Index Insurance and Climate Risk
Management In Malawi:
Theory and Practice (mostly practice)
Presented by Daniel Osgood (IRI) at the 2007 CPASW, Seattle
deo@iri.columbia.edu
Material contributed by: Miguel Carriquiry, Ashok Mishra, Nicole Peterson….
Support: World Bank CRMG, IRI, CU-CRED
Index insurance
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Insurance is an important link to allow use of climate information in
decisionmaking
Private Information problems with traditional crop insurance
– Moral hazard (incentives to let crops die)
– Adverse selection (farmers with secret weaknesses more likely to join)
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The index innovation
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Closely related to weather derivatives
Insure weather index (such as seasonal rainfall), not crop
Only partial protection (basis risk), should not oversell
Cheap, easy to implement, good incentives
Design complex: only a naive partner would reveal all their cards
– All partners must play active role in a cooperative design process
•
Price: Money in = average(Money out) + cost of holding risk
– EG: Ave(Payout) + 0.065 * 0.06 (99th % payout – Ave(Payout))
– This price must < value to client for market to exist
– Only clients really know personal value (their info may be used against them)
Index issues, risk layering, basis risk
• Not only to farmers for crop loss using rainfall
• Broad applications, in principal
– Temp, rainfall, degree days, wind, SST, reservoir level, model output,
remote sensing
– Not limited to target group
• Not comprehensive--target cost effective parts of risk
• Index protects some people from some risks
– Risk management needs other solutions for other risks, players
– Build risk layering system
• Eg: farmer, group, cooperative, micro-lender, government, re-insurer
• Only partial coverage--must not oversell
• But an important link in climate risk management
• This application is development oriented NOT famine relief
Some projects
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India
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BASIX, hundreds of thousands of farmer transactions completed in only about 4 years
Ethiopia
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Drought famine relief (client: national government, first transacted 2006)
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Crop loss micro-insurance (client: <100 farmers, piloted 2006)
Malawi
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Drought relief (insurance/price options, client: national government)
Farm level crop loss, bundled contracts
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Example of early action system/trigger policies
initially ~900 farmers, 2005
We designed 2006 contracts in operation now, several thousand contracts
Working on projects for 2007 in Kenya, Tanzania, MVP, Central America
Experimental 2007 pilot precip/NDVI for selected counties’ rangelands in US
(subsidized)
–
http://www.rma.usda.gov/policies/pasturerangeforage/ , NOAA CPC, EROS data
World Bank CRMG, Re-insurance companies, WFP highly involved
Micro loans
• Insurance, credit, savings complement each other
– Insurance for uncertainty (useless without uncertainty)
– Uncertainty hurts credit markets
• Work better together
– Insurance can make loans to riskier clients possible
– “Traditional” microfinance strategy of relying on group liability is
vulnerable to widespread drought
• Package of microfinance insurance, credit, savings
• Bundle contracts
– Lender packages insurance in loan, so farmer can use insurance if dry
– Seed provider packages insurance in seed sale, so farmer gets payment if
seed fails due to drought
Groundnuts from farmer in Malawi program
Maize of farmer in groundnut program (not yet program Maize)
Malawi Groundnut contract bundle
• Farmer gets loan (~4500 Malawi Kwacha or ~$35) that covers:
– 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
Some Stakeholders
Graphical representation of Insurance Contract developed with Farmers
Nicole Peterson, CRED
Contract design?
• Different simulation strategies provide different results
• Historical yield data scarce and unreliable, for different
varieties, different inputs
• Private information inherent to design problem:
– Only naïve players show all of their cards
– We do not know risk preferences, productivity, self-insurance,
production details, consumption needs, hedging strategies, other
sources of income, if having new child year…
• So we cannot run ‘ideal’ optimization
• But we must design contracts
Cooperative Design Strategy
Chitedze Groundnut Simulations
Ashok Mishra
Cooperative design steps
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Stakeholders choose maximum insurance price
Use qualitative knowledge of vulnerability to set initial guess for optimizer
Computer optimization (really just “tuning”):
– WRSI based simulation of losses (using historical precip)
– Optimize upper triggers to:
• Minimize variance of (losses - insurance payments)
• Subject to specified maximum insurance price (can get great correlation at high price)
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Compare contracts performance against array of simulations and historical
data, looking for contract vulnerabilities.
