6 0 30 Guyana 882 0.412 0.346 −0.066 0 2 1 3 Honduras 1,121 0.210 0.164 −0.046 0 17 0 24 Jamaica 3,487 0.260 0.254 −0.006 0 1 0 11 Mexico 5,424 0.258 0.229 −0.029 1 8 1 29 (Table continues on next page) Table 5A.1 (continued) Mean Number of Events Expenditures/ Revenues/ Deï¬?cit/ Number of Financial and Fiscal Instruments for Catastrophe Risk Management Country GDP per capita GDP GDP GDP Geological Climatic Other Observations Nicaragua 805 0.227 0.164 −0.063 0 1 0 7 Panama 3,395 0.237 0.236 −0.001 1 2 0 14 Paraguay 1,415 0.159 0.157 −0.002 0 4 0 13 Peru 2,077 0.176 0.143 −0.033 1 11 2 32 St. Lucia 2,620 0.270 0.245 −0.025 0 2 0 10 St. Vincent and the 2,264 0.330 0.286 −0.043 0 2 0 21 Grenadines Uruguay 5,929 0.263 0.244 −0.019 0 3 0 29 Venezuela, RB 5,320 0.221 0.234 0.013 0 1 0 27 Total 3,840 0.231 0.207 −0.024 12 121 6 469 Note: The table provides descriptive statistics for each country, grouped by regions. Mean values are reported for real GDP per capita, and for government expen- ditures, government revenue and government deï¬?cit as a fraction of the GDP. The number of events by type of disaster, and the number of observations are also listed. 165 166 A World Bank Study Table 5A.2. Unit Root and Cointegration Tests Panel A. Unit root tests Levels Differences Frac. Reject Frac. Reject LLC test IPS test (ADF) LLC test IPS test (ADF) Variable (1) (2) (3) (4) (5) (6) GDP per capita −19.3 −9.2 0.3 −37.0 −27.5 0.8 Government −3.7 −2.3 0.2 −31.7 −29.9 0.8 Expenditures Government −5.6 −3.9 0.2 −26.1 −27.6 0.8 Revenues Inflation −52.2 −28.7 0.6 n.a. n.a. n.a. Interest Rate −4.6 −2.5 0.2 −50.8 −33.4 0.9 Terms of Trade −6.9 −5.2 0.3 −39.4 −39.4 1.0 Panel B. Panel cointegration tests VAR including TT, GEXP, GDP, GREV, INF, and R Alt. hypothesis: common AR coefs. Statistic Prob. Panel v-Statistic −0.72 0.76 Panel rho-Statistic 11.54 1.00 Panel PP-Statistic −0.19 0.42 Panel ADF-Statistic −1.50 0.07 Alt. hypothesis: individual AR coefs. Statistic Prob. Group rho-Statistic 14.97 1.00 Group PP-Statistic -3.64 0.00 Group ADF-Statistic -3.97 0.00 Note: Panel A shows the results of country-by-country and panel unit root tests performed for the main series used in the paper. Columns (1) to (3) show results for the variables in levels, and columns (4) to (6) for the variables in differences. The exception is inflation, which being the changes in the price level, is just included in levels. Columns (1) and (4) show the results of the Levin-Lin Chu panel unit root test, and Columns (2) and (5) the statistics for the Im, Pesaran, and Shin test. Columns (3) and (6) report the fraction of countries in the sample in which a standard, country-by-country augmented Dickey Fuller test could not reject the null hypothesis of a unit root. All the tests in level allow for a country-speciï¬?c intercept and trend, and those in differences for the country-speciï¬?c intercept only. Also, all tests use the Newey-West bandwidth selection with the Bartle kernel for the estimation of the long run variance of the series. The table in Panel B reports the statistic and associated p-value of the different variants of Pedroni’s (1999) panel cointegration test. The null hypothesis in each case is no cointegration. n.a. = not available. Financial and Fiscal Instruments for Catastrophe Risk Management 167 Table 5A.3. Comparing Years With and Without Disasters: Two Sample Mean Tests GDP Growth Expenditures Growth Revenues Growth Geological Climatic Geological Climatic Geological Climatic Mean No Disaster 0.026 0.026 0.026 0.025 0.033 0.034 Disaster 0.013 0.024 0.036 0.027 0.014 0.028 t-stat D = ND 0.141 0.569 0.638 0.779 0.636 0.670 ND > D 0.071 0.285 0.681 0.610 0.318 0.335 ND < D 0.929 0.715 0.319 0.390 0.682 0.665 Note: The table shows the t-test for the difference on the average growth of GDP, Expenditures and Revenues, in years when a disaster occurs (Disaster), and in years without disasters (No Disaster). D is the mean of the sample with at least one disaster, and ND is from the sample with zero disasters. Figure 5A.1. Cumulative Impulse Response Functions of Levels Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.010 0.300 0.100 0.400 0.300 0.000 0.000 0.200 0.200 -0.100 -0.010 0.100 0.100 -0.200 -0.020 0.000 0.000 -0.300 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 1.000 0.800 1.000 0.000 0.500 0.600 0.500 -0.050 0.400 0.000 0.200 0.000 -0.100 -0.500 0.000 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.050 0.000 0.000 0.500 -0.500 -0.500 0.000 0.000 -1.000 -1.000 -0.500 -0.050 -1.500 -1.500 -1.000 -0.100 -2.000 -2.000 -1.