Weather insurance in India - A snaphot (2010)
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Weather insurance in India - A snaphot (2010)

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    Weather insurance in India - A snaphot (2010) Weather insurance in India - A snaphot (2010) Presentation Transcript

    • Scaling Crop Weather Index Insurance Lessons from India 1
    • Basis Risk: The primary hurdle Imperfect relationship between index & targeted loss Distance from the settlement metrological station Differing scales of risk faced by insurer & farmers Time averaged meteorological indices makes farmers responsible for relating them to production losses 2
    • 3 Exogenous Meteorological Indices Not adjusted to farms yields (cumulative rainfall) Extremely easy to design Carries high basis risk Yield Tailored Meteorological Indices Designed using regression of yield on weather pattern Maximum average risk reduction. More than one weather parameter can be used
    • 4 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0 2 4 6 8 10 12 14 16 18 waterusgae week after emergence -> Av corn water use based on maximum daily air temp. 50° - 59° 70° -79° 90° -99° Water Stress based loss models are the good aids for their ease of use.
    • 5 Risk assessment factors at a development stage Vulnerability to a particular weather peril Prevalence of the said weather peril Microclimate factors Corn Soil Moisture Stress Criteria Growth Stage Period Vulnerability Emergence – Onset of tassels 40 days Can withstand up to 60% soil water depletion Tassel onset - Blister kernel stage 40 – 80 days Root zone not more than 50% water deficient Post Blister kernel stage > 80 days Can withstand 60% water depletion with out yield reduction
    • Growth stage Evapotranspiration (inches per day) Percent yield loss per day of stress (min-ave-max) Seedling to 4 leaf 0.06 --- 4 leaf to 8 leaf 0.1 --- 8 leaf to 12 leaf 0.18 --- 12 leaf to 16 leaf 0.21 2.1 - 3.0 - 3.7 16 leaf to tasseling 0.33 2.5 - 3.2 - 4.0 Pollination 0.33 3.0 - 6.8 - 8.0 Blister 0.33 3.0 - 4.2 - 6.0 Milk 0.26 3.0 - 4.2 - 5.8 Dough 0.26 3.0 - 4.0 - 5.0 Dent 0.26 2.5 - 3.0 - 4.0 Maturity 0.23 0 Estimated corn evapotranspiration & yield loss per stress day during various stages of growth.
    • s7 The index is triggered when a pre defined weather event occurs which is potentially damaging. A good index should capture all the damage points. Should account for the demanded protection by farmers The triggers for a product will depend on the index type. Eg. In a water deficit index distribution of rainfall is more important consideration than the total volume
    • 8 Deficient Rainfall Reduction in rainfall Kharif grain production fall 1982 -14% -12% 1987 -19% -7% 2002 -19% -19% 2009 -23% -16%
    • Premium = Expected Loss + Risk Margin + Admin Charges 9 Expected Loss is the average payout for the product in any year Risk Margin is to compensate the insurer for taking the risk of unexpected payouts Administrative charges like marketing cost, taxes, reinsurance & brokerage charges.
    • 10 Pricing using Burn Analysis Straightforward & simple. Good for 1st look. Few assumptions, hence lesser error points Low level of accuracy Pricing using Index Modeling More complex. A distribution is fitted to underlying weather indices To be preferred when long data series is available. Modeling error can lead to greater level of inaccuracies
    • 11 Risk Margin: Dependencies Trends in weather pattern Missing historical weather data Unprecedented weather events Current pricing methodologies uses VaR of the contract to determine the risk margin.
    • 12 “Actual” Premium depends on many factors Actuarial premium Economic profile of the target farmers Marketing Cost Marketing cost comprise almost 10-15% of premium in retail sales. Willingness to purchase weather insurance Deductibles Insurance schemes associated with contract farming operations have lower rates.
    • 13 The promise of Weather Insurance Fast claim settlement Independently variable loss data Hassle free claim settlement process Roadblocks to efficient claim handling Settlement Data issues Administrative inefficiency Very few bank account holders amongst the farmers
    • 14 Settlement Data Issues Failure of primary weather stations Inadequate indemnity when compared to actual loss Primary St. Secondary St.
