1. Inderjit Claire October 2007
Index Based Insurance – an innovative approach towards agricultural risk financing mechanism
Inderjit Claire
October 2007

2. Background – a general perspective
High exposure of low income countries to weather risks (drought, floods…), pests and diseases
Lack of insurance and other risk management tools
Need of innovative approaches to deal with the nature of agricultural risks
Rainfall and economic performance in Andhra Pradesh

3. Background – Traditional Vs Index Based Insurance
Multi-peril Crop Insurance
High Administrative Costs
Moral Hazard
Adverse Selection
Index-Based Weather Insurance
Rainfall is a proxy for damage
Objective triggers and structured rules for payouts
Improved correlation between need and provision
More on Index Based. . .
Financial protection against adverse weather conditions
Contracts can be structured as insurance or derivatives
Based on the performance of a specified weather index during the risk period
Payouts are made if the index crosses a specified trigger level at the end of the contract period
Protect against yield volatility

4. Goals The goal is to meet the demands of various stakeholders
Farmers
Insurers
Delivery channels
Marketing agencies/organizations etc
Steps involved in the design and validation of the weather insurance pilots
Demand assessment
Ensuring initiatives were in response to perceived and expressed needs of farmers and their interest groups
Identification of key insured parties : compulsory or voluntary
Determination of key perils
The most important factor in insurance design
Decision on crops to be covered
Loss assessment procedures
Rating
Identify possible complementary role of Government

5. Weather Insurance Product Development – a ladder

6. Desk Research Stage 1 - Desk Research Literature review
-draw lessons from past experiences
Data availability
Crop yield data
CCE data/insurance units level
Mandal/district level
Selection of area
weather data
Daily
weekly
Selection of crops
- Identify preliminary risks
Evaluation of weather and crop yield data
Availability of weather data
Temporal
Spatial,
Limitations
Availability of crop yield data
Spatial
Collection of relevant research
IMD Crop calendar
Refer ICAR publications on Field and Plantation crops
Meeting with agrometeorologists, agronomists
To identify essential crop pheonphase risks
Critical crop growth stages and their dependencies to rainfall affecting yield
Review of insurance products being offered, if any!
Local climatology

7. Data Analysis Stage 2 - Data analysis Sanity checks
Data consistency
Data quality
Data limitation
1. Weather Data cleaning and enhancement
- Replacement of missing and erroneous values
through spatial and temporal interpolation/correlations
2. Detect data discontinuities
Through one or multiple stations values
4. Evaluation of crop yield data
- For mandal/ district level validation as proxy of weather
3. Weather simulation (WXGEN)
To generate 100 yrs of data using Markov chain model

8. Field Research Stage 3 - Field Research Demand assessment
Examine the risk structure of specific key crops
2. Determination of key perils
a key factor in insurance design
3. Decision on crops to be covered
another key factor
4. Identify crop growing season
Key crop growth stages and duration
5. Significance of specific weather parameters
Analysis on index able weather perils
6. Critical/ strike for weather parameters
Definition of strike and exits for the payouts
Definition of daily rainfall floor
Periodic rainfall caps
Daily level excess rainfall limits etc

9. Contract Design Stage 4 – Preliminary contract design
Identification of insurable risks (Key Perils)
Production risks due to adverse weather conditions
Drought/ Deficient rainfall
Sowing cover e.g. failed sowing/ germination failure
Phenophase – wise cover e.g. Vegetative Growth, Flowering, Grain Filling etc.
Excess rainfall
Frost (temperature based index)
High Winds
Satellite NDVI based cover

10. Weather Contract Design
Overarching Objective
To design contracts that cover
both magnitude and frequency of deviations of key weather parameters from requisite levels.
The challenge was to do it in an integrated manner (under a single cover) while ensuring robustness of the contracts

11. Weather Contract Design (Choice of Key Product Components)
Choice of caps and floors
Choice of triggers/ limits
Choice of indemnity payment levels
Choice of period & stages of cover
Interchangeability between indices and actual weather parameter values
Choice of sum assured under various covers
Overall flexibility in setting product parameters

12. Coverage of Weather Risks – Cotton, Mahabubnagar District
15-55 days
50 days
40 days
Ploughing
Harvest
Sowing & Germination
Vegetative Growth & Flowering
Boll Formation & Boll Development
Deficient Rainfall for Vegetative Growth & Flowering
Deficient Rainfall for Boll Formation & Boll Development
Perils
Inadequate Rainfall for Sowing
Covers for Crop Stages after Germination
Stages

13. Bundled Product Payouts - Historical Data

14. Risk assessment
Risk profile of the areas – importance of weather risk in this profile
Availability of yield data and agronomic information
Issues related to basis risk – topographic make-up, presence of microclimates
Weather data and infrastructure – presence of weather stations, satellite information, historical data
Time period – key seasonal milestones

