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Providing Weather Forecasting Services along with Agricultural Insurance to Small Farmers in West Bengal Presentation on findings of Project End Report.

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Presentation on theme: "Providing Weather Forecasting Services along with Agricultural Insurance to Small Farmers in West Bengal Presentation on findings of Project End Report."— Presentation transcript:

1 Providing Weather Forecasting Services along with Agricultural Insurance to Small Farmers in West Bengal Presentation on findings of Project End Report Kinnary R. Desai CIRM

2 Why Agro-Insurance? Agriculture is important: -provides employment to 2/3 rd of our population -growth and development taking place But, vulnerable: -affected by volatility in input and output prices, -credit availability, -government regulations, subsidies, support prices - storage and processing risks and - production risks like unfavorable weather, catastrophe, diseases and pests Agro-insurance: one of the market based formal methods risk management in agriculture; Weather insurance to manage weather risks

3 Weather Index Insurance (WII) What is WII? A product whose pay-outs are linked to a publicly observable index, such as rainfall recorded on a local rain gauge - Gine, Townsend & Vickery (2008) Other weather indices: temperature, humidity, wind velocity, sunshine hours, etc. Advantages: -transparent -payout time minimized -adverse selection and moral hazard problems minimized -applicable in case historical yield data is absent Disadvantages: -high basis risk -complex framework, difficult to understand for farmers

4 The Intervention Background: -Comprehensive Agriculture Risk Management Services (CARM) launched in six remote districts of India: Howrah, Hooghly, Bankura,East and West Medinipur in West Bengal and Kamrup in Assam; analysis based on Howrah only. -Partners: WRMS and CIRM -37 Automated Weather Stations (AWS) installed in WB and 15 in Kamrup to record rainfall. Why? The Product: -WII (rainfall index) for paddy and potato farmers ; policy underwritten by ICICI Lombard and AIC. -perils covered: excess rainfall for Kharif crop (paddy) and unseasonal rainfall and extremely high temperatures during the cropping season, resulting in continuous crop disease conditions for Rabi crops (paddy and potato) -WII was bundled with weather forecasts (24 to 48 hrs from time of delivery) broadcasted through mobile SMSs as a value add. Why? -Delivery channel set up by WRMS- rural banks, NGOs, microfinance institutions, cooperatives, and contract farming and input firms

5 Research Questions and Methodology What are the factors that determine the take-up of the weather index insurance product by farmers? -Logit regression Model Has the dissemination of weather forecasts through SMS helped the insured to mitigate and manage risk posed by unfavourable weather? -Self Reported evidence of client farmers What is the impact of weather insurance on the production related decisions of the client group? -Feedback of client farmers

6 Determinants of Take-Up Client Charact- eristics Education: school enrollment Participation in Govt. Schemes Educational status/ product understanding & Risk Agriculture as Main Occupation Agriculture as Secondary Occupation Off Farm Activities (X 11 =1 if yes) Occupation & alternative means of risk management Liquidity constraints Number of Mobile Phones Value added services Response to agricultural shocks/risk perception Risk Covered Demographic Characteristics Logarithm of Annual Income Jewellery and livestock Type of house: Kutchcha Inputs financed through borrowing: paddy and potato Possession of water pump Risk lover Time to return to normal cons. Weather forecasts important and among top 3 factors important for good yield Area Cropped Total Input Cost Area owned (Bighas) Weather crop shock loss Joint Family Gender Category (general/SC/ST) Age Household Size Take-up

7 The Sample Data collection: -1/3 rd of the households (from 25 villages) from MIS information were listed between Dec and Jan sample selection was not stratified, but, was randomized; challenges faced -final sample of 211 client and 211 non-client households was selected Descriptive statistics: Desc. stat.ValueDesc. stat.Value Male84.6%Hindu98.1% Avg. age43.5 yrsGeneral Cat.75.8% Avg. hhld. size4.8 membersPast schooling91.7% Married87.9%Main occ.- agri83.4% Avg. area cropped2.7 bighasAvg. annual inc.Rs. 37,745

8 Model Specification Logit Regression

9 Results: Determinants of Take-Up Education: School enrollment has a significant positive impact on take-up; consistent with expectations Occupation: -clients are less likely to have agriculture as main occupation -counter-intuitive; might be due to liquidity constraints Agriculture as occupation Type of household ClientsNon clientsTotal Main occupation73.90%92.90%83.40% Assets Type of household ClientsNon clientsTotal Total Value of livestock (Rs.) Avg. value of jewellery (Rs.)