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run gov- ernment deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endog- enous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted dif- ference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. 168 A World Bank Study Figure 5A.2. Cumulative Impulse Response Functions of Differences Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.004 0.150 0.100 0.050 0.002 0.100 0.000 0.050 0.000 0.050 -0.050 -0.002 0.000 0.000 -0.100 -0.004 -0.050 -0.050 -0.150 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.400 0.000 0.200 0.100 -0.010 0.200 0.000 0.000 -0.020 -0.100 0.000 -0.200 -0.030 -0.200 -0.200 -0.400 -0.040 -0.300 -0.400 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.020 0.500 0.500 0.000 0.000 0.000 -0.200 0.000 -0.020 -0.400 -0.500 -0.040 -0.500 -0.600 -1.000 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run gov- ernment deï¬?cit. The parameters used to estimate the IRF come from a speciï¬?cation with all variables in differences, and including two lags. The order of the endogenous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 169 Figure 5A.3. Cumulative IRFs Adding Lags Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.300 0.300 0.100 0.020 0.200 0.200 0.000 0.010 0.100 0.000 0.100 -0.100 0.000 -0.010 0.000 -0.100 -0.200 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 1.000 2.000 1.000 1.500 -0.050 0.500 1.000 0.500 -0.100 0.500 0.000 -0.150 0.000 0.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.100 0.000 0.500 1.000 0.000 0.050 -0.500 0.500 -0.500 0.000 -1.000 0.000 -1.000 -0.050 -1.500 -1.500 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run gov- ernment deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including three lags. The order of the endog- enous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted dif- ference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. 170 A World Bank Study Figure 5A.4. Cumulative IFRs Using Different Disaster Indicators Panel A. Index by Category of Disaster Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.020 0.600 0.300 0.200 0.200 0.000 0.000 0.400 0.100 0.200 -0.200 -0.020 0.000 -0.100 -0.400 -0.040 0.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 1.000 1.000 1.000 -0.050 0.500 0.500 0.500 -0.100 0.000 0.000 0.000 -0.150 -0.500 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.050 0.000 0.500 0.000 -0.500 0.000 0.000 -1.000 -0.500 -0.050 -1.500 -1.000 -0.100 -2.000 -2.000 -1.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years (Figure continues on next page) Financial and Fiscal Instruments for Catastrophe Risk Management 171 Figure 5A.4 (continued) Panel B. Index Considering the Timing of the Disaster Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.010 0.400 0.300 0.100 0.300 0.200 0.000 0.000 0.200 0.100 -0.100 -0.010 -0.200 0.100 0.000 -0.020 -0.300 0.000 -0.100 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 1.000 1.000 1.000 0.500 0.500 -0.050 0.500 0.000 0.000 -0.100 0.000 -0.500 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.050 0.000 0.000 0.500 0.000 -0.500 0.000 -0.050 -1.000 -0.500 -0.100 -1.500 -1.000 -0.150 -2.000 -2.000 -1.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gures show the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run gov- ernment deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endog- enous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted dif- ference of revenues and expenditures. In Panel A, the index used to show the occurrence of disasters takes the value 1 if at least one disaster of each category took place in a given year. In Panel B, this index takes into account the month when the disaster occurs. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. 