Would most payouts have occurred in most of the worst years of history, for
right reasons?
Communicate results with stakeholders, iterate, and manually adjust contracts
to address requests, reporting price, payout, and correlation impacts of
changes.
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
Chitedze Groundnut example analysis
• Upper triggers: 35 35 220
• Lower Triggers: 30 30 20
• Price rate (target, actual): 0.07, 0.083
Pearson’s
Correlation
Years
Payouts
% Payyears in % drop in var
worst 1/4
(arbitrary)
WRSI
0.54
45
9
78
21
Historical
Yields (all
Groundnut)
0.66
12
4
50
40
Crop
simulation
0.30
43
8
50
9
Ranking of losses and payouts
[,1] [,2] [,3] [,4] [,5]
[1,] 1995 7641.140 1 3132.5 1.0
[2,] 1973 6542.680 1 312.5 7.0
[3,] 1966 6324.398 0 0.0 27.5
[4,] 1996 6315.617 1 2177.5 2.0
[5,] 1990 5903.817 0 0.0 27.5
[6,] 1984 5660.633 1 1467.5 3.0
[7,] 2005 5598.026 1 275.0 8.0
[8,] 1970 4929.469 1 1232.5 4.0
[9,] 1992 4904.982 0 0.0 27.5
[10,] 1997 4459.438 1 555.0 6.0
[11,] 1968 4400.516 0 0.0 27.5
[12,] 1969 4296.916 1 72.5 9.0
[13,] 1980 4235.219 0 0.0 27.5
[14,] 1994 4136.128 0 0.0 27.5
[15,] 2004 3921.972 0 0.0 27.5
[16,] 1979 3513.749 0 0.0 27.5
[17,] 2000 3399.898 0 0.0 27.5
[18,] 1983 3399.299 0 0.0 27.5
[19,] 2001 3367.294 0 0.0 27.5
[20,] 2006 3347.076 0 0.0 27.5
[21,] 2002 3218.283 1 1100.0 5.0
[22,] 1967 3070.731 0 0.0 27.5
[23,] 1962 0.000 0 0.0 27.5
[24,] 1963 0.000 0 0.0 27.5
[25,] 1964 0.000 0 0.0 27.5
[26,] 1965 0.000 0 0.0 27.5
[27,] 1971 0.000 0 0.0 27.5
[28,] 1972 0.000 0 0.0 27.5
[29,] 1974 0.000 0 0.0 27.5
[30,] 1975 0.000 0 0.0 27.5
[31,] 1976 0.000 0 0.0 27.5
[32,] 1977 0.000 0 0.0 27.5
[33,] 1978 0.000 0 0.0 27.5
[34,] 1981 0.000 0 0.0 27.5
[35,] 1982 0.000 0 0.0 27.5
[36,] 1985 0.000 0 0.0 27.5
[37,] 1986 0.000 0 0.0 27.5
[38,] 1987 0.000 0 0.0 27.5
[39,] 1988 0.000 0 0.0 27.5
[40,] 1989 0.000 0 0.0 27.5
[41,] 1991 0.000 0 0.0 27.5
[42,] 1993 0.000 0 0.0 27.5
[43,] 1998 0.000 0 0.0 27.5
[44,] 1999 0.000 0 0.0 27.5
[45,] 2003 0.000 0 0.0 27.5
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?
– Look for 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 Malawi Project Challenges
• Basis risk
– Seed quality
– Aflotoxin
– Rainfall spatial variability
• Seed and Yield prices
• Repayment
• Scaling challenges
– Station availability, history
– How do you responsibly include thousands of new farmers?
