Agriculture Insurance in India

Crop Insurance market in India 25 million out of 120 million farmers (20%) are insured under crop insurance schemes 90% are loanee farmers. 10% penetration among non-loanee farmers Weather Index Insurance – Perhaps the World’s largest weather-based crop insurance programme. 12 million farmers covered - Implemented in 16 states. Area Yield Index Insurance - World’s largest Crop insurance programme, million farmers covered Implemented in 25 states. Government of India targets doubling the farmers’ coverage during 12th plan from 25 million to 50 million.

Background Area Yield Approach suggested back in 50’s – Provinces mooted a proposal requesting GoI for financial assistance in the early 1960s. Ministry of Food & Agriculture - examined the feasibility of crop insurance – Circulated a draft scheme to all the States – Not favored by states due to paucity of fund Elaborate administrative machinery not available & paucity of resources – Each insured area to be divided into blocks with one Crop Insurance Inspector and 10 crop insurance sub inspectors. Geographically homogeneous regions – Difficulty in delineating - absence of data on area-wise farming practices.

Background GoI introduced a Crop Insurance Bill & a Model Scheme of Crop Insurance – referred to Dharam Narain Committee – stalled the progress. Grounds – More emphasis on elements on “individual approach” Breakdown of insurance principle – “ The number of claimants turns out to be nearly as large as that of the premium paying farmers”

Background Admittedly, we came to consider it as second best as we found a crop insurance based on ‘individual approach totally impractical. Now, instead of making it impractical by importing into it elements of individual approach, we should accept it as the second best and agree to give it a fair trial”- Dandekar

Background Mid 1980s onwards - Studies reflecting the dismal performance of all risk crop insurance programmes world- wide- Rainfall insurance suggested as a response to the unsatisfactory performance of crop insurance in the past decades. CCIS in early Eighties Modified into NAIS late nineties – A separate company constituted to implement the scheme Further modified in 2010 as MNAIS – MNAIS is an Insurance Product and not a scheme

Background World Bank (1992) – Drought insurance scheme for all rural households – All insured to pay the same premium and receive the same indemnity per unit of sum insured. Pioneering work by J.S. Chakravarti (1920). No insurance authority could ever maintain a supervising agency which would be able to watch and enforce that every insured field receives the required amount of care and attention at the hands of its cultivator. Unless some method can be devised by which this great difficulty is eliminated, a system of crop insurance would indeed be impossible”.

Background “ A famine in India does not mean grain famine but money famine due to enforced unemployment of agriculturist owing to unfavorable seasonal conditions. An effective system of agricultural insurance by insuring the peasantry against serious pecuniary loss in respect of agricultural operations will render the country less liable to the ravages of famine. In this sense and to this extent agricultural insurance will also be famine insurance”  First Weather Insurance product launched by private sector as an Insurance product Govt. allocated subsidy to weather insurance

Present State : Area Yield Index Insurance Area yield based approach. Covers –Crops subject to availability of past yield data (10 years). Mandatory for borrowing farmers/voluntary for others. Capped premiums for FCOS ( % of SI) and Actuarial rates for ACH crops. Yields measured through Stipulated Minimum Crop Cutting Experiments (CCEs). Ex-post financing for claims processing. (Not applicable in the modified version) Guaranteed yield – 60%/80%/90% of past 3/5 yrs avg. Sum Insured - amount of bank finance / value of guaranteed yield/ 150% of the value of Average Yield.

Area Yield Index Insurance Guaranteed Yield TY = 3 year/5 year moving average yield X IL. Linear trend resulting in low coverage and high premium rates. Unusually good or bad years have high impact Unrealistic uniform ILs/premium rates across the state. Low coverage levels in areas with continuous adverse seasons. Overstatement of yield in good years will increase premium rates despite low payment of indemnity. Modifications Detrended yield data Moving Average Last 7 years yield data (excluding 2 calamity yrs).

MNAIS: Improvements over NAIS

WBCIS – Business Spread 2 mio 0.1 mio mio 150 mio 3-4 mio 90 mio 0.2 mio 5-6 mio 1-2 mio 5-6 mio 70 mio 3-4 mio 4-5 mio High Low 6-8 mio In terms of premium in $ mn