172 A World Bank Study Figure 5A.5. Cumulative IRFs Using a Different Measure of GDP Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.010 0.500 0.100 0.300 0.000 0.000 0.200 -0.100 -0.010 0.100 -0.200 -0.020 0.000 0.000 -0.300 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 1.000 1.000 1.000 0.000 0.500 0.500 -0.050 0.500 0.000 0.000 -0.100 -0.500 0.000 -0.500 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.000 0.000 0.000 0.500 -0.500 0.000 -0.050 -1.000 -1.000 -0.500 -0.100 -1.500 -2.000 -1.000 -0.150 -2.000 -3.000 -1.500 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. GDP is expressed in real per capita terms and ad- justed for purchasing power parity; government deï¬?cit is reported in real per capita terms; government expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (ex- cept the interest rate), and including two lags. The order of the endogenous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 173 Figure 5A.6. Cumulative IRFs Using Interest Rate Level Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.005 0.400 0.300 0.100 0.000 0.300 0.000 0.200 -0.005 0.200 -0.100 -0.010 0.100 0.100 -0.200 -0.015 0.000 0.000 -0.300 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.020 1.000 1.000 1.000 0.000 0.500 -0.020 0.500 0.500 -0.040 0.000 0.000 -0.060 -0.500 0.000 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.000 0.000 0.000 0.500 -0.500 0.000 -0.050 -1.000 -1.000 -0.500 -0.100 -1.500 -2.000 -1.000 -2.000 -1.500 -0.150 -3.000 -5 0 5 -5 0 5 -5 0 5 -5 0 5 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run gov- ernment deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels, and including two lags. The order of the endogenous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The dot- ted lines show one standard deviation conï¬?dence bands. 174 A World Bank Study Figure 5A.7. Cumulative IRFs Changing Order in VAR Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.010 0.400 0.300 0.100 0.300 0.000 0.000 0.200 0.200 -0.100 -0.010 0.100 0.100 -0.200 -0.020 0.000 0.000 -0.300 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 1.000 0.800 1.000 0.500 0.600 0.500 -0.050 0.400 0.000 0.200 0.000 -0.500 0.000 -0.100 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.050 0.000 0.000 0.500 -0.500 -0.500 0.000 0.000 -1.000 -1.000 -0.500 -0.050 -1.500 -1.500 -1.000 -2.000 -2.000 -0.100 -1.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run gov- ernment deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endog- enous variables entered in the VAR is the following: GDP, inflation, interest rate, government expendi- tures and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted dif- ference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 175 Figure 5A.8. Cumulative IRFs for High Income Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.030 0.500 1.500 0.300 0.020 0.000 1.000 0.200 0.010 -0.500 0.100 0.500 0.000 -1.000 0.000 0.000 -0.010 -1.500 -0.100 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.050 5.000 0.000 5.000 0.000 -1.000 -0.050 0.000 0.000 -2.000 -0.100 -0.150 -5.000 -3.000 -5.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.000 5.000 10.000 0.000 -0.200 0.000 5.000 -2.000 -0.400 -5.000 0.000 -0.600 -10.000 -4.000 -5.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues, for a sample of High Income countries according to the World Bank classiï¬?cation. GDP and government deï¬?cit are expressed in real per capita terms; gov- ernment expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endogenous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The dot- ted lines show one standard deviation conï¬?dence bands. 176 A World Bank Study Figure 5A.9. Cumulative IRFs for Middle Income Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.010 0.600 0.300 0.000 0.000 0.400 0.200 -0.010 0.100 -0.200 0.200 -0.020 0.000 0.000 -0.400 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 1.000 1.000 1.000 -0.050 0.500 0.500 0.500 -0.100 0.000 0.000 -0.150 -0.500 0.000 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.100 0.000 0.000 0.500 0.050 -0.500 -0.500 0.000 0.000 -1.000 -1.000 -0.500 -1.500 -0.050 -1.500 -1.000 -2.000 -0.100 -2.000 -1.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues, for a sample of Middle Income countries accord- ing to the World Bank classiï¬?cation. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endogenous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The dot- ted lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 177 Figure 5A.10. Cumulative IRFs for Low and Middle Income Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.000 0.600 0.000 0.100 -0.020 0.400 0.000 -0.200 -0.100 -0.400 -0.040 0.200 -0.200 -0.600 -0.060 0.000 -0.300 -0.800 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 1.500 1.500 1.000 -0.050 1.000 1.000 0.500 -0.100 0.500 0.000 -0.150 0.500 0.000 -0.500 -0.200 -0.500 0.000 -1.