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
Why Agro-Insurance?
 Agriculture is important:
-provides employment to 2/3rd 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
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
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 inWB 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
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
Determinants of Take-Up
Educational status/
product understanding &
Risk
Occupation & alternative
means of risk management
Client
Characteristics
• Education: school enrollment
• Participation in Govt. Schemes
• Agriculture as Main Occupation
• Agriculture as Secondary Occupation
• Off Farm Activities (X11 =1 if yes)
Liquidity constraints
• Logarithm of Annual
Income
• Jewellery and livestock
Value added services
• Number of Mobile Phones
Response to agricultural
shocks/risk perception
Risk Covered
Demographic
Characteristics
• Type of house: Kutchcha
• Inputs financed through
borrowing: paddy and potato
Take-up
• Possession of water pump • Weather forecasts important
• Risk lover
and among top 3 factors
• Time to return to normal cons.
• important for good yield
• Area Cropped
• Total Input Cost
• Area owned (Bighas)
• Weather crop shock loss
• Joint Family
• Age
• Gender
• Household Size
• Category (general/SC/ST)
The Sample
 Data collection:
-1/3rd of the households (from 25 villages) from MIS information
were listed between Dec. 2011 and Jan. 2012
-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.
Value
Desc. stat.
Value
Male
84.6%
Hindu
98.1%
Avg. age
43.5 yrs
General Cat.
75.8%
Avg. hhld. size
4.8 members
Past schooling
91.7%
Married
87.9%
Main occ.- agri
83.4%
Avg. area cropped
2.7 bighas
Avg. annual inc.
Rs. 37,745
Model Specification
Logit Regression
Take-up = β0 +
26
𝑖=1 βi Xi
+ Ɛ …………. (1)
Results: Determinants of Take-Up
 Education:
School enrollment has a significant
positive impact on take-up;
consistent with expectations
60.00%
50.00%
40.00%
30.00%
Clients
20.00%
Non Clients
10.00%
0.00%
Didn't go Primary Secondary Higher
to school education Education Education
 Occupation:
-clients are less likely to have agriculture as main occupation
-counter-intuitive; might be due to liquidity constraints
Agriculture as occupation
Main occupation
Assets
Type of household
Clients
Non clients
Total
73.90%
92.90%
83.40%
Type of household
Clients
Non clients
Total
Total Value of livestock (Rs.)
527689.9
343655.7
871346
Avg. value of jewellery (Rs.)
10349.3
6876.5
8612.9
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
Payouts in each case (in Rs.) % of clients
who prefer
Option
each option
Heads
Tails
1
50
50
42%
2
40
120
53%
3
0
200
5%
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:
- Char.
Demo.
Correlation with take-up
Joint family
Positive
SC/ST category
Negative
Household size
Negative
SMS Based Forecasts: Client Experience
 Salience of weather forecasts:
-20% clients: weather forecast most important factor for good yield
0%
Helps in managing risk of
crop failure
15%
-why weather forecasts?
48%
37%
Helps in increasing yields of
traditionally grown crops
Helps in planting different
variety of crops
Other
-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
Other sources of weather information
Most used means for weather forecast
TV forecasts
Discussions with neighbours
Respondents
(%)
43.36
41
Radio forecasts
7.82
Traditional knowledge
6.16
Other means
0.95
SMS updates
0.71
Total
100
Impact of WII on clients
 Details on claims paid:
-41 farmers had claimed
Expected
Avg.
Crop
premium compensation
paid (Rs.)
received (Rs.)
Average
Percentage
compensation
with
received (Rs.)
30 %
loss >
Paddy Aman
482.1
226.7
1166.63
84.83
Paddy Boro
635.04
18.48
221.87
19.43
 Impact on clients who claimed:
-87.8% felt that the settlement was made on time; positive impact
How was claim used (top three
responses in order)
How has claim helped (top three responses in order)
Buy agricultural inputs
Better off due to better production decisions in next season
Buy agricultural equipment
Better off due to presence of financial assistance in event of loss
Repay loan
Better off due to possibility of riskier decisions
 Impact on all clients:
Contd.
Impact of insurance on clients
Percentage of clients
Insurance helped in changing sowing decisions
23.7
Change in number of crops harvested
8.07
Helped in reducing farming costs
28.4
Good mechanism for coping with crop failure
83.9
Trusts the insurer
95.3
Faced difficulty in paying premiums
32.2
Weather insurance is very useful
94.3
-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% trustWRMS as an insurance intermediary; positive impact
Contd.
 Renewals:
-Low renewal interest
-32% found it difficult to pay premiums
Reasons for non-renewal
Percentage of clients
Claims did not come through
80.57
Bad past experience
18.96
No trust/High basis Risk
0.47
Total
100
- majority of clients simply have not understood the product
Reason for claim less than loss suffered
Percentage of clients
Related to work/location of weather station & basis Risk
8.06
All losses are not covered
0.94
Agents don't work
10.5
Don’t know
80.5
Total
100
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
Thank You!!