Livestock Insurance in Kenya: A Market-Based Innovation for Climate Risk Management

Apurba Shee, ILRI

IWMI-CCAFS Workshop on Institutions and Policies for Scaling Out Climate Smart Agriculture, Colombo, Sri Lanka – Dec 2 2013

Outline

Introduction and motivations

Potential of index insurance – insurance for the poor

IBLI coverage

Designing the index

IBLI contract pricing and features

Implementation

Opportunities and challenges

Conclusions

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Livestock- the pastoralist livelihood

• Livestock are a significant global asset; account for 20%-40%

of agricultural GDP (Steinfeld et al., 2006; Herrero et al., 2013)

• Arid and semi-arid lands (2/3 of Africa) – 20 million

pastoralists’ main livelihood is livestock grazing

• Livestock is the key productive asset

• Low and erratic rainfall and poor soils prohibit crop production

• Pastoralist systems adapted to variable climate, but very

vulnerable to severe drought events. In the past 100 years,

northern Kenya recorded 28 major droughts, 4 occurred in

last 10 years

• Droughts forage shortage livestock mortality poverty

trap and dependence on food aid

• Uninsured climate risk is main driver of persistent poverty

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Impact of drought risks on livelihoods

Cause of Livestock Mortality Component Shares of Income

• Drought is by far the leading cause

of livestock mortality

• Disease and Predation likely to be

directly related to drought

• Sale of livestock and livestock

products constitute 40% of household

income

• External support (food and cash)

make up nearly 25% of household

income

Data source: Project baseline 2009 (924 Marsabit Households) 4

Poverty trap and risk from climate change

5

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Exp

ecte

d h

erd

siz

e 1

0 y

ears

ah

ead

Initial herd size

Prob. = 0.03

Prob. = 0.12

Prob. = 0.06

Source: Lybbert, Barrett, Desta, and Coppock, 2004 Source: Barrett and Santos, 2011

Existence of poverty trap and vulnerability due to increased drought risk.

Variation of simulated herd dynamics with drought risk (rainfall<250mm/yr.).

There could even be no higher level equilibrium with increased drought risk.

Costs of uninsured risk

• 2008 -2011: 4 consecutive years

drought:

– Overall estimated effect is $ 12.1

billion including asset loss and

losses in the flows of the economy

across all sectors

• This magnitude of losses can not be

financed by the govt. and donors. A

market based solution is needed

12.5%

72.2%

0.4%

0.7%

0.5%

0.7%

0.4%

3.3% 9.1%

0.1%

Agriculture

Livestock

Fisheries

Agro Industry

Health

Nutrition

Education

Energy

Total Value Drought Losses US$ 12.2 billion

Source: Kenya Post-Disaster Needs Assessment (PDNA) 2008-2011 Drought, Govt. of Kenya

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Index insurance for the poor?: opportunities and challenges

Make loss compensation based on a ‘well-defined index’ (highly correlated

with insurable loss and not manipulable by insure parties)

Advantages: avoids market failures of traditional insurance:

• No transactions costs of measuring individual losses

• Preserves effort incentives (no moral hazard) as no one can influence index

• Adverse selection does not matter as payouts do not depend on the riskiness

of those who buy the insurance

• Suitable for systemic (covariate) climate shock

• Spatial and temporal risk pooling

• Available on near real-time basis: faster response than conventional

humanitarian

Disadvantages: Basis risk

• Imperfect match of individual losses and insurance payout

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IBLI Coverage

IBLI was commercially

launched in the Marsabit district

in Kenya in January 2010

Launched in Borana Zone in

southern Ethiopia in July 2012

Have developed contracts for

all arid counties of Kenya (108

divisions)

Contract provision extended to

Isiolo and Wajir in August 2013

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Designing the index

Find a reliable, objectively verifiable signal, that explains most of the variation in household’s seasonal livestock mortality

We use functions of NDVI, a remotely sensed proxy for forage availability. An indicator of the level of photosynthetic activity in the vegetation.

Model a relationship between the risk to be insured (area-average livestock mortality) and the driving signal (NDVI)

DATA

• Livestock Mortality

• Remotely-Sensed NDVI

Response Function

Index

• Predicted Livestock Mortality

9

Satellite imagery solves the data challenges

Normalized difference vegetation index (NDVI)

1-10 May 2010 good vegetation 1-10 May 2011 bad vegetation

10

Satellite imagery, cont’d.

11

Marsabit

Isiolo

Wajir

1-10 May 2010 1-10 May 2011

Temporal/seasonal coverage

Source: Chantarat et.al 2012 JRI 12

Spatially explicit contract- scalable mortality index

Drought being a covariate climate shock creates spatial dependence. Also in

response to climate shocks livestock migrate from one place to another.

Incorporating spatial interactions by using spatial lag model allow unbiased

and more precise estimation

Spatial method allow for maximal information extraction for missing data cases

and provides scalable index construction

Conditional premium pricing: Conditioning the premium rates on actual

present condition eliminates inter-temporal opportunistic behavior for

purchasing insurance.

Risk coverage and pricing: Households are provided flexibility of

choosing a strike/ deductible of either 10% or 15%

Contract features

Predicted mortality index readings

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• Key Findings

– Appropriately triggered payments indicative of product precision and building client trust

– ITC-based sales and information delivery platform reducing transactions costs

– Preliminary analysis showing potential welfare impacts:

• Key Challenges

– Catalyzing Informed Demand

• Extension challenge

• Uptake challenge

Opportunities and challenges

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Conclusions

• Uninsured climate (drought) risk is a major cause of food insecurity and

poverty traps

• IBLI appears effective financial innovation for protecting pastoralists

against drought related livestock mortality and could help households

avoid poverty traps

• Considering the challenges it is important to encourage public private

partnership and policy to make index insurance a sustainable

development tool

• Index insurance can be embedded with structural credit product to

reduce farmers default probability to weather risk and hence it not only

can protect downside risk for the farmers but it also provides credit

access for agricultural development.

• Meso level (county governments) – allows for timely provision of

resources for drought emergency response

a promising option for addressing poverty traps that arise from catastrophic

drought risk

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Thank you for your attention and comments