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Climate Change, Climate Variability And
Poverty Traps:
The Role (and Limits) of Index Insurance
for East African Pastoralists
Christopher B. Barrett
Cornell University
Presentation at the Brown International Advanced Research Institute on
Climate Change and Its Impacts
Providence, RI
June 13, 2011
Motivation
Arid and semi-arid
lands (ASAL) cover ~
2/3 of Africa, home to
~20mn pastoralists –
who rely on extensive
livestock grazing.
Pastoralist systems
adapted to variable
climate, but very
vulnerable to severe
drought events. Big
herd losses cause
humanitarian and
environmental crisis.
Motivation
Poverty traps in the southern
Ethiopian rangelands
Standard policy response to
climate shocks in the ASAL:
food aid (slow, insufficient,
inefficient, even insulting).
Pay attention to the risk
and dynamics that cause
destitution!
Insurance to manage risk
Large economic/human costs of uninsured risk, esp. in
presence of 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
• Reinforce extant social insurance mechanisms
But conventional (individual) insurance rarely works
in remote rural areas like the ASAL:
• High transactions costs
• Moral hazard/adverse selection
Index insurance
Index insurance can avoid problems that make
individual insurance infeasible in ASAL:
• 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 relief
In principle, index insurance can help create an
effective safety net to alter poverty dynamics and
help address climate shocks.
Index insurance
‘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 delivery mechanism for making insurance
available for numerous small and medium scale
producers
Index insurance
Solutions to the ‘Big 5’ Challenges
1. High quality data:
– Satellite data (remotely sensed vegetation index:
NDVI)
2. Minimize uncovered basis risk:
– Analysis of household data on herd loss
3. Innovation incentives for insurers:
– Researchers do product design, develop awareness
materials and assist with capacity building
4. Establish informed effective demand
– VIPs; Simulation games; comic books; radio shows
5. Low cost mechanism
– Delivery through partners
IBLI
New commercial Index-Based Livestock Insurance
(IBLI) product launched commercially in January
2010 in Marsabit District in northern Kenya
Based on technical design developed at Cornell, refined and
led in the field by the International Livestock Research
Institute (ILRI) in collaboration with university and private
sector partners.
Now being adapted and extended to Ethiopia and expanded
to other ASAL districts in Kenya.
IBLI
ZNDVI: Deviation of NDVI from long-term average
NDVI (Feb 2009, Dekad 3)
IBLI insures
against area
average herd
loss predicted
based on
NDVI data
fitted to past
livestock
mortality data.
Laisamis
Laisamis Cluster,
zndviCluster
(1982-2008)
Karare
Logologo
NASA NDVI Image Produced By: USGS-EROS Data Center. Source: FEWS-NET
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1987
1986
1985
1984
1983
1982
Ngurunit
1981
5
4
3
2
1
0
-1
-2
-3
Korr
Historical droughts
IBLI
NDVI-based Livestock Mortality Index
The IBLI contract is based on area average livestock
mortality predicted by remotely-sensed (satellite)
information on vegetative cover (NDVI):
IBLI
Spatial Coverage
– Two separate area-specific “response functions” map
NDVI into predicted livestock mortality.
– Five separate index coverage regions (2 in one area, 3
in the other).
SABARET
ILLERET
Upper Marsabit
cluster
DUKANA
EL-HADI
DARADE
FUROLE
BALESA
NORTH HORR
HURRI HILLS
MOITE
EL GADE
GALAS
KALACHA
GAS
MAIKONA
LOIYANGALANI
TURBI
ARAPAL
LARACHI
KURUGUM
OLTUROT
MT. KULAL
Lower Marsabit
cluster
KURUNGU
BUBISA
MAJENGO(MARSABIT)
KARGI
JIRIMEQILTA
HULAHULA
SAGANTE
OGUCHODIRIB GOMBO
KITURUNI
SONGA
KARARE JALDESA
SOUTH HORR(MARSA)HAFARE
KAMBOYE
KORR
ILLAUT(MARSABIT)
LOGOLOGOGUDAS/SORIADI
LONYORIPICHAU
NGURUNIT
LAISAMIS
LONTOLIO
KOYA
IRIRMERILLE
SHURA
IBLI
Temporal Coverage
– Year-long contract, with two prospective indemnity
payment dates, following each dry season.
