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UKRAINIAN AGRICULTURAL WEATHER RISK MANAGEMENT WORLD BANK COMMODITY RISK MANAGEMENT GROUP Ulrich Hess Joanna Syroka PhD January 20 2004 UKRAINIAN AGRICULTURAL.

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Presentation on theme: "UKRAINIAN AGRICULTURAL WEATHER RISK MANAGEMENT WORLD BANK COMMODITY RISK MANAGEMENT GROUP Ulrich Hess Joanna Syroka PhD January 20 2004 UKRAINIAN AGRICULTURAL."— Presentation transcript:

1 UKRAINIAN AGRICULTURAL WEATHER RISK MANAGEMENT WORLD BANK COMMODITY RISK MANAGEMENT GROUP Ulrich Hess Joanna Syroka PhD January UKRAINIAN AGRICULTURAL WEATHER RISK MANAGEMENT WORLD BANK COMMODITY RISK MANAGEMENT GROUP IFC PEP Ukraine Ulrich Hess Joanna Syroka PhD January Weather Index Insurance for Agriculture COMMODITY RISK MANAGEMENT GROUP The World Bank 13 th October 2006 William J. Dick

2 Overview of the Commodity Risk Management Group (CRMG), the World Bank Index-based Weather Insurance How to develop a Weather Insurance program? Extending the index concept to flood insurance OUTLINE

3 CRMG Overview

4 CRMG facilitates…. Market-based Risk Transfer Products Weather index-based insurance Price risk management contracts New Applications Disaster risk financing Extension to new hazards Access to risk capital Access to global reinsurance markets Knowledge Transfer and Education Technical assistance in projects Publications and training workshops Existing Transactions India, Ethiopia, Malawi, Ukraine.…

5 Feasibility Study Pilot DesignPilot Implementation CRMG global activities

6 New Model: Index-based Weather Insurance

7 Motivation Traditional crop insurance Multi-Peril Crop Insurance (MPCI) Yield-based insurance is not sustainable Named peril Crop Insurance Damage-based insurance is viable for selected localised perils Main Problems Loss adjustment and farm level data Moral hazard Adverse selection due to asymmetric information High monitoring and administrative costs Often heavily subsidised Operationally difficult for small farmer agriculture

8 Experience with public crop insurance Condition for sustainability: (A+I)/P < 1 Where: A = average administrative cost I = average indemnities paid P = average premiums paid CountryPeriod(A+I)/P Brazil Costa Rica Japan Mexico Philippines USA Source: Hazell Financial performance of crop insurance

9 Index insurance Challenge Design an alternative, efficient and cost-effective crop failure insurance program that facilitates risk transfer and is feasible for small farmers in low- income countries.

10 What are index insurance contracts ? An index insurance contract indemnifies based on the value of an index- not on losses measured in the field An index is a variable that is highly correlated with losses and that cannot be influenced by the insured Example indices: rainfall, temperature, regional yield, river levels Index insurance contracts overcome most of the supply side problems of traditional insurance contracts

11 Main characteristics of an index Observable and easily measured Objective Transparent Independently verifiable Able to be reported in a timely manner Stable and sustainable over time Weather indexes can form the basis of an insurance contract that protects farmers from weather risk

12 Payout structure: drought protection Financial payout - increment per mm Maximum Payout Trigger Rainfall LevelLong-Term Average Rainfall Deficit Rainfall Index (mm.) EXAMPLE OF PAYOUT STRUCTURE Payout (unit per ha)

13 Index insurance: Advantages and challenges AdvantagesChallenges Less moral hazard and adverse selectionBasis Risk Timely payoutSustainability of the index Lower administrative costsPrecise actuarial modeling Standardized and transparent structureEducation Availability and negotiabilityMarket Size Reinsurance acceptabilityForecast VersatilityMicro climates

14 How can index instruments be used ? Micro levelWeather-indexed insurance for smallholder farmers, intermediated through institutions with rural outreach Ex. India, Nicaragua, Malawi, Ukraine Meso levelWeather-indexed portfolio hedge for rural financial institutions that lend to poor farmers Ex. India Macro levelWeather insurance or weather-indexed contingent credit line for governments or international organizations that provide safety nets for the poor Ex. Ethiopia, Malawi

