Parallel Session IIIb: Do livestock markets enhance pastoralists’ resilience against climate-induced external shocks? Evidence from a conflict-prone marsabit county, kenya

1. Senior Lecturer, University of Nairobi
DO LIVESTOCK MARKETS ENHANCE PASTORALISTS’ RESILIENCE
AGAINST CLIMATE-INDUCED EXTERNAL SHOCKS? EVIDENCE FROM A
CONFLICT-PRONE MARSABIT COUNTY, KENYA
Dr. Patrick Irungu

2. • Background
• Methods and Data
• Key Findings
• Conclusions & Suggestions
OUTLINE

3. 3
1. Background

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1. Background…

5. 5
1. Background…

6. 6
1. Background…

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1. Background…
(c) Employment
(d) Local institutional framework for fostering
change
(e) Platform for conflict resolution & peace
building
(a)Platform for trade
(b)“Vent-for-surplus” for the disposal of
assets and replace and/or rebuild lost
assets

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Study Objective
• Do livestock markets actually contribute to
building pastoralists’ resilience against
drought and food insecurity?
• Case study: Marsabit County, Kenya

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2. Methods & Data
www.google.com
• 66,923km2
• 12,751 km2
• Rwanda & Burundi
• ~3X Djibouti
• 6pax/km
• 23.47 ASAL counties
• Hot & dry
• <650mm annually
• Livestock keeping
• 2,787,587 livestock
(2019)

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A. Study Areas
Source: www.google.com

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B. Data Types & Sources
1. Primary data
a) Qualitative – KIIs, FGDs, DFO, case studies (videos)
b) Quantitative
• Pastoralists’ household survey
• Cochrane (1963) = 388 households in 4 catchments:
Moyale (88), North Horr (121), Saku (89), Laisamis (90)
• 18 market operators’ survey – input providers, traders/brokers,
transporters & butchery operators
2. Secondary data
a) Desk review
b) Other literature

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C. Theoretical Framework
• Theory of development resilience (Barrett & Constas,
2014):
• Development resilience is “the capacity over time of
a person, household or other aggregate unit to avoid
poverty in the face of various stressors and in the
wake of myriad shocks. If and only if that capacity is
and remains high over time, then the unit is resilient”
• Means the ability to replace the domination of
circumstances and chance over individuals by the
domination of individuals over chance and
circumstances (Max and Engels, 1846)

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Systems Thinking on Resilience
“Capacities”
Bene et al. (2012)

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Three Resilience “Capacities”
• Absorptive capacity = ability to mitigate or resist the
impact of shocks and maintain stability without negative
impact on the basic needs of the household or the function
of the market system. It requires effective coping
strategies
• Adaptive capacity = ability of households or the market
system to learn and adjust to shocks and stresses through
incremental changes, to maintain flexibility, and to take
advantage of new opportunities that arise from change
• Transformative capacity = ability to fundamentally
change the structure of the system when the previous
system is no longer sustainable as a result of severe
shocks

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D. Measuring Household Resilience
• Two methods in literature:
(a) Indicator-based - suffers from “circular logic”
(b) Resilience “costs” approach (Bene, 2013):
• Overcomes “circular logic” problem
• Definition:
Resilience “costs” as “costs” that a household
(or a community, or an ecosystem) has to ‘pay’
to pass through a particular shock

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• Three types of “resilience costs”:
1. Anticipation costs = Ex ante investments made
to prepare for an adverse event (shock or stress)
2. Impact costs = Costs of destruction following the
impact of the shock
3. Recovery costs= Ex post costs of recovery,
including costs of replacing what has been
destroyed
• The higher the resilience “costs”, the higher
the household resilience
𝑹𝒆𝒔𝒊𝒍𝒊𝒆𝒏𝒄𝒆 𝒄𝒐𝒔𝒕𝒔∗
= 𝑨𝒏𝒕𝒊𝒄𝒊𝒑𝒂𝒕𝒊𝒐𝒏 𝒄𝒐𝒔𝒕𝒔∗ + 𝑰𝒎𝒑𝒂𝒄𝒕 𝒄𝒐𝒔𝒕𝒔∗ + 𝑹𝒆𝒄𝒐𝒗𝒆𝒓𝒚 𝒄𝒐𝒔𝒕𝒔∗

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Resilience Vs Vulnerability
“Inverse” relationship between household
resilience & vulnerability

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E. Measuring Household Vulnerability
• IPCC’s (2012) definition of vulnerability:
Vulnerability = AC – (S+E)
where:
AC= agent’s or system’s adaptive capacity
S = sensitivity to risk
E= exposure to risk
• Indicator-based method employed to measure
HH vulnerability using Principal Components
Analysis (PCA)