10 Contd. Borrowing: - % of clients (86% paddy; 5% potato) financing inputs through borrowing is significantly higher than non-clients (68%; 1% resp.) -significant coefficient -take-up to repay loan to maintain relation with moneylender Mobile Ownership: - 61% of clients and 49% of non-clients own mobile phones -number of mobile phones positively correlated to take-up -access to mobile phone necessary for utilization of VAS Risk Profile: -risk lovers have significantly low take-up -consistent with a-priory expectations Option Payouts in each case (in Rs.) % of clients who prefer each option HeadsTails % % %

11 Contd. Perceived importance of weather forecast: -70% of clients and 57% of non-clients ranked weather forecast among the top three requirements for good yield -significant positive coefficient Participation in Govt. schemes: - NREGA, SHGs, etc. -65 % of clients, but, only 51.7% of non-clients participate -opened various rural networks to generate product awareness; better product understanding Demographic characteristics: - Demo. Char.Correlation with take-up Joint familyPositive SC/ST categoryNegative Household sizeNegative

12 SMS Based Forecasts: Client Experience Salience of weather forecasts: -20% clients: weather forecast most important factor for good yield -why weather forecasts? -top 3 desired qualities: regularity and availability in regional languages and trust on the source Experience with SMS forecasts: -only 6.16% clients were able to recognize weather related SMS -3.8% felt forecasts were regular and trustworthy -3.3% understood advice given -5.6% made recommendations

13 Other sources of weather information Most used means for weather forecast Respondents (%) TV forecasts43.36 Discussions with neighbours41 Radio forecasts7.82 Traditional knowledge6.16 Other means0.95 SMS updates0.71 Total100

14 Impact of WII on clients Details on claims paid: -41 farmers had claimed Impact on clients who claimed: -87.8% felt that the settlement was made on time; positive impact Crop Avg. premium paid (Rs.) Expected compensation received (Rs.) Average compensation received (Rs.) Percentage with loss > 30 % Paddy Aman Paddy Boro How was claim used (top three responses in order) Buy agricultural inputs Buy agricultural equipment Repay loan How has claim helped (top three responses in order) Better off due to better production decisions in next season Better off due to presence of financial assistance in event of loss Better off due to possibility of riskier decisions

15 Contd. Impact on all clients: -84% of clients feel insurance is a good mechanism to manage crop failure -94% feel insurance is useful -11.4% opened bank A/C after insurance purchase -95% trust WRMS as an insurance intermediary; positive impact Impact of insurance on clientsPercentage of clients Insurance helped in changing sowing decisions23.7 Change in number of crops harvested8.07 Helped in reducing farming costs28.4 Good mechanism for coping with crop failure83.9 Trusts the insurer95.3 Faced difficulty in paying premiums32.2 Weather insurance is very useful94.3

16 Contd. Renewals: -Low renewal interest -32% found it difficult to pay premiums - majority of clients simply have not understood the product Reasons for non-renewal Percentage of clients Claims did not come through80.57 Bad past experience18.96 No trust/High basis Risk0.47 Total100 Reason for claim less than loss sufferedPercentage of clients Related to work/location of weather station & basis Risk8.06 All losses are not covered0.94 Agents don't work10.5 Dont know80.5 Total100

17 Recommendations Client education and communication of product features Rural Networks used to spread product awareness Improved target group selection Reconsider mode, manner and delivery of VAS Adoption of flexible payment structure

18 Thank You!!

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