– Two marketing campaigns, just prior to rainy season.
– NDVI observed and index updated continuously.
IBLI
Risk Coverage and Pricing
Payoffs for predicted losses above 15% (“strike point”).
Trade off: Higher Strike  Lower Risk Coverage  Lower Cost
Contract Cluster
Consumer Price
Upper Marsabit
5.5%
Lower Marsabit
3.25%
IBLI
Testing the Index Performance
Performance of predicted herd mortality rate in predicting area-average
livestock mortality observed in longitudinal data
– Out-of-sample prediction errors within 10% (especially in bad years)
– Predicts historical droughts well
Out of sample
Actual Vs. Predicted Seasonal Mortality Rate - Laisamis Cluster
50%
40%
30%
Predicted
Actual
20%
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
0%
1982
10%
Actual Vs. Predicted Seasonal Mortality Rate - Chalbi Cluster
50%
40%
30%
Predicted
Actual
20%
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
0%
1982
10%
IBLI
IBLI Implementation
Commercially launched in January 2010
Two sales periods of varying experience:
• Jan/Feb 2010: Sold ~2000 contracts: Premiums collected
~ $46,000: Value of livestock covered ~$1,200,000
• Jan/Feb 2011: Sold ~750 contracts: Premiums collected ~
$9,500
Key ongoing considerations/challenges:
•
•
•
•
Delivery Channel
Extension/Education
Information Dissemination and Trust Building
Regulation
IBLI
Impact Evaluation Under Way
Confounding factor: ongoing implementation of cash transfer (HSNP)
Encouragement design
•Insurance education game: played among 50% sample in game site
•Discount coupon on the first 15 TLU insured: (no subsidy for 40% of
sample, 10%-60% subsidies for the rest)
Legend
IBLI Game
MarsabitIBLI
SABARET
ILLERET
DUKANA
No IBLI
Game
HSNP, IBLI Game_HSNP, No
EL-HADI
HSNP, IBLI Game
DARADE
FUROLE
BALESA
HSNP, No IBLI Game
NORTH HORR
HURRI HILLS
HSNP
4 sites
4 sites
MOITE
No HSNP, IBLI Game
EL GADE
GALAS
KALACHA
GAS
No HSNP, No IBLI Game
MAIKONA
LOIYANGALANI
TURBI
ARAPAL
LARACHI
KURUGUM
No
HSNP
5 sites
3 control
sites
OLTUROT
MT. KULAL
KURUNGU
BUBISA
MAJENGO(MARSABIT)
KARGI
JIRIMEQILTA
HULAHULA
SAGANTE
OGUCHODIRIB GOMBO
KITURUNI
SONGA
KARARE JALDESA
KAMBOYE
SOUTH HORR(MARSA)HAFARE
KORR
ILLAUT(MARSABIT)
LOGOLOGOGUDAS/SORIADI
LONYORIPICHAU
SHURA
 Sample selection: 924 households
• Sample/site proportional to relative pop. sizes
•For each site, random sampling stratified by livestock wealth class
NGURUNIT
LAISAMIS
LONTOLIO
KOYA
IRIRMERILLE
IBLI
Core impact evaluation questions
1) For whom is IBLI most attractive and effective?
- simulation-based answer: IBLI most valuable among the vulnerable
non-poor
- simulation-based and WTP survey based answer: Highly price elastic
demand for IBLI
2) Does IBLI induce increased asset accumulation and
escapes from poverty? Does it reduce asset loss and falls into
poverty? How does it perform relative to cash transfers? Are
there spillover effects on the stockless poor?
- simulation-based answers: Yes on first two points. Don’t know on
latter two questions.
Use survey data to test these hypotheses in quasi-experimental setting
with real insurance in a survey designed to test IBLI vs./with cash
transfers under Kenya’s new Hunger Safety Nets Program.
Break
IBLI is a promising option for putting
climate risk-based poverty traps behind us
Thank you for your time, interest and comments!
Let’s take a short break.
Threat of Climate Change
Much attention to climate
change impacts in Africa.
But focus falls mainly on
the likely effects of changes
in average rainfall and
temperature on crop
output.
