Index-based Livestock Insurance (IBLI) for Northern Kenya
Pastoralists
Christopher B. BarrettOctober 7, 2009
Institute for African Development, Cornell University
Strong evidence of poverty traps in the arid and semi-arid lands (ASAL) of east Africa
Usual humanitarian response to shocks: food aid
Pay attention to the risk and dynamics that cause destitution … else beware an aid trap!
Getting Smart About Risk and Poverty Traps
Economic costs of uninsured risk, esp. w/poverty traps Sustainable insurance can:
• Prevent downward slide of vulnerable populations• Stabilize expectations & crowd-in investment and
accumulation by poor populations• Induce financial deepening by crowding-in credit
supply and demand But can insurance be sustainably offered in the ASAL? Conventional (individual) insurance unlikely to work,
especially in small scale pastoral/agro-pastoral sector:• Transactions costs• Moral hazard/adverse selection
Insurance and Development
Index Insurance: Advantages
Index insurance provides insurance based on events collectively – rather than individually – experienced. Can avoid problems that make individual insurance infeasible:
• No transactions costs of measuring individual losses• Preserves effort incentives (no moral hazard) as no
single individual can influence index.• Adverse selection does not matter as payouts do not
depend on the riskiness of those who buy the insurance• Available on near real-time basis: faster response than
conventional humanitarian aid
Index insurance can, in principle, be used to create a productive safety net needed to alter poverty dynamics
‘Big 5’ Challenges of Sustainable Index Insurance:
1. High quality data (reliable, timely, non-manipulable, long-term) to calculate premium and to determine payouts
2. Minimize uncovered basis risk through product design
3. Innovation incentives for insurance companies to design and market a new product
4. Establish informed effective demand, especially among a clientele with little experience with any insurance, much less a complex index insurance product
5. Low cost mechanism for making insurance available for numerous small and medium scale producers
Index Insurance: Challenges
Solutions to the ‘Big 5’ Challenges:
1. High quality data • Satellite data (remotely sensed vegetation: NDVI)
2. Minimize uncovered basis risk• Analysis of household panel data on herd loss
3. Innovation incentives for insurers• Researchers do product design work, develop awareness
materials, help facilitate reinsurance
4. Establish informed effective demand• Simulation games with real information & incentives
5. Low cost mechanism• Delivery through partners
Index Insurance: Solutions to the Challenges
One possible index is based on area average livestock mortality predicted by remotely-sensed (satellite) information on vegetative cover (NDVI):
Livestock Mortality Index
NASA NDVI Image Produced By: USGS-EROS Data Center. Source: Famine Early Warning System Network (FEWS-NET)
NDVI February 2009, Dekad 3 Deviation of NDVI from long-term average February 2009, Dekad 3
Laisamis Cluster
-3-2-1012345
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Karare
Logologo
Ngurunit
Korr
Laisamis Cluster, zndvi (1982-2008)
Historical droughts
NDVI Data Real-time available in 8×8 km2 resolution
27 years available since late 1981
High Quality Data
Estimate separate response functions for distinct geographic clusters due to differences in herd composition, grazing ranges, water access, etc.
Geographic Clusters
Temporal structure of IBLI contract
Product Design
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
Period of continuing observation of NDVIfor constructing LRLD mortality index
LRLD season coverage SRSD season coverage
1 year contract coverage
Sale periodFor SRSD
Predicted SRSD mortality is announced.Indemnity payment is made if triggered
Period of NDVI observationsfor constructing SRSDmortality index
Prior observation of NDVI sincelast rain for LRLD season
Sale periodFor LRLD
Sale periodFor SRSD
Predicted LRLD mortality is announced.Indemnity payment is made if triggered
Prior observation of NDVI since last rainfor SRSD season
Short Rain Short Dry Long Rain Long Dry Short Rain Short Dry
Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb
Period of continuing observation of NDVIfor constructing LRLD mortality index
LRLD season coverage SRSD season coverage
1 year contract coverage
Sale periodFor SRSD
Predicted SRSD mortality is announced.Indemnity payment is made if triggered
Period of NDVI observationsfor constructing SRSDmortality index
Prior observation of NDVI sincelast rain for LRLD season
Sale periodFor LRLD
Sale periodFor SRSD
Predicted LRLD mortality is announced.Indemnity payment is made if triggered
Prior observation of NDVI since last rainfor SRSD season
Short Rain Short Dry Long Rain Long Dry Short Rain Short Dry
Consider 1-year contract for a pastoralist in the Chalbi cluster who would like to insure 1 cattle worth KSh10,000.
