Transcript Document

Index insurance:
contract design
Daniel Osgood (IRI)
[email protected]
Material contributed by:
Miguel Carriquiry, Ashok Mishra, Nicole Peterson,
Megan McLaurin
The International Research Institute
for Climate and Society
Cooperative Design
Cooperative design steps
•
Stakeholders determine
–
–
•
Set initial guess for optimizer
–
•
Premium constraint
Payout frequency target
Pursue strategies that target alternate risks (eg sowing vs flowering)
Computer optimization (“tuning”):
–
–
Using performance measures, WRSI based loss
Optimize upper triggers to:
•
•
Minimize variance of (losses - insurance payments)
Subject to specified maximum insurance price
•
Compare contracts performance against information sources
looking for contract strengths and vulnerabilities
•
Adjust parameters to round numbers so that client does not get
misimpression that design information is higher accuracy than it is
•
Communicate results with stakeholders
–
–
Correct years for correct reasons
Is coverage what clients demand?
•
Adapt contracts and models
•
Typically trade-offs: Must sacrifice something for gain
•
Iterate
Starting point—initial parameters
•
Sowing parameters
– WRSI model assumptions, farm, expert input
• Sowing window beginning, end
• Sowing trigger
– Cost information
• Failed sowing payout
•
Phase parameters
– Number of phases
• Balance crop and climate seasonal phases
– Beginning, end of each phase
• WRSI assumptions, farmer, expert input
– Upper trigger
• Deductible based on WRSI
• Investigate targeting alternate drought risks
– Exit
• Financial constraint for payout condition
– Maximum payout per phase
• Cost, loss information
•
Maximum total payout
– Cost, loss information, financial constraints
Contract Performance Evaluation
•
For a given premium
How well does contract reduce risk?
– Risk = Variance of hypothetical farmer with
•
•
–
When comparing contracts with same premium, better
performing contract has lower:
•
–
Yield loss driven reductions in revenues
Insurance payouts
Var(Losses – Insurance payouts)
Computer optimizer
•
•
Minimizes variance subject to price constraint
Adjusts upper triggers:
Balances deductibles between phases
to provide most risk reduction for price
•
Other important metrics
–
Correlation
•
•
–
Useful measure
Not for design algorithms--correlation is not identical to risk faced
Client’s perspective: Are insurance payments in critical years?
•
•
•
Useful, but not enough of a criterion to identify optimum
Because of price and pay frequency constraints, typically more
tough years than payments
So design deductible to find which bad years can be covered
most cost effectively
Tuning Contract parameters
•
Upper trigger (deductible)
– Computer optimization
– Payout rate constraint
– Evaluate alternate strategies
•
Exit
– If premium low enough, can increase to increase
coverage without changing payout rate
•
Phase timing
– Adjust to target risk more effectively or to address
client demand
•
Sowing window, condition
– Adapt to reflect season timing, risks reported, price,
payout constraints
•
Note
– If farmers are farming, risks must be reasonable
– If insurance is not, revisit information on practices
Losses
• Insurance
– Not for 1% reduction from best year in history
– For worst year out of 5 or 10
• Use appropriate loss proxy
– Role: indicator to balance protection between
phases to most cost effectively reduce risk
– Absolute magnitude
• Only changes weighting between larger and
smaller losses
– Losses, not small reductions from best year
• Generate proxy
– Zero for good years
– Approximate magnitude of cash losses
• Tune magnitude to weigh optimizer to reward
higher/lower payout frequencies
Parameter iteration
Upper trigger
–
–
–
–
•
Phase Payout function 2006
Deductible
Payout frequency
Type of protection
Optimizer
Exit
– Does not impact freq.
– Increases coverage, price
– Catastrophe
300
275
250
225
Mvula Rainfall (mm)
•
200
175
150
125
100
75
50
25
0
•
Phase length, timing
– Target vulnerabilities
– If too tight may be out of sync
– Trade off: Split up/long
•
In general, contract
– Mostly determined by cost, payout
constraints
– Want most cost effective protection
0
500
1000
1500
2000
2500
3000
Kwacha
3500
4000
4500
5000
Chitedze Groundnut Loss based on Daily WRSI, seasonal KY
4000
2000
0
loss
6000
Loss
Pay
Loss-Pay+E[Pay]
1970
1980
1990
years
2000
Loss
Pay
Loss-Pay+E[Pay]
1500
1000
500
0
loss
2000
2500
3000
Chitedze Groundnut Historical Loss
1992
1994
1996
years
1998
2000
2002
Loss
Pay
Loss-Pay+E[Pay]
2000
0
loss
4000
6000
Chitedze Groundnut Simulated
1970
1980
years
1990
2000
•
•
•
Upper triggers: 35 35 220
Exits: 30 30 20
Price rate (target, actual): 0.07, 0.083
Pearson’s
Correlation
Years
Payouts
% Payyears in
worst 1/4
WRSI
0.54
45
9
78
Historical
Yields (all
Groundnut)
0.66
12
4
50
Crop
simulation
0.30
43
8
50
Ranking of losses and payouts
RANK
[1,]
[2,]
[3,]
[4,]
[5,]
[6,]
[7,]
[8,]
[9,]
[10,]
[11,]
[12,]
[13,]
[14,]
[15,]
[16,]
[17,]
[18,]
[19,]
[20,]
[21,]
[22,]
[23,]
[24,]
[25,]
YEAR
1995
1973
1966
1996
1990
1984
2005
1970
1992
1997
1968
1969
1980
1994
2004
1979
2000
1983
2001
2006
2002
1967
1962
1963
1964
LOSS
PAYOUT?
