Index Based Insurance

1
Index Based Insurance an innovative
approachtowards 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

• Evaluation of weather and crop yield data
• Availability of weather data
• Temporal
• Spatial,
• Limitations
• Availability of crop yield data
• Temporal
• Spatial
• Limitations
• 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/correla
  tions
• 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
Harvest
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 18-25 years (upto
  2003)
• Mahabubnagar
• Rainfall data available for 16-18 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

• 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 1988 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 mapping _at_ 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
• 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
18 August 2006
12 June 2006
15 July 2006
14 May 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 15.21 .

• 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 180.5 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
• 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