Index-Based Livestock Insurance (IBLI):
Preliminary Findings on the Positive
Impacts of An Imperfect Product
Christopher B. Barrett, Cornell University
International Livestock Research Institute, Nairobi, Kenya
July 3, 2014
Motivation: Why IBLI?
Catastrophic herd loss risk due to major droughts
identified as the major cause of poverty traps facing
east African pastoralists (Lybbert et al. EJ 2004, Barrett et al. JDS
2006, McPeak et al. book 2011, Santos & Barrett JDE 2011).
Climate change may aggravate this (Barrett and Santos EE 2014).
Standard responses – food aid, post-drought restocking
– are often slow, insufficient and expensive.
Conventional commercial insurance not viable due to
high transactions costs, moral hazard/adverse selection.
The Potential of IBLI
Index insurance (like IBLI) is a variation on traditional insurance:
- Does not insure individual losses.
- Instead insures some “index” that is strongly correlated
with individual losses, objectively verifiable, low cost, nearreal time, not manipulable by either party to the contract.
- Resolves cost and information problems of conventional
insurance at the price of basis risk (imperfect correlation
between individual losses and the insured index).
IBLI can, in principle, offer a timely, financially sustainable, selftargeting safety net against catastrophic drought shocks. Can
help accelerate herd recovery and reduce human suffering.
But it’s necessarily imperfect … lottery ticket or true insurance?
Index-Based Livestock Insurance: Design
The signal: Normalized Difference Vegetation Index (NDVI) collected by satellite
Response function: regress historic livestock mortality onto transforms of historic
cumulative standardized NDVI (Czndvi) data. In Borana, just NDVI.
Indemnity payments: In Kenya, predicted livestock mortality >15% according to:
(Jensen, Barrett &2014)
𝑚𝑎𝑥 𝑖𝑛𝑑𝑒𝑥𝑑,𝑡 (𝐿𝑑,𝑡 , 𝜇𝑑,𝑡 ) − 0.15, 0 ∗ 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘 𝑖𝑛𝑠𝑢𝑟𝑒𝑑
1 year contract coverage
Temporal Structure of IBLI contract:
12 month contract sold during 2month sales windows just prior to
usual start of seasonal rains. Payouts
March 1 and/or October 1.
Chantarat et al. 2013
LRLD season coverage
Jan
Feb
Sale period
For LRLD
Mar
Apr
May
Jun
Jul
Aug
SRSD season coverage
Sep
Oct
Nov
Dec
Jan
Feb
Period of NDVI observations for
constructing LRLD mortality index
Sale period
For SRSD
Period of NDVI observations
For constructing SRSD
mortality index
Predicted LRLD mortality is announced.
Indemnity payment is made if IBLI is triggered
Predicted SRSD mortality is announced.
Indemnity payment is made if IBLI is triggered
IBLI Pilots in Ethiopia and Kenya
IBLI products (surveys) launched in Marsabit, Kenya in Jan 2010
(Oct 2009) and in Borana, Ethiopia, in Aug 2012 (Mar 2012).
(Jensen, Barrett &2014)
IBLI: An Imperfect Product
Covariate risk is important but
household losses vary a lot …
and the index does not
perfectly track covariate losses.
(Jensen, Barrett & Mude 2014)
Notes: The left figure illustrates the covariate (average) loss rate in each
season. The right figure illustrates the distribution of losses within each
seasons. The boxes depict the interquartile range, the upper and lower adjacent
values are either 3/2 the interquartile range or the value furthest from the
median. The remaining observations fall outside the adjacent values.
Notes: Covariate loss-index observations are seasonal division
average mortality paired with the index value for that divisionseason. Fitted lines and confidence intervals are generated by
regressing livestock mortality rates on the index.
- IBLI hhs still hold most risk: Division avg downside basis risk 68-93% of total.
- Most basis risk is idiosyncratic and random, not targetable or correctable.
- Significant spatial variation in covariate share – geographically target IBLI?
Jensen, Barrett & Mude 2014
IBLI Uptake Significant Nonetheless
In HH surveys , in Borana (Ethiopia)/Marsabit (Kenya):
- 54/44% ever purchased IBLI within first 4 sales periods
- But repurchase rates low: 18-68%/16-27%
- High rates of disadoption : 26/41% within 2 years
Key determinants of IBLI uptake
General uptake findings — robust across specifications and surveys
Price: Responsive to premium rate (price inelastic). Price elasticity grows
w/design risk.
Design Risk: Design error reduces uptake; greater effect at higher premium rates.
Idiosyncratic Risk: Hh understanding of IBLI increases effect of idiosyncratic risk
Understanding: Extension/marketing improves accuracy of IBLI knowledge but no
independent effect of improved understanding on uptake.
Herd size: Likelihood of uptake increasing in HH herd size
Liquidity: IBLI purchase increasing w/HSNP participation and HH savings
Intertemporal Adverse Selection: HHs buy less when expecting good conditions.