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.200 0.000 0.000 4.000 -1.000 0.100 2.000 -2.000 0.000 0.000 -3.000 -0.100 -5.000 -4.000 -2.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues, for a sample of Low and Middle Income countries according to the World Bank classiï¬?cation. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endogenous vari- ables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. 178 A World Bank Study Figure 5A.11. Cumulative IRFs for Higher Middle Income Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.080 0.400 1.000 0.800 0.060 0.600 0.200 0.040 0.500 0.400 0.000 0.020 0.200 0.000 -0.200 0.000 0.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.150 0.500 1.000 1.000 0.100 0.500 0.000 0.500 0.050 0.000 0.000 -0.500 0.000 -0.500 -0.050 -1.000 -1.000 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.200 0.500 1.000 1.500 0.000 1.000 0.100 0.000 -0.500 0.500 0.000 -1.000 -1.000 0.000 -0.100 -1.500 -2.000 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues, for a sample of High and Middle Income countries according to the World Bank classiï¬?cation. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endogenous vari- ables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 179 Figure 5A.12. Cumulative IRFs for Different Debt Levels Panel A. Low Debt Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.300 0.200 0.200 0.000 -0.010 0.200 0.000 0.000 -0.020 -0.200 0.100 -0.200 -0.030 -0.400 -0.040 0.000 -0.400 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 1.000 1.000 0.500 0.000 0.500 -0.050 0.500 0.000 0.000 -0.100 0.000 -0.500 -0.500 -0.150 -1.000 -0.500 -1.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.100 0.000 0.500 0.000 -0.500 0.000 0.050 -0.500 -1.000 -0.500 0.000 -1.500 -1.000 -0.050 -1.000 -2.000 -1.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years (Figure continues on next page) 180 A World Bank Study Figure 5A.12 (continued) Panel B. High Debt Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.020 0.800 0.400 0.500 0.000 0.600 0.000 0.400 0.200 -0.020 -0.500 0.200 0.000 -0.040 0.000 -1.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.100 1.000 2.000 1.000 0.050 0.500 1.000 0.000 0.000 0.000 0.000 -0.050 -1.000 -0.100 -0.500 -1.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.600 0.000 4.000 15.000 0.400 -5.000 2.000 10.000 0.200 -10.000 0.000 5.000 0.000 -0.200 -15.000 -2.000 0.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. Panels A and B reports the results for countries with debt to GDP ratio below and above the sample median respectively. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endogenous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 181 Figure 5A.13. Cumulative IRFs by Debt Controlling for Income Level Climatic, GDP Climatic, Govt Expenditure Climatic, Govt. Revenue 0.080 0.010 0.020 0.060 0.000 0.000 0.040 -0.020 -0.010 0.020 -0.040 0.000 -0.020 -0.060 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt Expenditure Geological, Govt. Revenue 0.050 0.300 0.400 0.000 0.200 0.200 -0.050 0.100 0.000 -0.100 0.000 -0.150 -0.100 -0.200 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues, for countries with high and low levels of debt controlling for differences in income. The solid lines show the impact of a type of disaster, for countries with low (thin line) and high (thick line) debt levels (25th and 75th sample percentiles of debt to GDP ratio respectively). The do ed lines show one standard deviation conï¬?dence bands. 182 A World Bank Study Figure 5A.14. Cumulative IRFs for Different Levels of Financial Development Panel A. Financially Underdeveloped Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.000 0.200 0.400 0.200 -0.010 0.100 0.100 0.200 -0.020 0.000 0.000 0.000 -0.100 -0.030 -0.040 -0.200 -0.100 -0.200 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 0.500 1.000 1.000 -0.050 0.500 0.500 0.000 -0.100 0.000 0.000 -0.150 -0.500 -0.500 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.200 0.000 0.000 0.000 -0.500 0.100 -1.000 -0.500 -1.000 0.000 -2.000 -1.000 -1.500 -0.100 -2.000 -3.000 -1.