7641.140
1
6542.680
1
6324.398
0
6315.617
1
5903.817
0
5660.633
1
5598.026
1
4929.469
1
4904.982
0
4459.438
1
4400.516
0
4296.916
1
4235.219
0
4136.128
0
3921.972
0
3513.749
0
3399.898
0
3399.299
0
3367.294
0
3347.076
0
3218.283
1
3070.731
0
0.000
0
0.000
0
0.000
0
[26,]
[27,]
[28,]
[29,]
[30,]
[31,]
[32,]
[33,]
[34,]
[35,]
[36,]
[37,]
[38,]
[39,]
[40,]
[41,]
[42,]
[43,]
[44,]
[45,]
1965
1971
1972
1974
1975
1976
1977
1978
1981
1982
1985
1986
1987
1988
1989
1991
1993
1998
1999
2003
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Insurance Contract developed with Farmers
Nicole Peterson, CRED
Stakeholder input drives contracts
• Look for:
– Do stakeholders understand contracts?
– Do stakeholders show evidence of negotiating
in their own interests?
– Do stakeholders understand basis risk and what
is not covered?
– Insightful complaints
• Malawi stakeholders have been very active,
driven design
– Original CRMG project proposal was for stand
alone Maize Insurance
– Malawi stakeholders proposed groundnut
bundle
Some Stakeholders
Malawi Groundnut contract bundle
•
Farmer gets loan (~4500 Malawi Kwacha or ~$35) for:
– Groundnut seed cost (~$25, ICRSAT bred, delivered by
farm association)
– Interest (~$7), Insurance premium (~$2), Tax (~$0.50)
– Prices vary by site
•
Farmer holds insurance contract, max payout is loansize
– Insurance payouts on rainfall index formula
– Joint liability to farm “Clubs” of ~10 farmers
– Farmers in 20km radius around met station
•
At end of season
– Farmer provides yields to farm association
– Proceeds (and insurance) pay off loan
– Remainder retained by farmer
•
•
Farmers pay full financial cost of program
Only subsidy is data and contract design assistance
•
Partners: Farmers, NASFAM, OIBM MRFC, ICRSAT, Malawi
Insurance Association, the World Bank CRMG, Malawi Met
Service, IRI, CUCRED
Exploratory analysis: Hypothetical Historical Payouts of Drought Insurance
2005 Contracts for Groundnuts in Lilongwe, Malawi
1800
Payout (Kwacha)
1600
1400
1200
1000
800
600
400
200
0
1961
1966
1971
1976
1981
Year
1986
1991
1996
2001
Exploratory Analysis: Standardized Seasonal Rainfall Anomaly Predictions
(October) vs Payouts from Groundnut Insurance
1800
1600
1200
1000
800
600
400
200
Predicted anomaly (standardized)
1.45
1.15
0.82
0.69
0.59
0.44
0.33
0.25
0.22
0.16
0.03
-0.1
-0.3
-0.5
-0.7
-1
-1.1
0
-1.2
Payout (Kwacha)
1400
Visions for climate risk management
•
Malawi farmers
– Knew about Enso impacts on precipitation
– Would like to adjust practices to take advantage of
seasonal forecasts but are unable to obtain
appropriate fertilizer and seed
– We are researching and cooperatively developing
packages that provide price incentives, risk protection,
and strategic input availability so farmers can take
advantage of forecasts
– No ‘historical’ payouts for La Nina years for many
stations
– ICRSAT would like to develop seeds to compliment
these packages
– Fundamental research on insurance, production, and
forecast necessary
– When asked how they adapt to climate
variability and change farmers reported that
they signed up for the index insurance
program.
Adjusting insurance price with forecast may increase profits
100000
Enso Based
Standard
50000
Gross Revenue (MKW)
150000
Non-Hybrid ENSO shifted Land Allocation
Based on Historical District Yields
1985
1990
1995
Year
2000
Climatology correlations important
•
Northern and Southern Malawi
–
–
“opposite” Enso phase response
Location of north-south dividing line challenging to forecast
•
But climate info still very valuable for insurance
•
Potential for natural hedge
–
–
Negative correlations, forecast potentially very valuable in Central America
Stdev Pricing
326
324
322
Premium (Kwacha)
•
By strategic pooling of contracts from the north and south, total risk can be reduced,
reducing costs of insurance
Pool Kenya with Malawi?
320
318
316
314
312
310
308
306
0
10
20
30
40
50
60
Percent Lilongwe
70
80
90
100
Forecast issues
• Seasonal forecasts have information
• Future: to build system robust to forecasts
– Multi year contracts, contract sale before forecast
– Build contracts that avoid losses using forecast
• Important once pilots grow sufficiently