Spatial Adverse Selection: HHs in divisions with covariate risk are more likely to
purchase and with greater coverage (spatial adverse selection).
Gender: no gender diff in uptake. Women more sensitive to risk of new product.
Bageant 2014; Jensen, Mude & Barrett 2014; Takahashi et al. 2014
IBLI’s Impacts: Herd mortality risk
Proportion of households for whom IBLI improves their position with respect to each statistic
Proportion better off with IBLI coverage
Statistic
Loaded & Unsubsidized
Subsidized
Mean
0.304
1.000
Variance
0.346
0.346
Skewness
0.818
0.818
Semi-Variance
0.354
0.595
Proportion of households that are better off with IBLI than without (Simulated utility analysis)
Subsidized Rate1 Actuarially Fair2
Coefficient of Risk Aversion
R=0 (risk neutral)
R=1
R=2 (risk averse)
Lower
0.999
0.975
0.966
Upper Lower
0.938
0.942
0.914
0.921
0.912
0.890
Upper
0.536
0.584
0.605
Loaded &
Unsubsidized3
Lower
Upper
0.626
0.741
0.613
0.737
0.569
0.748
1At
that time that this data was collected, the annual subsidized rates were Central/Gadamoji=3.325%, Laisamis=3.325%,
Loiyangalani=3.325%, and Maikona= 5.5%. 2The within-sample actuarially fair annual premium rates are
Central/Gadamoji=9.25%, Laisamis=7.5%, Loiyangalani=7.0%, and Maikona=12.25%. 3The commercial annual premium rates are
Central/Gadamoji=10.6%, Laisamis=11.3%, Loiyangalani=9.2%, and Maikona=10.7%.
Jensen, Barrett & Mude 2014
IBLI’s Impacts: Livestock productivity/income
Production strategies:
Herd Size
Veterinary Expenditures (KSH)
Household is Partially or Fully
Mobile
Production outcomes:
Milk income (KSH)
Milk income per TLU (KSH)
Livestock Mortality Rate (X100)
HSNP (FE-IV)
IBLI (FE-IV)
-3.405
(4.349)
[0.090]
363.9
(269.8)
[0.095]
0.231***
(0.088)
[0.261]
-2.639
(2.190)
[0.079]
592.1**
(295.2)
[0.090]
0.184
(0.142)
[0.247]
1,143
(1,411)
[0.181]
12.71
(136.8)
[0.221]
0.0150
(0.0362)
[0.177]
4,605**
(1,995)
[0.161]
671.3***
(197.8)
[0.170]
-0.0466
(0.0512)
[0.175]
A complete list of covariates, coefficient estimates, and model statistics
can be found in Jensen, Mude & Barrett (2014). Clustered and robust
standard errors in parentheses. R2 in brackets. *** p<0.01, ** p<0.05, *
p<0.1.
HSNP Participation:
•Improves the likelihood that
target type (poor, old, many
dependents) households remain
mobile, an important
characteristic for pastoralists
IBLI coverage:
•Increases investments in
maintaining livestock through
vet expenditures
•Increases total and per TLU
income from milk.
Note: TLU veterinary expenditures are
pos/sign related to milk productivity
Jensen, Mude & Barrett 2014
IBLI’s Impacts: Less adverse post-drought coping
Marsabit HHs received IBLI indemnity payments in October 2011,
near end of major drought. Survey HHs with IBLI coverage report
much better expected behaviors/outcomes than the uninsured:
- 36% reduction in likelihood of distress livestock sales, especially
(64%) among modestly better-off HHs (>8.4 TLU)
- 25% reduction in likelihood of reducing meals as a coping
strategy, especially (43%) among those with small or no herds
IBLI appears to provide a flexible safety net, reducing reliance on
the most adverse behaviors undertaken by different groups.
Janzen & Carter 2013
IBLI’s Impacts: Household subjective well-being
Borana survey HHs report overall life satisfaction. In principle,
insurance helps risk averse people even when it doesn’t pay out.
But an imperfect product with commercial loadings might not.
• IBLI has a positive, stat sig effect on HH well-being, even after premium
payment and w/o any indemnity payments
• IBLI coverage for 5 TLU moves a HH 1 step up the SWB scale
• Fully insuring 20 TLU (roughly sample mean) shifts HH from lowest to
highest SWB category
• Ex post of contract, purchasers exhibit some buyer’s remorse in the absence
of indemnity payments.
• But the positive effect of IBLI coverage is significantly higher than the
negative effect of buyer’s remorse.
Even with prospective buyer’s remorse, IBLI purchase improves
subjective well-being.
Hirfrot et al. 2014
Although IBLI offers incomplete and imperfect
coverage against herd loss, uptake is solid and
IBLI has clear favorable impacts on purchasers.
IBLI is a promising option for addressing poverty
traps that arise from catastrophic drought risk
Thank you for your time, interest and comments!
For more information visit www.ilri.org/ibli/