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E. Measuring Household Vulnerability
𝑉𝑖𝑛𝑑𝑒𝑥𝑖 = 𝐴𝐶𝑖 − (𝑆𝑖 + 𝐸𝑖)
𝑉𝑖𝑛𝑑𝑒𝑥𝑖 = 𝑘=1
25
𝑓𝑘
𝐴𝐶𝑖𝑘−𝐴𝐶𝑘
𝑆1𝑘
− 𝑘=1
15
𝑓𝑘
𝑆𝑖𝑘−𝑆𝑘
𝑆2𝑘
+ 𝑘=1
22
𝑓𝑘
𝐸𝑖𝑘−𝐸𝑘
𝑆3𝑘
AC= agent’s or system’s adaptive capacity
S = sensitivity to risk
E= exposure to risk
where

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F: Modelling Role of Markets in
Resilience Building
• Simultaneous equation:
ResCosts = f(Vindex, MP, other factors)
Vindex = f(ResCosts, MP, other factors)
• Use predicted ResCosts & Vindex values to
group HHs into 9 resilient-vulnerability
categories
• The 9 categories were used to assess the
role of markets in pulling out a HH from a
low resilience/high vulnerability category:

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Vulnerability
Resiliency
Very Moderately Not
Not
Moderately
Very
Very vulnerable
Very resilient
Very vulnerable
Mod. resilient
Very vulnerable
Not resilient
Mod. vulnerable
Very resilient
Mod. vulnerable
Mod. resilient
Mod. vulnerable
Not resilient
Not vulnerable
Very resilient
Not vulnerable
Mod. resilient
Not vulnerable
Not resilient

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3. Key Findings

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3.1 Respondents’ Characteristics
• Male (62.6%)
• No formal educ -87.6 %; 15.7% primary educ.
• 3.7% living with disability
• Av. family size = 6 pax ; national av. = 4.8 pax
• Sale of livestock and livestock products accounted
for 48.9% of HH income
• HH income: Saku>Moyale>North Horr>Laisamis
• 85% of HHs below the poverty line of US$
1.9/day; national av. = 36.8% in 2015 (WB, 2019)
• North Horr =89/264 > Moyale =66/264> Laisamis >
62/264 > Saku = 47/264
• Livestock holding: Saku=3.8, North Horr=4.4,
Laisamis=4.1; Moyale=4.3 TLUs
[1TLU = 1 cow & a calf = 5 goats or sheep]

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3.2 Livelihood options

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3.3 Exposure to Hazards

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3.4 Livestock deaths due to Drought
N.B.: 1TLU=1cow & calf=5goats/sheep

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3.5 Effect on Livestock Prices
Source: NDMA Reports (2018)

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3.6 Market Closures
Main Reasons
• Disease outbreaks – 65%
• Ethnic clashes – 11.2%
(n=197 responses)

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3.7 Drought Preparation Costs
• Only 4% HHs
prepared for
2016/17 drought
• Spent 24% of their
income
KShs 11,333
KShs 3,667
KShs 11,833
KShs 7,000

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3.8(a) Drought Coping Strategies

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3.8(b) Impact [Coping] Costs
• Includes on resources &
transfers
• Av. 35% of HH income

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3.9 Drought Recovery Costs

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3.10 Adaptive Capacity
(a) Group membership

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3.10 Adaptive Capacity…
(b) Receipt of social transfers

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(c) Access to services
Service/Facility Average distance (Km)
Saku North
Horr
Laisamis Moyale Pooled
Professional vet services 12.7 45.9 13.2 9.6 22.5
Nearest market 10.6 31.1 14.2 3.7 16.3
Nearest police post/station 9.8 13.3 16.1 4.1 11.0
All weather road 6.2 9.6 24.6 2.3 10.6
Grazing area 4.0 11.8 11.9 6.7 8.9
Health facility 4.4 3.9 7.6 2.2 4.5
Primary school 1.9 4.3 2.3 1.5 2.7
Water source 4.0 2.0 2.5 1.8 2.5
3.10 Adaptive Capacity…

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(d) Access to loans
3.10 Adaptive Capacity…

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(e) Sources of information
3.10 Adaptive Capacity…

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3.11 Effect of market on HH Resilience
Variable Std Error z P>|z|
Vindex 0.646 0.324 2.00 0.046
Subcounty:
North Horr 0.407 0.256 1.59 0.112
Laisamis 0.124 0.273 0.45 0.650
Moyale 0.536 0.287 1.87 0.062
Saku 0 0 - -
Age 0.020 0.005 3.89 0.000
Gender: Male 0.499 0.159 3.13 0.002
Yrs_Educ 0.025 0.016 1.59 0.112
HHSize -0.003 0.028 -0.12 0.908
MarketPart: Yes 0.160 0.134 1.20 0.232
LnIncome 0.094 0.049 1.89 0.058
LnTransfers 0.054 0.021 2.65 0.008
MarktDist -0.002 0.002 -0.99 0.323
MarktGov: Good 0.159 0.134 1.19 0.233
MarktActors -0.121 0.061 -2.00 0.046
CopingStrategies 0.229 0.044 5.20 0.000
MarktClosure: Yes 0.001 0.003 0.53 0.595
EarlyWarning: Yes 0.351 0.152 2.31 0.021
TLUOwned 0.015 0.006 2.55 0.011
Conflicts: Yes 0.460 0.224 2.06 0.039
Droughts: Yes 0.470 0.207 2.28 0.023
Diseases: Yes 0.150 0.156 0.97 0.334
Mobility: Yes 0.257 0.145 1.78 0.076
Intercept 4.50 0.982 4.59 0.000
n=193; RMSE=0.783; R2=0.4727; χ2=198.24, p=0.0000

