1. UNHCR compound, Juba, South Sudan 13 – 15 November 2018
Accountability and Learning Event Drivers of Vulnerability in SS (FSNMS Round 20 & 22)
UNHCR compound, Juba, South Sudan 13 – 15 November 2018
Alemu A. Manni, FAO
Aaron Wise, WFP
Danvers Omolo, FAO

2. Content
Understanding vulnerability drivers through describing “profiles” of food insecure (vulnerable) households
Profiles of beneficiaries vs non-beneficiaries
Livelihood Characterizations - vulnerable and non-vulnerable households

3. General findings
While the proportion of households classified as Severely Food Insecure increased from 24 to 29 percent between 2017 and 2018, other measures were more indicative of a general improvement
Severity declined in the 7 CPAs from 32 to 29% with declined geographical spread from 2017
The Food Consumption Score (FCS) is computed from the numbers of days in a 7-day period on which households consume foods within 8 basic groups. Food consumption indicators are designed to reflect the quantity and quality of people’s diets1 . The FCS is a measure of dietary diversity, food frequency and the relative nutritional importance of the food consumed.
Candidate Partnership Areas (CPAs)

4. Severe food insecurity is becoming less widespread geographically, but is intensifying in several areas with dire impacts on levels of acute malnutrition

5. Comparisons: 2017 vs 2018
Multi-sectoral humanitarian assistance plus relative stability in the country might have contributed to “positive” outcomes, though the number of people in Phases 3,4 & 5 are at a record high.
Oct – Dec > Oct – Dec 2018
Status
No. of Counties %
No change 46
59%
Predominantly Phases 3&4
Two phase change 2 3%
Significant deterioration
One phase change (deterioration) 11
14%
Predominantly Phase 3 to 4
Phase changes 19
24%
Improvement
Total 78

6. Key results: General
Best improvements in FCS were observed among households receiving food assistance1 and those which own livestock
1 Households who reported that their main source of livelihood was Food Assistance - Humanitarian assistance is projected to have an impact in 14 counties (19%), sufficient to change phase.

7. 2014–2018 Peak of lean season trends (impact of humanitarian assistance)
Given current delivery capacity of humanitarian assistance, Phases 2 and 4 have generally stabilized, but Phase 3 is increasing at a significant rate
Though the percentage of the population in Phase 4 has doubled since 2016

8. Key results: Profile of Food Insecure HHs
In RD 22, severely food insecure households were more1 likely to:
Have no education
Have no land
Be a Returnee
Be female-headed
Have no livestock
1 Compared to all households in the RD22 survey

9. Key results: Change in Profile of Food Insecure HHs
Compared to RD20, the profile of severely food insecure HHs in RD22 were increasingly likely to:
Be a Returnee
Have no land
Be female-headed
Have no livestock
*Meaning these households made up a greater share of severely food insecure than in RD20

10. Change in Profile of food aid beneficiary HHs
Compared to RD20, the profile of beneficiary HHs in RD22 were increasingly likely to:
Have access to improved water
Report Agriculture as main livelihood
Have high dependency ratio1
1 For the current analysis, dependency ratio represents the proportion of all household members that are children (<16) and elderly (>60)

11. Change in Profile of agricultural input beneficiary HHs
Compared to RD20, the profile of agricultural input beneficiary HHs in RD22 were increasingly likely to:
Have more asset – land, LS, and durables
1. Access to basic services: school, health, water, sanitation, market, etc.
2. Assets (productive and non-productive): land, livestock, agricultural tools, house, etc.
3. Social safety nets: access to credit, access to borrowing opportunity and frequency, access to transfer, etc.
4. Adaptive capacity: ability to adapt to a new situation and develop new livelihood strategies (years of education, livelihood diversification, income sources, coping strategies, etc.)
1 For the current analysis, dependency ratio represents the proportion of all household members that are children (<16) and elderly (>60)

12. Profiles of FAO beneficiary and non-beneficiary HHs
Profiles of FAO non- agricu input beneficiary HHs in RD22 were increasingly likely to be more dependent on humanitarian assistance (safety net) and have better adaptive capacities (including coping)
1 For the current analysis, dependency ratio represents the proportion of all household members that are children (<16) and elderly (>60)

13. Which households are resilient?
Clusters
Characteristics 1 2 3 4 5 6
Livelihood (main)
Agriculture
Ag, petty, food assistance
Ag, other, petty
Livestock
Ag, formal
Ag, livestock, food assistance
Education HoH
None
Some primary
Some secondary
Status
Resident
IDP/returnee
Sex HoH
Female
Male
Own land
100%
61%
47%
83%
90%
79%
Own livestock
60%
45%
28%
48%
50%
Dependency
Moderate
High (Elderly-headed)
Improved water
High
Low
Migrate
Some
Clustering analysis1 allowed for the creation of groups2 to trace between RD20 and RD22
1 Clustering is an analytical technique that seeks to create groups which minimize differences internally and maximize difference with other groups
2 For the current analysis, 6 groups were chosen to provide a sufficient range while also facilitating general interpretation

14. Group performance between rounds
Group 3 materially deteriorated between 2017 and 2018, while groups 4 and 5 proved the most coping capacity Consistent with earlier findings that IDP/returnee HHs, those without livestock, without land were most vulnerable

15. Takeaways
Even whilst the food security situation in South Sudan appears to have modestly improved between 2017 and 2018, the intensity and levels of vulnerability are high
HHs of most concern include those with no land, no livestock, female-headed and IDP/returnees
The change in beneficiary profile between 2017 and 2018 suggests that targeting (both FA and agr input) might be increasingly favoring those with better access (e.g. improved water, infrastructure, education, etc.) -> more analysis of data needed