During the sale period at the beginning of the coverage year, he pays an annual premium (Ksh) = % × insured value
At the end of each of the two covered season, he receives indemnity payment (KSh) = (predicted mortality rate - M*)% × insured value
How will IBLI work?
Annual premium Strike M* = 10% Strike M* = 15% Strike M* = 20% Strike M* = 25% % of insured value 9% 5% 3% 1% KSh (insured value = 10,000 KSh) 9%×10,000=900 5%×10,000=500 3%×10,000=300 1%×10,000=100
9%
Indemnity payment (KSh) Strike M* = 10% Strike M* = 15% Strike M* = 20% Strike M* = 25% If predicted mortality = 5% 0
0 0 0
If predicted mortality = 15% (15-10)% ×10,000=500
(15-15)% ×10,000=0
0 0
If predicted mortality = 30% (30-10)% ×10,000=2,000
(30-15)% ×10,000=1,500
(30-20)% ×10,000=1,000
(30-25 )% ×10,000=500
Performance of NDVI-based Mortality Index
Index predicts large-scale losses well
Performance of NDVI-based Mortality Index
Cluster Strike Correct decisionType I error Type II error
Chalbi 10% 0.75 0.25 0.0015% 0.88 0.00 0.1320% 0.75 0.00 0.2525% 0.88 0.00 0.1330% 0.88 0.00 0.13
Laisamis 10% 1.00 0.00 0.0015% 1.00 0.00 0.0020% 0.75 0.25 0.0025% 0.75 0.25 0.0030% 0.75 0.25 0.00
Performance of mortality index in predicting insurance trigger
Incorrect decision
Experimental IBLI Game
(i) Teach how IBLI works and how IBLI can affect herd dynamics(ii) Game with real monetary stakes. Pretested in 2008.
Establishing Informed, Effective Demand
Willingness to pay (WTP) experiments using contingent valuation methods
Establishing Informed, Effective Demand
Fair premium
6%
7%
8%
9%
10%
11%
Pre
miu
m (%
of i
nsur
ed h
erd)
0 10000 20000 30000 40000Insured herd (TLU)
Less than 15 TLU Between 15-30 TLUGreater than 30 TLU Aggregate
Demand for 10% Strike Contract by Herd Group
Estimated WTP for 10% strike contract(Fair premium rate = 6.8% of total insured herd value)
IBLI demand appears very price elastic.
WTP(%) % chosen herdMean 7.74 0.71Median 7.70 0.75S.D. 1.40 0.28Minimun 2.73 0.25Maximum 11.15 1.00
Establishing Informed, Effective Demand
1. Pilot plan for Marsabit District (northern Kenya) in early 2010 by Equity Bank and UAP with international reinsurance, leveraging point of sale devices used for Hunger Safety Net Program.
2. Integrated survey design to study impact and design of IBLI
• HH survey of targeted population in pilot and control locations• Discount coupons randomly allocated to eligible
subpopulations to encourage uptake and generate variation in premiums.
3. World Bank has funded replication of this work in Tanzania
The Ways Forward
IBLI is a promising option for putting risk-based poverty traps behind us
Thank you for your time, interest and comments!
For more information visit www.ilri.org/livestockinsurance
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