• Financial recordkeeping quality
• Compatibility with government subsidy programs
Seasonal forecasts, long term trends, and
climate change
• Seasonal forecast and index insurance interact
– Difficult to take chance using forecast if livelihood at stake
• Well designed insurance can take risk out of forecast
• Maps probabilistic forecast to deterministic outcome
• Farmers (banks) can take intensification chances for higher
productivity
– Insurance can communicate forecasts and risk costs as price signal
– Seasonal forecast makes badly designed insurance insolvent
• Well designed insurance robust to forecast
• “Low skill” forecasts/indices can have high skill for
insurance specific decisions
• Can climate science “guarantee” no skill?
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
Miguel Carriquiry
1986
1991
1996
2001
Exploratory Analysis: Standardized Seasonal Rainfall Anomaly
Predictions (October) vs Payouts from Groundnut Insurance
1800
1600
Payout (Kwacha)
1400
1200
1000
800
600
400
200
Predicted anomaly (standardized)
Miguel Carriquiry
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
-1.2
0
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.
Climatology 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
– By strategic pooling of contracts from the north and south, total
risk can be reduced, reducing costs of insurance
– Research underway (Megan McLaurin . . .)
– Pool Kenya with Malawi?
– Negative correlations, forecast critical in Central America
• We are building integrated data/contract design web tools
Example of ENSO based pricing
El Niño
La Niña
Neutral
All
Insurance Rate
0.1568
0.0179
0.1114
0.1198
Insurance Price (MKW)
702.90
702.90
702.90
702.90
3515.25
30915.85
4949.38
4602.90
966.69
8501.86
1361.08
1265.80
Input Budget (MKW)
2812.35
30212.95
4246.48
3900
Maximum Liability (MKW)
4481.94
39417.71
6310.46
5868.69
0.72
7.75
1.09
1
Loan (MKW)
Interest (MKW)
Input Budget Weight
Preliminary results—do not cite
Example gross revenue calculations Insurance/Loan
package--ENSO based pricing
PRELIMINARY RESULTS—do not cite
Non-Hybrid ENSO shifted Land Allocation
Based on Historical District Yields
150000
Scaled Package
Based on simulated yield
100000
50000
Gross Revenue (MKW)
8 e+05
4 e+05
Enso Based
Standard
0 e+00
Gross Revenue (MKW)
Enso Based
Standard
1970
1980
Year
1990
2000
1985
1990
1995
Year
2000
Gross Revenue Simulations
Simulation based
Standard (MKW)
Enso based (MKW)
Enso/Standard
Historical Yields
Mean
Min
Max
Var
89034.88
-5868.69
145951.31
1902719400
246798.42
-6310.46
1113942.41
106489037713
2.6663
0.9923
7.1810
55.97
Mean
Min
Max
Var
Standard (MKW)
12977.79
6682.94
19932.01
14850032
Enso based (MKW)
37129.32
6565.28
152822.54
2584196785
2.8610
0.9824
7.6672
174.0196
Enso/Standard
I’m Mrs Timange Mateyo Kalitsiro from the Chiponde GAC,
Chiwamba Association and one of the Volunteers of Gender and
HIV.
I would like to talk about the Chalimbana 2000 Groundnut
variety. This type of ground nut is high yielding. But, we had a
seed problem. Not all the seed that we planted germinated. This
is what caused us not to achieve the normal high yield expected
from Chalimbana 2000.
We hope that if we can be given a good seed this coming season,
we will be able to harvest high yields. Chalimbana 2000 is
different from the ordinary Chalimbana . If we can be given
good seed and take a good care of our gardens we can benefit a
lot from this crop.
We wanted to know more about insurance. What is the meaning
of insurance? We did not know much about this insurance, but
now through the explanation that has been given to us by the
agricultural advisors and visitors who came here at Chiwamba,
now we have understood how this insurance works. We will be
able to explain to our friends how the insurance works and how
we can benefit from it in the time of disaster.
Our request is that the insurance should not only cover rain
disaster but also other agricultural problems. We the farmers,
we are ready to work with you for the success of the project, and
the insurance coverage will help us when we have a disaster.
We farmers from Chiwamba, we promise to work hard if we
are given the farm inputs in good time and plant with the first
planting rain, we will have enough time to take care of the crops
at the end we will have enough yields.
Thank you (Zikomo)