Identifying Resilience Thresholds to Food
Insecurity in Ethiopia and Nigeria
Manzamasso Hodjo, Marco d'Errico, Ibrahima Diouf

#2023 AGRODEP CONFERENCE
Outline
1. Motivation
2. Research question
3. Data and Empirical Approach
4. Results & Key findings
5. Recommendations

#2023 AGRODEP CONFERENCE
Motivation
1. Void in literature: RCI lacks
threshold(s)
2. Why is threshold(s) Important
a) Provide early warning signals for
impending failure
b) Inform decision-making in a wide range of
contexts
c) Designing and implementing effective

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Research Question
How does resilience in year t-1
determines HH food security in year t ?

Data and Empirical
Approach
 Methods
 Descriptive: Cohort analysis
Inferential: Threshold
model
 Threshold model
𝐹𝑆𝑖𝑡 = 𝛼 + 𝑋𝑖𝑡(𝑅𝐶𝐼𝑖𝑡−1, 𝛾𝑘)𝛽𝑗 + 𝜇𝑖 + 𝜀𝑖𝑡 (1)
Where,
𝑋𝑖𝑡(𝑅𝐶𝐼𝑖𝑡−1, 𝛾𝑘) =
𝑋𝑖𝑡𝐼(𝑅𝐶𝐼𝑖𝑡−1 ≤ 𝛾𝑘)
𝑋𝑖𝑡𝐼(𝛾𝑘 < 𝑅𝐶𝐼𝑖𝑡−1 < 𝛾𝑘)
𝑋𝑖𝑡𝐼(𝑅𝐶𝐼𝑖𝑡−1 > 𝛾𝑘)
(2)

Data and Empirical
Approach
Data : LSMS
Ethiopia: 4 waves (2011, 2013, 2015 & 2019)
Nigeria: 3 Waves (2010 2012 & 2015 )
Year RCI FCS HDDS
2011 47.18 39.92 6.12
2013 47.81 41.04 6.14
2015 47.22 40.66 6.01
2018 47.81 40.60 5.92
Average (2011-2018) 47.50 40.56 6.05
Ethiopia
Year RCI FCS HDDS
2010 61.33 55.40 7.46
2012 65.10 53.39 7.42
2015 67.45 53.89 8.56
Average (2010-2015) 64.56 54.23 7.80
Nigeria

Results
Cohort Analysis:Ethiopia
0.0
20.0
40.0
60.0
2011 2013 2015 2018
FCS
Not recovered Recovered
0
2
4
6
8
10
2011 2013 2015 2018
HDDS
Not recovered Recovered
0
20
40
60
80
2010 2012 2014 2016 2018 2020
RCI
Not recovered Recovered
0.0
20.0
40.0
60.0
2010 2012 2014 2016 2018 2020
RCI
Not recovered Recovered

Results
Cohort Analysis:Nigeria
0
20
40
60
80
2010 2012 2015
RCI
Not recovered Recovered
0
2
4
6
8
10
2010 2010 2015
HDDS
Not recovered Recovered
0
20
40
60
80
2010 2012 2015
RCI
Not recovered Recovered
0
20
40
60
80
2010 2012 2015
FCS
Not recovered Recovered

From Cohort Analysis
Evidence to soundly suspect
existence of RCI thresholds
Combining cut-offs for FCS
and HDDS scenarios,
consider 3 groups of HH
• Non-resilient
• Sub-resilient
• Resilient

Results
Threshold Model
• Binary dependent variable, food secure vs food insecure
• RCI in year t-1 is the TH variable
• Optimal # of thresholds : BIC & HQIC
Country
Dependent variable
base
Order Threshold SSR
Ethiopia
FCS 1 68.56 176.73
HDDS 1 51.06 159.79
Nigeria
FCS 1 48.97 185.71
HDDS 1 63.67 69.98

Results
Threshold Model
Clearly identify one TH based each Food security
Indicator
Combining THs leads to 3 HH groups:
Non-resilient (NR),
Sub-resilient (SR)
Resilient (R)
Gender of the HH and geographic localization are
significant determinants of TH regimes

Results
RSM for R, SR & NR : Ethiopia
• Ethiopia: 7% of resilient,
33% of sub-resilient and
61% of non-resilient
• RCI for Resilient households
is more balanced
26% 22%
34%
8%
9%
5%
53% 60%
39%
13% 8%
23%
NON-RESILIENT
(61%)
SUB-RESILIENT
(33%)
RESILIENT (7%)
ABS AST AC SSN

Results
RSM for R, SR & NR : Nigeria
• Nigeria: 54% of resilient, 29%
of sub-resilient and 18% of
non-resilient
• RCI for Resilient households is
more balanced
26% 22%
34%
8%
9%
5%
53% 60%
39%
13% 8%
23%
NON-RESILIENT (61%) SUB-RESILIENT (33%) RESILIENT (7%)
ABS AST AC SSN

Key Findings
Ethiopia
Non-resilient (RCI < 51.07) ;
 Sub-resilient (51.07<= RCI<68.6)
Resilient (RCI > 68.6)
Nigeria
Non-resilient (RCI < 49)
Sub-resilient (49<= RCI<63.7)
Resilient (RCI > 63.7)
Threshold does exist in RCI
3 groups of HH based on FCS and HDDS TH
The shorter the recovery period, the higher the resilience threshold
and vis versa.
Based on FCS and HDDS, Resilient HH recover at most 2 years
after the shock while NR and SR HHs still food insecure

THANK YOU

Results
Robustness check
• Robustness of the threshold model based on RCI threshold for
longer recovery period
• We expected a lower TH for longer recovery period
• TH model with 2018 food secure and insecure HH faced with a
shock in 2013 when they were all food secure
• TH of 54.62 for households that had five years of recovery (Vs
68.56 for 3 years recovery)

Recommendation
Comparing TH analysis across different countries, regions
or communities could help identify the commonalities
and differences in resilience determinants and strategies,
as well as the factors that contribute to successful
adaptation in different contexts.