State Specific Peculiarities StateIssues Regarding ProductsData Status Rajasthan High Frequency products. Termsheets are defined by the government. Major risk is drought for Kharif program and temperature for Rabi program. Good quality historical data. Large spread of settlement stations Bihar Aggressive strikes. Multiple iterations on the product. Drought in Kharif and high temperature covers in Rabi are aggressive. Poor quality of data hinders pricing. Poor quality historical data. Large spread of settlement stations. Karnataka 10 yr BPR should be at least 60% Product contours are not suggested by govt. Government also doesn’t define sum insured breakup among perils. Good quality historical data. Good settlement station spread. Tamilnadu Criteria similar to that of Karnataka but with BPR of around 5-6 years. Due to merging of two monsoons, large variations in rainfall exist. Good quality historical data. Reasonable Settlement station network. Haryana Govt suggests strike but allows insurance companies to define notional as well as second strikes. High temperature is major risk in Rabi. Reasonable data but with significant data gaps. Reasonable station network Chattisgarh Government provides the product details. Insurance companies can choose to take it or leave it Poor quality historical data. Inadequate settlement station network AP Govt supports only PSU insurer. Business underwritten only through AIC Data Quality is good Jharkhand Historical data of poor quality. Govt gives leeway in product design and pricing Data quality is very poor with most stations having large data gaps

WBCIS – Market $ 360 mn - Current weather insurance market $ 800 mn weather insurance market $ 200 mn weather insurance market States on the borderline bring more districts under WBCIS More states bring horticulture crops under WBCIS More competition pulls up the non loanee market States on the border line substitute WBCIS with MNAIS or NAIS WBCIS claim ratios remain too low and the payoffs do not cover actual losses States decide to reduce its subsidy burden by opting for MNAIS

Rajasthan Termsheet STATE- RAJASTHAN DISTRICTDausaAll Tahsil CROP -Bajra REFERENCE WEATHER STATIONAS PER NOTIFICATIONUNIT:/ HEC. DEFICIT RAINFALL PHASE IPHASE IIPHASE III RAINFALL VOLUME PERIOD01-July to 20 July 21 JUL TO -20 AUG21 AUG TO 30 SEPT. INDEXAggregate of rainfall over respective Phases STRIKE I(<) STRIKE II(<) EXIT000 RATE I (Rs./mm) RATE II (Rs./mm) MAXIMUM PAYOUT (Rs.) TOTAL PAYOUT (Rs.) PERIOD 01 JUL TO 10 SEPT RAINFALL DISTRIBUTION (Single pay out) INDEXNumber of the days in a spell of Consecutive dry days. STRIKE (=>)20 EXIT72 PAYOUT PER DAY(Rs.) TOTAL PAYOUT (Rs.)2000 EXCESS RAINFALL (Singal Payout) INDEX Maximum of 3 consecutive day's cumulative rainfall in respective Phases PHASE IPHASE IIPHASE III PERIOD01-July to 20 July 21 JUL TO -20 AUG21 AUG TO 30 SEPT. STRIKE I(>) STRIKE II(>) EXIT400 RATE I(Rs./mm) RATE II(Rs./mm) MAXIMUM PAYOUT (Rs.) TOTAL PAYOUT (Rs.) SUM INSURED (Rs.) PREMIUM (Rs.)660 PREMIUM %10% Note: Payout of Deficit rainfall and Excess rainfall will be calculated on "either or basis" the cumulative payout of respective phases will be compared and which ever is more will be considered

UP - Termsheet CROPPADDY DistrictMathura RISK COVERED - Deficit Rainfall IndexAggregate Rainfall of Respective Phases below Trigger Value 1-Jul-1316-Jul-131-Aug-1316-Aug-131-Sep-1316-Sep Jul-1331-Jul-1315-Aug-1331-Aug-1316-Sep-1330-Sep-13 Trigger 1 (mm) Trigger 2 (mm) Exit (mm) Payout Rate 1 Rs/mm Payout Rate 2 Rs/mm May Payout Total Max Payout35002 RISK COVERED - Excess Rainfall(2 days consecutive rainfall of respective phase(Multiple pay out) 1-Jul-1316-Jul-131-Aug-1316-Aug-131-Sep-1316-Sep-131-Oct Jul-1331-Jul-1315-Aug-1331-Aug-1316-Sep-1330-Sep-1315-Oct-13 Trigger 1 (mm) Trigger 2 (mm) Exit (mm) Payout Rate 1 Rs/mm Payout Rate 2 Rs/mm May Payout Total Max Payout17498 Total Sum Insured (Rs.)52500 Total Premium (Rs.)5250 Farmer's share (Rs.)1312.5

Bihar Termsheet

Claim Settlement Mechanism Over 2500 weather stations installed to settle weather insurance claims – Stations installed by approved third party administrators AWS prototypes certified by the local Met department State/provincial govts decide the no. of stations to be installed Insurance Companies pay the data fees Satlleite data being studied, fails to capture local variations Crop cutting experiments to be independently audited by third party service providers

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