Managing Weather and Price Risks Using Index Insurance and Risk-Contingent Credit Index-based livestock insurance (IBLI) and risk-contingent credit are presented as innovative risk management tools for pastoralists in Kenya. IBLI uses satellite-derived NDVI data correlated with livestock mortality to payout insurance based on a predicted mortality index, avoiding costs of measuring individual losses. Risk-contingent credit embeds a contingent claim in farm loans that transfers debt obligations to the lender if a specified risk such as low commodity prices is triggered. Both tools aim to protect vulnerable populations from poverty due to weather and price shocks while unlocking access to credit for agricultural development. Opportunities and challenges of basis risk

1. Managing Weather and Price Risks Using Index
Insurance and Risk-Contingent Credit
Apurba Shee, ILRI
International Conference on Revolutionising Finance for Agri-Value Chains
Nairobi, Kenya – July 15 2014

2. Outline
Index Based Livestock Insurance (IBLI)
Motivations and Potential of index insurance
IBLI coverage, pricing and implementation
Opportunities and challenges
Risk Contingent Credit (RCC)
Concept and features of risk contingent credit
Schematics, pricing and example
Concluding comments
2

3. 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
3

4. 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
4

5. 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
5

6. 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
6

7. Satellite imagery solves the data challenges
Normalized difference vegetation index (NDVI)
1-10 May 2010 good vegetation 1-10 May 2011 bad vegetation
7

8. Temporal/seasonal coverage
Source: Chantarat et.al 2012 JRI
8

9. 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

10. Predicted mortality index readings
10

11. • 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
11

12. Risk contingent credit
• Objective: Minimizing downside risks and unlocking
credit to smallholder farmers
• Definition: Risk-contingent credit is a general term for
any credit instrument that embeds within its structure a
contingent claim which, when triggered, transfers a part
of the borrower’s liability or debt service to the lender.
• The credit product is designed with actuarially fair
interest rate (above base interest rate) that is determined
by expected value of indemnified risk
12

13. Schematic of risk contingent credit
13
Milk/ commodity price
LoanrepaymentOptionpayout
Put Option
premium reflected
in loan interest
rate
Loan amount

14. Schematic of risk contingent credit
14
Predicted mortality
LoanrepaymentOptionpayout
Loan amount
Call Option
premium reflected
in loan interest
rate

15. 15
Pricing of Commodity Linked Loan
Lender’s portfolio with commodity option PV=
Without option PV=
To hedge commodity price risk,
))]](,0[max([
*
tSKfeeB TrrT
−−= −
ψ
TrrT
feeB )(
1
**
−
=
TrrTTrrT
feetSKEfee )( ***
))]](,0[max([ −−
=−−ψ
T
e
f
tSKE
r
Tr






+
−
=
)(
*
**))](,0[max(
ln
ψ

16. Example: Risk Contingent Credit for Pulse Crops in India
(Shee and Turvey, 2012)
Time (years) 1
Loan amount (f) 20000
Strike 2825
Insurance Cost 215.43
r** 12%
Scale factor 7.079646
r* 0.185446
risk Premium 6.545%
Loan at expiry 24075.11
Loan without option 22549.94
Difference 1525.168
New prices Loan repayment
3531.25 24075.11
2825 24075.11
2542.5 22075.11
2260 20075.11
1977.5 18075.11
1695 16075.11
1412.5 14075.11
1130 12075.11
847.5 10075.11
Commodity price vs loan obligation
0
5000
10000
15000
20000
25000
30000
0 500 1000 1500 2000 2500 3000 3500 4000
Ranchi Bengalgram prices
Loanrepayment

17. 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.
• Risk-contingent loans virtually guarantee loan repayment relative to
indemnified risk: reduce chance of poverty traps, scale up production,
improve food security
17

18. 18
Thank you for your attention and comments