Climate Change, Climate Variability And Poverty Traps:
The Role (and Limits) of Index Insurance for East African Pastoralists
Christopher B. BarrettCornell University
Presentation at the Brown International Advanced Research Institute on Climate Change and Its Impacts
Providence, RIJune 13, 2011
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!
Motivation
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
Insurance to manage risk
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 Insurance1. 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 loss3. 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
shows5. Low cost mechanism
– Delivery through partners
Index insurance
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.
NASA NDVI Image Produced By: USGS-EROS Data Center. Source: FEWS-NET
ZNDVI: Deviation of NDVI from long-term average
Laisamis Cluster
-3-2-1012345
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
Karare
Logologo
Ngurunit
Korr
Laisamis Cluster, zndvi (1982-2008)
Historical droughts
NDVI (Feb 2009, Dekad 3)
IBLI insures against area average herd loss predicted based on NDVI data fitted to past livestock mortality data.
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).
Upper Marsabitcluster
Lower Marsabitcluster
KARGI
SHURA
MAIKONA
BUBISA
TURBI
ILLERET
GALAS
SABARET
KOYA
DARADE
NORTH HORR
DUKANA
EL GADE
KORR
KURUGUM
BALESA
LAISAMIS
EL-HADI
FUROLE
KALACHA
HAFARE
GAS
HURRI HILLS
LOIYANGALANI
KURUNGU
LONTOLIO
ARAPAL
LOGOLOGO
QILTA
MT. KULAL
MOITE
GUDAS/SORIADI
KARARE
IRIR
NGURUNIT
LARACHI
KAMBOYESOUTH HORR(MARSA)
LONYORIPICHAU
SONGA
MERILLE
ILLAUT(MARSABIT)
HULAHULA
MAJENGO(MARSABIT)
OGUCHO
OLTUROT
JALDESA
KITURUNIDIRIB GOMBO
JIRIME
SAGANTE
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 PricingPayoffs 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 PerformancePerformance 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
0%
10%
20%
30%
40%
50%
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
Actual Vs. Predicted Seasonal Mortality Rate - Laisamis Cluster
PredictedActual
0%
10%
20%
30%
40%
50%
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
Actual Vs. Predicted Seasonal Mortality Rate - Chalbi Cluster
PredictedActual
Out of sample
IBLI
IBLI ImplementationCommercially launched in January 2010Two 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)
Sample selection: 924 households • Sample/site proportional to relative pop. sizes• For each site, random sampling stratified by livestock wealth
class
KARGI
SHURA
MAIKONA
BUBISA
TURBI
ILLERET
GALAS
SABARET
KOYA
DARADE
NORTH HORR
DUKANA
EL GADE
KORR
KURUGUM
BALESA
LAISAMIS
EL-HADI
FUROLE
KALACHA
HAFARE
GAS
HURRI HILLS
LOIYANGALANI
KURUNGU
LONTOLIO
ARAPAL
LOGOLOGO
QILTA
MT. KULAL
MOITE
GUDAS/SORIADI
KARARE
IRIR
NGURUNIT
LARACHI
KAMBOYESOUTH HORR(MARSA)
LONYORIPICHAU
SONGA
MERILLE
ILLAUT(MARSABIT)
HULAHULA
MAJENGO(MARSABIT)
OGUCHO
OLTUROT
JALDESA
KITURUNIDIRIB GOMBO
JIRIME
SAGANTE
Legend
MarsabitIBLI
HSNP, IBLI Game_HSNP, No IBLI Game_No HSNP, IBLI Game_No HSNP, N
HSNP, IBLI Game
HSNP, No IBLI Game
No HSNP, IBLI Game
No HSNP, No IBLI Game
IBLI Game No IBLI Game
HSNP 4 sites 4 sites
No HSNP
5 sites 3 control sites
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.
IBLI
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.
Break
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.
Threat of Climate Change
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.
Core Question
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 rainfall-conditional 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.
Parametric herd growth estimates match NP results
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.
Variable
Rainfall Regime
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
Key findings 1
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Exp
ecte
d h
erd
siz
e 10
yea
rs a
hea
d
Initial herd size
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.
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.
Simulated using the parametric herd growth function estimates and mean-preserving changes of rainfall variance, defined by π= prob(rainfall<250 mm/yr)
0
10
20
30
40
50
60
0 10 20 30 40 50 60
Exp
ecte
d h
erd
siz
e 10
yea
rs a
hea
d
Initial herd size
Prob. = 0.03
Prob. = 0.12
Prob. = 0.06
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.
Policy implications
Thank you for your time, interest and comments!