Little study of the likely
consequences of increased
climate variability, nor to
the likely effects on
livestock systems.
Core Question
What happens to east African pastoralists if the frequency of
extreme rainfall events changes?
Barrett & Santos (2011) explore the likely consequences of
more frequent drought in the African ASAL on pastoralists’
livestock herd dynamics.
- Use original primary data on rainfall-conditional herd growth
dynamics collected among Boran pastoralists in S.Ethiopia
- Demonstrate state-dependence of herd growth
- Reproduce unconditional herd dynamics previously observed
- Simulate herd dynamics under changed climate distributions
The results demonstrate how vulnerable pastoralists systems
are to relatively modest increases in the frequency of drought.
Previous results
Past herd dynamics studies from the region find
nonlinear, bifurcated wealth dynamics. For example,
among the southern Ethiopia Boran pastoralists we
study, Lybbert et al. (2004 EJ) find:
Data
Data
Collected subjective herd growth expectations data,
conditional on anticipated rainfall regime, from 116
households in four villages from same Boran region.
Each household asked subjective dist’n of 1 year
ahead herd size based on 4 randomly assigned initial
herd sizes. Thus multiple observations per hh.
Methods
Methods
1) Nonparametrically explore differences in rainfallconditional herd dynamics.
2) Fit parametric herd growth functions.
3) Use estimation results from 2) and historical
rainfall data to simulate decadal herd dynamics.
Compare against previous results.
4) Use estimation results from 2) to simulate herd
dynamics under different climate distributions.
Key findings 1
Key findings
1) Not surprisingly, herd dynamics differ markedly
between good and poor rainfall states.
Figure 1. Expected one year ahead herd dynamics with (A)
poor rainfall or (B) good rainfall. Points reflect herder-specific
observations based on randomly assigned initial herd sizes.
The solid line reflects stable herd size. The dashed line depicts
the nonparametric kernel regression.
Key findings 1
Parametric herd growth estimates match NP results
Rainfall Regime
Variable
Very good
Good/Normal
Bad
Very Bad
h0
1.293 (0.00)
1.477 (0.019)
0.538 (0.224)
0.246 (0.246)
h02
0.026 (0.010)
0.009 (0.010)
h03
-0.00039 (0.0001)
-0.00017 (0.0001)
Constant
0.897 (0.448)
0.179 (0.416)
0.513 (1.185)
-0.575 (1.083)
N
61
96
192
61
R2
0.986
0.994
0.792
0.589
Table 1. Estimates of expected one year ahead herd size
conditional on rainfall regime (columns) and randomly assigned
initial herd size (h0). P-values in parentheses; estimates
statistically significant at the five percent level in bold.
Key findings 2
Key findings
2) Simulated herd dynamics using parametric herd
growth function estimates and historical (N(490,
152)) rainfall distribution generates unconditional
herd dynamics very similar to observed patterns.
60
Expected herd size 10 years ahead
50
40
30
20
10
0
0
10
20
30
40
50
60
Initial herd size
So pastoralists seem to grasp clearly the underlying
herd dynamics of he current system.
Key findings 3
Key findings
3) Herd dynamics change with drought (rainfall <250
mm/year) risk. Halving the current risk would
enhance resilience and eliminate apparent poverty
trap. By contrast, doubling drought risk would
eliminate high-level equilibiurm and lead to system
collapse in expectation.
60
Prob. = 0.03
Expected herd size 10 years ahead
50
Simulated using the
parametric herd growth
function estimates and
mean-preserving changes
of rainfall variance,
defined by π=
prob(rainfall<250 mm/yr)
Prob. = 0.06
40
30
Prob. = 0.12
20
10
0
0
10
20
30
Initial herd size
40
50
60
Policy implications
The main store of wealth of Africa’s pastoralists is at risk if
climate change brings increased drought, as expected.
Climate variability adaptation is crucial
ASAL pastoral systems highly vulnerable to potential
system change due to quite plausible changes in rainfall
variability. Need more than just food aid in response to
disasters. Need to alter herd dynamics to cope with
increasing drought risk.
Must begin addressing:
- range and water management
- resource tenure (e.g., dry season reserve access)
and reconcile with biodiversity conservation goals
- livestock insurance. IBLI one possible tool.
Thank you for your time,
interest and comments!