Index-based Livestock Insurance (IBLI) for Northern Kenya

Pastoralists

Christopher B. Barrett

October 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 3Deviation of NDVI from long-term average

February 2009, Dekad 3

Laisamis Cluster

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Karare

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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

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11%

Pre

miu

m (%

of i

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0 10000 20000 30000 40000

Insured 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