    • 15 Admin. Inefficiencies in claim handling 3rd party observers take time to release verified data Data is processed at multiple levels Admin issues (claim verification, cheque printing) Cheque distribution to individual farmers
    • Obstacles to Acceptability Complex models can be considered opaque by the farmers The farmers may view the process to be vulnerable to manipulation Farmers perceive premium amount lost in case of no payout. Needed a balanced trade off between basis risk and farmer’s ease of understanding.
    • Obstacles to a Sellable product Poorly trained ground staff often unable to explain the product to farmers Limited numbers of “Trusted” Point of Sales. Below par post sales service. Needed a balanced trade off between basis risk and communication challenges. 17
    • Handling Product Complexity Split product according to multiple weather variable for ease 18 Lesson Learned from India Handling Trust Issues Local agro-input dealers, cooperatives, NGOs as POS Handling Service Quality Issues Daily weather forecast, actual data & agro-advisory messages
    • 19 GIS in weather index insurance Vulnerability map of a region Can help cut down location specific basis risk Can help in claim settlement process Rainfall & topography to calculate runoff Usage in loss calculation To model evapotranspiration for arid regions Flood Inundation
    • 20 TERMSHEET FOR WEATHER INDEX INSURANCE FOR LAC Peril Type 1 Damage due to high fluctuation in Max temperature Peril Period 1 Feb 1 to Feb 28 Varying Temp Index (VTI) Cumulative value of deviation in daily temperature over the Peril period VTI Strike 35 Notional 2% VTI Index ( HDD - Strike ) * Notional Peril Type 2 Damage due to high temperature Peril Period 2 Mar 16 to Apr 15 High temp Index (HTI) HDD of Max temp above 37* C Loss rate in % HDD 1st 15 days 2nd 15 days 15 4% 2% 25 8% 4% Loss Cap 70% 30% Index VTI + HTI Index Threshold 30 Notional (Rs./unit) 20 Max Sum Insured 900 Premium (incl Service Tax) 125
    • 21 TERMSHEET FOR WEATHER INDEX INSURANCE FOR SALT PRODUCTION Risk Index Excess Rainfall Wet Spell Strike 2 consecutive days of rainfall greater than 15 mm Wet Spell Exit 3 consecutive dry days Policy Period Phase 1 Phase 2 10 Jan – 15 Mar 16 Mar – 31 May Index_1 (Production losses due to wet spell) Loss index in % 15mm < Rain ≤ 50mm 3% 3% 50mm < Rain ≤ 100mm 8% 8% 100 mm < Rain 15% 25% Index_2 (consequential losses for each rainy day) Extra loss for each rainy day (Eloss) 0.40% 0.80% Index_2 = Wet Days Count X Eloss TLI { Index_1 + Index_2 - 25% } Notional (amount paid for each point of TLI) 0 < TLI ≤ 25 50 25 < TLI 105 Payout = TLI X Notional Sum Insured = Rs. 6500 Premium = Rs. 800
    • 22 Excess Rainfall Index (ERI) Stages Initial Phase Dev Phase Mid Phase End Phase Dates 15 Jul – 13 Aug 14 Aug – 17 Sep 18 Sep – 22 Oct 23 Oct – 21 Nov Excess Rainfall Index ERS 1 150 150 125 100 ERS 2 25 25 15 15 ERC 1 Cumulative rainfall of two consecutive days – ERS1 ERC 2 If ERC_1 > 0, sum of rainfall of subsequent days till rainfall < ERS2 for two consecutive days Payoff ∑ {(ERC 1 + ERC 2) PHASE X Notional PHASE } Maximum payoff 2000 TERMSHEET FOR WEATHER INDEX INSURANCE, COTTON (AP)
    • 23 Water Deficit Index (WDI) Stages Initial Phase Dev Phase Mid Phase End Phase Precipitation Weight 1.5 1.5 0.5 NA Index Strike 400 Notional (Rs.) 4 Payoff formula Max [0, (Index Strike – Actual Index) X Notional] Max Payoff 2000 Loss Calculation Deductible Rs. 200 Max S.I. 4000 Total Payoff Max (4000, payoff for ERI+ payoff for WDI – Deductible) Premium 400
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