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years (Figure continues on next page) Financial and Fiscal Instruments for Catastrophe Risk Management 183 Figure 5A.14 (continued) Panel B. Financially Developed Countries Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 1.500 0.800 0.500 0.040 0.600 1.000 0.000 0.020 0.400 0.500 -0.500 0.200 0.000 0.000 0.000 -1.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.150 2.000 1.000 4.000 0.100 0.500 0.000 2.000 0.050 0.000 -2.000 0.000 0.000 -0.500 -0.050 -4.000 -2.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.100 2.000 1.000 4.000 0.000 0.000 0.000 2.000 -2.000 0.000 -0.100 -1.000 -2.000 -0.200 -4.000 -2.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. Panels A and B report the results for countries with the average ratio of private credit to GDP, below and above the sample median respectively. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and including two lags. The order of the endogenous variables entered in the VAR is the following: govern- ment expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The govern- ment deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. 184 A World Bank Study Figure 5A.15. Cumulative IRFs by Financial Development Controlling for Income Level Climatic, GDP Climatic, Govt Expenditure Climatic, Govt. Revenue 0.150 0.050 0.020 0.100 0.000 0.000 0.050 -0.020 -0.050 0.000 -0.040 -0.050 -0.100 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt Expenditure Geological, Govt. Revenue 0.050 0.400 0.600 0.000 0.400 0.200 -0.050 0.200 0.000 0.000 -0.100 -0.150 -0.200 -0.200 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues, for countries with high and low levels of ï¬?nancial development controlling for differences in income. The solid lines show the impact of a type of disaster, for countries with low (thin line) and high (thick line) levels of ï¬?nancial development (25th and 75th sample percentiles of average ratio of private credit to GDP respectively). The do ed lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 185 Figure 5A.16. Cumulative IRFs for Countries with Low Insurance Penetration Panel A. Countries with Low Insurance Penetration Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.020 0.300 0.400 0.300 0.010 0.300 0.200 0.200 0.000 0.100 0.200 -0.010 0.100 0.000 0.100 -0.020 -0.100 0.000 0.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.000 0.600 1.000 1.000 -0.050 0.400 0.500 0.500 0.200 -0.100 0.000 0.000 0.000 -0.150 -0.200 -0.500 -0.500 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.100 0.000 0.000 1.000 0.000 -0.500 -1.000 0.000 -0.100 -1.000 -2.000 -1.000 -0.200 -1.500 -3.000 -2.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years (Figure continues on next page) 186 A World Bank Study Figure 5A.16 (continued) Panel B. Countries with High Insurance Penetration Climatic, GDP Climatic, Govt. Deficit Climatic, Govt. Expenditure Climatic, Govt. Revenue 0.040 0.500 1.500 0.600 0.030 0.000 1.000 0.400 0.020 -0.500 0.200 0.500 0.010 0.000 -1.000 0.000 0.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt. Deficit Geological, Govt. Expenditure Geological, Govt. Revenue 0.300 1.500 2.000 4.000 1.000 0.200 0.000 2.000 0.500 0.100 -2.000 0.000 0.000 -0.500 -2.000 0.000 -4.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Other, GDP Other, Govt. Deficit Other, Govt. Expenditure Other, Govt. Revenue 0.100 2.000 0.000 2.000 -0.500 0.000 0.050 0.000 -1.000 0.000 -2.000 -1.500 -2.000 -0.050 -2.000 -4.000 -5 0 5 10 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues. Panels A and B report the results for countries with total value of premium to GDP ratio below and above the sample median respectively. GDP and government deï¬?cit are expressed in real per capita terms; government expenditures and revenues are expressed as fractions of the long run government deï¬?cit. The parameters used to estimate the IRF come from the baseline speciï¬?cation with all variables expressed in levels (except the interest rate), and includ- ing two lags. The order of the endogenous variables entered in the VAR is the following: government expenditures, GDP, inflation, interest rate, and government revenues. The model also includes country speciï¬?c means and trends, and with time ï¬?xed effects that capture global variables. The government deï¬?cit is obtained as the weighted difference of revenues and expenditures. The solid lines show the cumulative percentage deviation of each variable from its trend resulting from a