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3.12 Effect of market on HH Vulnerability
Variable Std Error z P>|z|
ResCosts -0.177 0.041 -4.34 0.000
Subcounty:
North Horr -0.182 0.111 -1.64 0.102
Laisamis -0.155 0.119 -1.30 0.193
Moyale -0.149 0.122 -1.23 0.220
Saku 0.00 0.00 - -
MarketPart: Yes -0.011 0.062 -0.18 0.857
HazardsNo -0.034 0.016 -2.18 0.029
MarktClosure: Yes -0.004 0.001 -4.12 0.000
Warmer: Yes -0.103 0.078 -1.32 0.187
Diseases: Yes -0.138 0.061 -2.27 0.023
Droughts: Yes -0.173 0.091 -1.90 0.058
Drier: Yes -0.517 0.079 -6.51 0.000
Conflicts: Yes -0.228 0.072 -3.18 0.001
EthClashes: Yes -0.606 0.071 -8.56 0.000
Intercept 3.179 0.387 8.22 0.000
n=193; RMSE=0.372; R2=0.7235; χ2=539.74, p=0.0000

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3.13 Resilience/Vulnerability Categories
Very Not
Moderate
Vulnerability
Not
Moderate
Very
12.4
19.2
2.1
0.0
19.2 12.9
20.7 9.8
Resiliency 3.6
n=193 HHs

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3.14 Households in “Best” Resilience/Vulnerability
Categories By Market Catchment
• Moderately resilient/moderately vulnerable
• Moderately resilient/non-vulnerable
• Resilient/moderately vulnerable

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3.15 Does the Market Pull a HH out of Low Resilience High
Vulnerability Category?
Variable Std Error Odds Ratio z P>|z|
Subcounty:
North Horr 0.152 0.313 1.164 0.49 0.627
Laisamis -0.093 0.280 0.911 -0.33 0.740
Moyale -0.859 0.351 0.424 -2.45 0.014
Saku 0.00 0.00 - - -
Age: Old (>35yrs) 0.851 0.225 2.342 3.78 0.000
Gender: Male 0.168 0.193 1.183 0.87 0.385
Edu: Formal schooling 0.854 0.207 2.349 4.12 0.000
FamSize: Small (≤4 members) 0.376 0.200 1.456 1.88 0.060
MarketPart: Yes 0.051 0.189 1.052 0.27 0.786
Disability: No -0.715 0.359 0.489 -1.99 0.047
Occupation:
Crop farming -0.856 0.830 0.425 -1.03 0.302
Business 0.429 0.346 1.536 1.24 0.215
Salaried employment -0.633 0.563 0.531 -1.12 0.261
Wage employment -0.193 0.209 0.824 -0.92 0.357
Livestock keeping 0.00 0.00 - - -
Wealth category:
Medium 0.353 0.281 1.423 1.25 0.210
Rich 0.092 0.283 1.096 0.32 0.746
Poor 0.00 0.00 - - -
n=193; log pseudo-likelihood=-288.156; pseudo-R2=0.0800; Wald χ2=76.63, Prob> χ2=0.0000

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4. Conclusions
• Droughts are a common phenomenon in Africa Horn
including northern Kenya, Marsabit County
• Likely to get worse with climate change – cf. Cyclone
Freddy & ENSO cycle
• Pastoralists are doing something about it – adoption
of different coping/survival strategies
• Some of those strategies compromise their long-
term resilience building ability
• Many organizations/partners/stakeholders doing a lot
to bring markets closer to the people – millions spent
todate

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4. Conclusions…
• However, so far, the markets in Marsabit County do not
seem to offer pastoralists the ability to “recover and
bounce back” from drought shocks
• They also do not seem to offer pastoralists the ability
to reduce vulnerability to drought shocks
• Finally, markets do seem to act as a rung to pull out
pastoralists from non-resilient/high vulnerability
categories

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5. What To Do?
• Increase market awareness
• Re-think strategy of siting markets
• Community participation
• Reduce insecurity around markets
• Will likely increase market linkages & livestock offtake
• Social behavior change – “cattle complex” [Melville
Herskovits (1926)]?
• Incentives?
• Marshall Plan for northern Kenya & other arid areas?

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Acknowledgements

47. THANK YOU!