Transcript Document

Index insurance:
structure, models, and data
Daniel Osgood (IRI)
[email protected]
Material contributed by:
Miguel Carriquiry, Ashok Mishra, Nicole Peterson,
Megan McLaurin
The International Research Institute
for Climate and Society
Examples from groundnut in Malawi
Contract Structure
• Rainfall summed over 10 day periods (dekads)
• Dekadal maximum ‘cap’
• Sowing rainfall condition
– Starts contract clock
– Or triggers ‘failed sowing’ payout
• Season split into phases
• Payouts each phase
– From capped dekadal rainfall total over phase
Phase sum payout function
Payout =
(1 – (Rainfall Sum – Exit) / (Upper trigger – Exit)) Max Payout
Phase Payout function 2006
300
275
250
Mvula Rainfall (mm)
225
200
175
150
125
100
75
50
25
0
0
500
1000
1500
2000
2500
3000
Kwacha
3500
4000
4500
5000
Insurance Contract developed with Farmers
Nicole Peterson, CRED
Contract parameters
• Sowing
– Sowing window beginning, end
– Sowing trigger
– Failed sowing payout
• Phases
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Number of phases
Beginning, end of each phase
Upper trigger, exit
Maximum payout per phase
• Maximum total payout
New obligations with index products
• Traditional insurance--Triggered on loss
– Pricing and financing on losses
– If payments not closely linked to losses
• Provider and client both face consequences
– Adjuster is responsible for agreement
– Insurance providers experienced assessing
losses
• Index insurance--Triggered on index
– Insurer pricing and financing built on index
– If there is an error linking payments to losses
• Only client faces consequences
– Contract must emulate adjustor
– Much more client interaction
Crops and Climate
• Crop models
– Summarize the biological drought vulnerability
of crops during a season
• Well selected crop
– Adapted for little vulnerability during the dry
spells in local climate
• Drought stress:
– Combination of biology and local climate
Insurance contracts must address this balance
Financial features of insurance
•
Deductible, payout frequency:
– Insurance only protects against the largest losses
– Insurance pays out rarely
•
Insurance must target losses that are important in
client’s risk management
– Client may prefer protection against 100 year loss, or
5 year loss
– Client may prefer protection against late season
losses because sowing problems might be better
addressed through practice changes
•
Price constraints
– Insurance must be affordable
– Risk coverage must be most cost effective option
These features must be addressed in design
Water Stress Information
• Multiple information sources
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WRSI
Process based crop models (eg DSSAT)
Historical regional yield
Farmer and expert feedback
Field trials
Each has strengths, limitations for design
WRSI
•
Powerful tool for ‘water stress accounting’
– Well known
– Assumptions intuitive
– Results are accounting of
• Rainfall
• With storage, loss assumptions
•
Not best for direct yield simulation
– Its developers at FAO use related statistical
techniques instead of model outputs for yields
•
In contract design useful
– Weigh relative water stresses due to crop
genetics and climate
– Platform for communication of crop features in design
– Starting point for contract parameters
– Statistically link local climate to crop vulnerabilities
WRSI Issues
•
Key parameter assumptions
– Timing of growth stages is assumed
– Relative vulnerability over season is assumed
•
Limited capabilities—`Simple but honest’
– Often inaccurate for small losses
– Not accurate quantification of
• Risk faced by individual farmer
• Yield losses
– Excess water impacts not modeled
– Crop failure is not modeled
Targets limited coverage to most important risk
Must verify using additional sources of info
Stress models
What is ‘truth’?
WRSI, DSSAT, Historical Yields
DSSAT and WRSI Simulated Yields and Historical Yields
for Chitedze Groundnut Crop
DSSAT Crop Yield
Historical yield
WRSI
1.00
900
700
0.80
500
300
Year
Correlations
Hist. Yields
DSSAT
WRSI
0.35
0.30
0.52
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
0.60
1962
100
WRSI crop yield (at 1 scale)
Crop Yield (kg/ha)
1100
Insurance targets covariate risk
Correlations with average yield:
CHILAZA
DEMELA
0.78
KAMBANIZITHE
0.92
MING'ONGO
0.78
MLOMBA
0.69
M'NGWANGWA
-0.52
MPINGU
0.69
EPA Historical Groundnut Yields
CHILAZA
Yield (kg/ha)
1600
DEMELA
1400
KAMBANIZITHE
1200
MING'ONGO
MLOMBA
1000
M'NGWANGWA
800
MPINGU
600
NTHONDO
SINYALA
400
UKWE
200
02
01
20
00
20
99
20
19
98
19
97
96
19
19
95
94
19
19
93
92
19
19
19
91
0
Year
Note: ~2-3 worst years most important for insurance
0.74
NTHONDO
0.89
SINYALA
0.81
UKWE
0.89
Questions for farmers and experts
•
What are the best years and the worst drought years that
you can remember?
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In which years did you have yield problems because of
drought, and for each year, what was the reason for the
problem
(eg dry sowing/weak start of rains or drought during the filling phase)?
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When do you typically plant?
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When is the earliest that you have planted?
When is the latest that you have planted?
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What do you do if rains are insufficient for planting?
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For what growth phases is rainfall most important?
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In what months?
Do the historical payouts from this contract
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Match the years you had reduced yields from drought?
Connect to the growth stage that your crops were in when
they were impacted?
Use of Water Stress Information Sources
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WRSI
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Process based crop models (eg DSSAT)
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Not only water stress
Often low quality
Short time series
Different varieties, practices
Use to see if important historical losses are covered
Field trials
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Must be carefully calibrated
Data intensive
Representative of very specific situation
Good for identifying and understanding for losses missed by WRSI
Historical regional yield
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Somewhat insensitive, direct product of assumptions
Good benchmark
Use as an accounting system for relative water stress, not a direct
simulation of yields
Artificial production situation, very limited availability
Detailed and reliable specifics of crop/climate interaction
Farmer and expert feedback
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Qualitative, strategic
Use to tune and verify WRSI and model timing, gauge how well
coverage addresses important years for correct reasons
But remember it may be strategic, unreliable
Contract design?
• Different data sources--different information
• Because of moral hazard in traditional
insurance:
– Only naïve players show all of their cards
– We can only approximate client
• risk preferences, productivity, self-insurance,
production details, microclimate, practices,
consumption needs, hedging strategies, other
sources of income, etc…
– Design is negotiation process
• Iterative statistical system for design
• Strategic use of information