climatic, geological or other natural disasters occurred at time 0 (time in years). The do ed lines show one standard deviation conï¬?dence bands. Financial and Fiscal Instruments for Catastrophe Risk Management 187 Figure 5A.17. Cumulative IRFs by Insurance Penetration Controlling for Income Level Climatic, GDP Climatic, Govt Expenditure Climatic, Govt. Revenue 0.060 0.100 0.150 0.040 0.100 0.020 0.050 0.050 0.000 0.000 0.000 -0.020 -0.050 -5 0 5 10 -5 0 5 10 -5 0 5 10 Geological, GDP Geological, Govt Expenditure Geological, Govt. Revenue 0.200 0.500 0.400 0.100 0.200 0.000 0.000 0.000 -0.100 -0.500 -0.200 -5 0 5 10 -5 0 5 10 -5 0 5 10 Time in years Note: The ï¬?gure shows the cumulative impulse response functions (IRF) for GDP, government deï¬?cit, government expenditures, and government revenues, for countries with high and low levels of insurance penetration controlling for differences in income. The solid lines show the impact of a type of disaster, for countries with low (thin line) and high (thick line) levels of insurance penetration (25th and 75th sample percentiles of total value of premium to GDP ratio respectively). The do ed lines show one standard deviation conï¬?dence bands. CHAPTER 6 Overall Conclusions of the Report M egaflood disasters in the V-4 countries of Central and Eastern Europe (Poland, the Czech Republic, Hungary, and the Slovak Republic) have resulted in major losses in the last two decades. Based on historical and projected trends, future losses could easily represent signiï¬?cant shares of countries’ GDP and government revenues, thus generating severe disruptions in ï¬?scal ï¬?nances and economic activity. While there exist pan-European mechanisms such as the EU Solidarity Fund to help EU members fund mega disasters, these only kick in at extremely high loss lev- els. Such funds can also be delayed in terms of disbursing for several months and are not entirely suitable for immediate emergency funding. Coupled with the fact that V-4 government budgets which are reserved for natural catastrophes are modest, this calls for more state-of-the-art risk management arrangements for country governments to consider. Given these issues, the governments of the V-4 countries should consider it a priority to set up risk transfer mechanisms to reduce ï¬?scal volatility following natural catastrophes. Such mechanisms should count on joint country cooperation under collec- tive risk pooling systems which will signiï¬?cantly reduce the costs of such instruments versus if each country arranged them on their own. In this regard, the report aims to provide the roadmap for policy makers in the Finance Ministries and other pertinent government offices, to understand the design parameters, costs, and beneï¬?ts of estab- lishing the pooled ï¬?scal insurance mechanisms proposed for the V-4 region. The private sector insurance markets in the V-4 countries appear adequate and reflect rather high levels of penetration in the economy and in the housing sector. In this regard, there does not appear to be a strong need to establish any catastrophe risk support mechanisms for the private sector or the insurance industry. However, for the government sector, there are signiï¬?cant exposures of properties, infrastructure, and other assets. While governments can always raise funds in the international bond mar- kets following a disaster, it would be prudent to have pre-arranged mechanisms in place to provide quick liquidity to handle emergency needs and priority reconstruction of life lines and key infrastructure, as well as to ensure temporary shelter if required. Economic and ï¬?scal analyses based on global data also shows that countries with insurance mechanisms and markets show a stronger GDP recovery path and lower ï¬?scal deï¬?cits following a disaster. The analysis is based on a large sample of countries including many with similar economic characteristics as the V-4 countries, and makes a strong case for relying on insurance and ï¬?nancial market mechanisms to achieve more sustainable economic and ï¬?scal paths in natural disaster prone countries. However, the V-4 countries, having a common hazard of flood, are in a unique po- sition to develop highly cost effective flood insurance mechanisms. While these coun- tries on their own, could design efficient ï¬?nancing and insurance structures to supple- ment ï¬?scal