15. Data Availability Rainfall data for Anantapur and Mahabubnagar Blocks
Field visit - 4 mandals in Anantapur and 6 Blocks in Mahabubnagar
IMD weather data
Anantapur
Rainfall data available for years (upto 2003)
Mahabubnagar
Rainfall data available for years (upto 2003)
Crop Yield Data
Mandal level
District level
Primary/Secondary information collected
Field survey
Agro meteorological/ agronomic information
Literature review – e.g., ICAR publications, iKisan etc

16. Data Availability
Crop Calendar

17. Probabilistic Drought Assessment – a framework
Production losses
Stochastic normal & drought events
Historical weather
Hazard Module
Simulated Weather Generator
Vulnerability Module
Crop Yield Model
Planting Area Model
Crops
Soil
Mgmt.
Customized from rapid onset disaster modeling framework
Probabilistic drought risk assessment model
Hazard module
Vulnerability module
Economic module

18. Hazard Module – Historical Weather
Mandal level Rainfall data used
Is – 2003 data enough (18-25 years) for non-parametric analysis?
Simulations produce more tail (extreme) events

19. Historical Weather – analyzing hazard
Data cleansing
- Sanity checks
Spatial and temporal consistency
No de-trending
Rainfall is lowest in Anantapur District
Coefficient of Variation is highest
Anantapur
Mahabubnagar
Rainfall risk is very high in these two districts

20. Hazard module – validation of stochastic weather events
The exceedance probability curves for both annual rainfall in historical and simulated data show the same trend (zero exceedance probability is equal to 800 in both the cases and rainfall with exceedance probability as 1 is just below 200).
Therefore the corresponding yield and rainfall curves from historical and simulated data are in line with each other.

21. Vulnerability Model - Crop model
EPIC simulated yield
Generated at Mandal level
Mandal level rainfall data used
Management inputs taken from ANGRAU
Field based inputs used
Reported yield
Available at both Mandal and district level
Results validated
drought years
Agro-met model

22. Vulnerability Mapping
Crop Yield Simulations
- For each model
Using historical data and then
Simulated weather events
Vulnerability Mandal level
Across six districts of AP
Covers both high and low vulnerable areas
State of Andhra Pradesh

23. Vulnerability Model - validations
The yield deviations of the simulated data is in the same range as the historical yield data

24. Losses Exceedence Probability loss Average Annual loss
Probable Maximum loss

25. Key Issues Technical Issues Administrative Issues Basis risk
Introduction of Mandal-level weather stations can help to mitigate intra-district heterogeneities
Farm level issue may prolong until density of weather stations improves substantially
Daily level measurement of rainfall and its dissemination to end-clients
Regular and timely communication of weather data to facilitate better tracking of indemnities
Asymmetries in geographical demarcation of insurance units needs consideration
Interpolation of weather data between existing and proposed weather stations to offset asymmetries due to administrative demarcation
Traditional MPCI will not get covered
Fire, hail storm etc
Pricing at every Mandal will NOT carry the same degree of confidence
Shall depend on quality and availability of weather data applied
Will require supporting field data and local knowledge
Allow premium adjustment based on experiences and underlying risk
Administrative Issues
Geographical and administrative equivalent of Mandal does not exist in any other pilot state except A.P.
Banks covered during the field research have expressed difficulties in two insurance schemes

26. Satellite based insurance contract design
Early Rice
Late Rice

27. Basis for approaching the new Classification – Spectral signatures
FCC
Overview
Classified
14 May 2006
12 June 2006
15 July 2006
18 August 2006

28. Ex: Wheat NDVI Analysis
Crop mapping
Supervised Classification technique
Area Estimation
Accuracy Assessment
Graph showing reported vs mapped acreage figures for wheat crop in the district. The difference is of %.
NDVI analysis
NDVI value computation using available values

29. Ex: Indemnity Payout Structure for NDVI Based Cover
NDVI based insurance product for wheat in Farrukhabad tehsil of Farrukhabad district
Payout structure is based on the slabs for a standard sum assured of Rs 100
Payout Structure
Crops having satellite derived rescaled mean NDVI values greater than 181 will receive no compensation
Between 181 and 180.5, farmers will receive 2 rupees for 0.5 value of deficit
Between and 179, farmers will receive 4 rupees for 0.5 value of deficit
Between 179 and 175, farmers will receive 5 rupees per value of deficit
Between 175 and 173, farmers will receive 10 rupees per value of deficit

30. Conclusions At the household level: For governments:
Gives farmers greater flexibility in investment decisions
Banks have greater interest in lending
Farmers see potential in investing in their farms
For governments:
Provides government contingent financing
Allows the cost of drought risk to be smoothed over time
Provides some predictability to drought financing and buys time for other emergency responses to take affect
Provides government a level of autonomy