15 The global market Deals transacted: Argentina I – Weather insured seed credit Argentina II – Dairy yield protection against low rainfall South Africa – Apple co-operative freeze cover India – Approximately 250,000 insured against poor monsoon Mexico – Crop insurance portfolio reinsurance through weather derivative structure Canada (Ontario) - Forage insurance with weather indexation Canada (Alberta) - Heat index insurance for maize Ukraine – Winter wheat protection against weather risks Malawi – Weather insurance pilot for groundnut farmers Ethiopia – WFP Drought Insurance Under preparation: Morocco – Wheat yield protection against drought Zambia – Maize yield protection against drought Nicaragua – Bank-intermediated weather insurance for groundnut farmers Thailand – Bank-intermediated weather insurance

16 How to develop a weather insurance program ?

17 Developing a pilot program I. Identify significant farmer exposure to weather II. Quantify the impact of adverse weather on their revenues III. Structure a contract that pays out when adverse weather occurs IV. Execute contract (with insurers and a delivery channel) V. Secure international reinsurance

18 High Probability, Low Consequence Risks Vs. Low Probability, High Consequence Risks High probability Low Consequence Reduced yields The producers generally perceive this as their risk Normal weather Low probability High Consequence Extremely low yields Low probability High Consequence Extremely low yields Extreme weather events (excess rainfall or flood) Extreme weather events (droughts)

19 The cropping calendar *Maize yields are particularly sensitive to rainfall during the tasseling stage and the yield formation stage – rainfall during the latter phase determines the size of the maize grain Diagram taken from the FAOs maize water requirement report* Sowing and establishment period is also critical crop survival A rainfall index is normally split into 3 or more crop growth phases Objective: maximise the correlation between index and loss of crop yield

20 Maize Rainfall Index - example Phase 1Phase 2Phase 3 Seedling Emergence to Knee High VegetativePhysiological Maturity Days Trigger (mm.) Limit (mm.) Tick size (Baht/mm./rai) 4221 Sum Insured (Baht/rai) 1,2001,6001,700

21 Index v. maize yield example

22 Distribution and risk transfer Bank-intermediated weather insurance contracts to farmers Insurance Company/ Syndicate Global Reinsurance Companies Reinsurance treaty National International Farmers Weather insurance contracts Agricultural Bank Contractual relationship (risk transfer, services, operations etc.)

23 Weather index insurance - summary The product is simple and weather measurements can be understood by farmers Basis risk can be reduced by increasing the density of low cost weather stations Low cost of distribution and loss adjustment Less specialist knowledge needed to underwrite the product The product is suited for catastrophe hazards The product is highly flexible and can multiply in the insurance market Reinsurers are interested to accept the risk

24 Extending the concept to flood insurance

25 The flood risk in Asia

26 Flood insurance concept Design a flood index which can proxy losses caused to crop Rice is the strategic crop most exposed to flood Flood impact is dependent on variety, time of occurrence, depth, speed and duration of flood water Harness technology to support insurance underwriting and operations 2 key components for index design phase Flood modelling (FM) Agro meteorological modelling (AMM) 2 key components for operational phase Geographical information system Earth Observation (EO)

27 Pasak River LA4 LA2 LA3 LA1 LA5 High Risk Pricing Zone Medium Risk Pricing Zone Low Risk Pricing Zone

28 Summary: Combining the Technology Components FM + AMM Design a flood index that proxies crop loss FM+EO+GIS Define flood risk zones and pricing the contract EO+ GIS Loss adjustment for payout determination according to the index FM: flood modelling. AMM: Agro-meteorological modelling. EO: Earth observation. GIS: Geographical Information System.

29 Remote sensing can measure flooded areas Flood assessment based on SAR - Bangladesh 07/2004 River gauges Flood map

30 Challenges in indexing flood risk Types of flood risk River inundation flood Flash flood Typhoon induced flood Coastal surge flood Challenges Zoning for insurance purposes Defining macro or micro level insurance products Pricing flood risk Influence of flood management practices on risk Avoidance of anti-selection Simplifying the product CRMG is still in the research phase Thailand, Vietnam and Bangladesh


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