resources in the event of major catastrophes, they would also obtain greater 189 190 A World Bank Study beneï¬?ts from sharing risks under ‘pooled’ mechanisms that would be er leverage the limited funds to support such schemes. Actuarial and portfolio risk analysis shows that all countries would beneï¬?t by lowering ï¬?nancing spreads if a pooled structure was used to insure ï¬?scal resources against disaster losses. As countries in general are more concerned with supplemental ï¬?scal resources rather than individual property losses, the governments of the V-4 countries can con- sider parametric style contracts. This effectively means that contractual ï¬?nancial pay- ments could be arranged and paid by insurers or investors (depending on the instrument used) and triggered by a physical measurement, for example, a threshold flood height in selected river catchments in the V-4 countries. Such contracts avoid any moral hazard in terms of measuring exact losses. This report has demonstrated how such “water level/ flowâ€? based contracts could be designed and provided several options. Nevertheless, risk transfer or insurance mechanisms are not the only types that need to be considered. Government budget reserves for disaster ï¬?nancing (as currently practiced) should also constitute an important element in these risk ï¬?nancing strategies. Contingent prearranged loans may also be considered at upper loss levels since these carry close to a zero cost to have in place if no event occurs. But they should be used mainly for these higher level losses which occur infrequently, so as to avoid unnecessar- ily building up debt. One of the options to consider is for the V-4 countries to jointly issue a catastro- phe bond against flood risks. Such a bond would be purchased by investors and they would beneï¬?t from a bond return that is not correlated to the ï¬?nancial markets as it is based on unrelated flood risks. However, if the threshold flood event does occur, the principal proceeds of the bond would not be repaid by the governments and used for reconstruction. This would not constitute any default as such arrangements are standard contractual conditions for catastrophe bonds. Another option that would likely lower costs for the V-4 governments would be the establishment of their own dedicated ï¬?scal insurance fund. If adequately funded up-front, and with some reliance on the reinsurance market, this would reduce ongoing insurance costs substantially and provide a mechanism for ï¬?scal compensation when the measured flood levels in the rivers of each of the countries, reached threshold heights. The analysis in this report is meant to show, besides the ï¬?nancial mechanisms that would be beneï¬?cial for risk management, what large catastrophe exposures ex- ist, and their relation to government ï¬?nances and macroeconomic measures. While the analysis in this report was conducted by technical experts in the ma er, govern- ments should nevertheless engage in more detailed risk exposure analysis in each of their countries, to make a full assessment of level of the risks and losses they may be facing on account of recurrent flood hazards in the region. Following a ï¬?nal phase of feasibility analysis and market testing, the V-4 coun- tries should thus consider establishing a multicountry insurance pool to provide fast emergency funding after disasters. Such a pool can price member contributions based on their individual risk exposures while accruing large savings to all members by struc- turing it as a combined risk portfolio. An alternative innovative approach would be the joint issuance of a catastrophe bond to investors, which, besides not counting as public debt given its special insurance structure, would immediately compensate government with the bond proceeds held in trust, if a major catastrophe were to generate high losses. Bibliography Chapter 1 References Canabarro, E., and M. Finkemeier. 1998. “Analyzing Insurance Linked Securities.â€? Gold- man Sachs Fixed Income Research, October. Carpenter, Guy. 2010. “World Catastrophe Reinsurance Market.â€? Available at: h p:// gcportal.guycarp.com/portal/extranet/popup/insights/reportsPDF/2010/World_ Cat_2010.pdf. CRED—Centre for Research on the Epidemiology of Disasters. 2010. Database on world- wide disasters. Froot, K. 1999. “The Market for Catastrophic Risk: A Clinical Examination.â€? National Bureau of Economic Research, Cambridge, MA. August. International Monetary Fund (IMF). 2009. “International Financial Statistics.â€? IMF, Washington, DC. Melecky, Martin and Claudio Radda . 2011. “How Do Governments Respond after Catastrophes? Natural-Disaster Shocks and the Fiscal Stance.â€? Policy Research Working Paper 5564. World Bank, Washington, DC. Pollner, J. 2001. “Managing Catastrophic Disaster Risks Using Alternative Risk Financ- ing and Pooled Insurance Structures.â€? World Bank, Washington, DC. Swiss Re and Sigma. 2010. “Natural Catastrophes and Man-Made Disasters in 2009.â€? Available at: h p://www.swissre.com/sigma/. 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ECO-AUDIT Environmental Beneï¬?ts Statement The World Bank is commi ed to preserving In 2010, the printing of endangered forests and natural resources. this book on recycled paper The Office of the Publisher has chosen to saved the following: print World Bank Studies and Working • 11 trees* Papers on recycled paper with 30 percent • 3 million Btu of total postconsumer ï¬?ber in accordance with the energy recommended standards for paper usage • 1,045 lb. of net greenhouse set by the Green Press Initiative, a non- gases proï¬?t program supporting publishers in • 5,035 gal. of waste water using ï¬?ber that is not sourced from endan- • 306 lb. of solid waste gered forests. For more information, visit www.greenpressinitiative.org. * 40 feet in height and 6–8 inches in diameter F inancial and Fiscal Instruments for Catastrophe Risk Management: Addressing Losses from Flood Hazards in Central Europe is part of the World Bank Studies series. These papers are published to communicate the results of the Bank’s ongoing research and to stimulate public discussion. This applied study addresses the large flood exposures of Central Europe and proposes efï¬?cient ï¬?nancial and risk transfer mechanisms to mitigate ï¬?scal losses from such natural catastrophes. In 2010 the Visegrad Four (V-4) countries (the Czech Republic, Hungary, Poland, and the Slovak Republic) demonstrated their historical vulnerability to floods—Poland suffered US$3.2 billion in flood-related losses, comparable to its US$3.5 billion in losses in 1997. Flood modeling analysis of the V-4 countries shows that a disaster event with a 5 percent probability in any given year can lead to economic losses in these countries between 0.6 percent and 1.9 percent of GDP, as well as between 2.2 percent and 10.7 percent of government revenues. Larger events could quadruple such losses. The European Union Solidarity Fund is available as a mechanism for disasters, but it comes into effect at only very high levels of losses, does not provide sufï¬?cient funding, and is not expeditious. An insurance-like mechanism for national governments can be tailored for country portfolio needs for buildings, properties, and critical infrastructure. By virtue of the broad territorial scope, ï¬?scal support should use mechanisms that provide payments triggered by physical flood measurements in selected areas (rather than site-by-site losses as in the traditional insurance industry). A multicountry mechanism for insurance that pools risks to protect infrastructure can also provide major cost efï¬?ciencies for all governments, using parametric or index contracts. Savings from pooling can range from 25 percent to 33 percent of the ï¬?nancing costs that each country would otherwise have to pay on its own. There are several instruments and options for both insurance- and debt-ï¬?nanced mechanisms for funding catastrophes. All instruments can be analyzed based on equivalencies in terms of market spreads. One hybrid-like instrument, the catastrophe bond, is a risk transfer instrument that is structured as a debt security. The V-4 countries should begin to set up such ï¬?nancial mechanisms to prevent major ï¬?scal disruptions or losses from future catastrophic floods. The instruments proposed can be market tested and supplemented with exacting studies on hydrology and topography used to ï¬?ne-tune the loss estimations per event and where property and infrastructure are exposed. World Bank Studies are available individually or on standing order. This World Bank Studies series is also available online through the World Bank e-library (www.worldbank.org/elibrary). ISBN 978-0-8213-9579-0 SKU 19579