Republic of South Sudan POVERTY AND EQUITY ASSESSMENT June 2024 Republic of South Sudan POVERTY AND EQUITY ASSESSMENT June 2024 © 2024 International Bank for This work is a product of the staff The World Bank does not guarantee Reconstruction and Development / of The World Bank with external the accuracy of the data included The World Bank contributions. The findings, in this work. 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Cover photo: © Mayak Akuot / FAO 5 TABLE OF CONTENTS Acronyms............................................................................................................................................10 Acknowledgments..............................................................................................................................11 Executive Summary............................................................................................................................12 Introduction.......................................................................................................................................23 I.1. Country Context.................................................................................................................................23 I.2. Data Challenges and Solutions in Poverty Measurement..............................................................25 CHAPTER 1. POVERTY AND INEQUALITY: PROFILE AND TRENDS........................28 1.1. Poverty and Inequality Profile...................................................................................................30 1.1.1. Poverty Profile.................................................................................................................................30 1.1.2. Consumption Inequality................................................................................................................30 1.1.3. Socioeconomic Characteristics of the Poor.................................................................................32 1.1.4. Multidimensional Poverty.............................................................................................................34 1.2. Spatial Dimension of Poverty.....................................................................................................34 1.3. Vulnerability to Poverty.............................................................................................................39 1.4. Poverty and Inequality Trends...................................................................................................39 1.4.1. Poverty and Extreme Poverty Have Increased............................................................................39 1.4.2. Consumption Inequality Declined...............................................................................................41 1.5. Drivers of Poverty.......................................................................................................................41 1.5.1. Consumption Growth, Inequality, and Poverty..........................................................................41 1.5.2. The Contribution to Poverty of Changes in Household Endowments....................................43 CHAPTER 2. FOOD SECURITY.......................................................................................44 2.1. The Prevalence and Profile of Food Insecurity..........................................................................45 2.2. The Underlying Causes of Food Insecurity................................................................................51 2.2.1. Low Household Own Food Production.......................................................................................51 2.2.2. Low Access to the Market..............................................................................................................54 2.2.3. Persistent Conflict...........................................................................................................................55 2.2.4. Climate Shocks................................................................................................................................56 REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 6 CHAPTER 3. SHOCKS AND RESILIENCE......................................................................58 3.1. The Nature and Occurrence of Shocks.......................................................................................59 3.1.1. Food Prices......................................................................................................................................60 3.1.2. Climate Shocks and Natural Disasters.........................................................................................60 3.1.3. Conflict and Violence.....................................................................................................................63 3.2. Profile of the Individuals and Locations Most Vulnerable to Shocks.......................................66 3.3. Impacts of Shocks on Households..............................................................................................67 3.3.1. Self-Reported Impacts....................................................................................................................67 3.3.2. The Impact of Floods on Household Welfare..............................................................................68 3.3.3. The Impact of Conflict on Food Insecurity.................................................................................70 3.4. How Are South Sudanese Coping with Shocks?........................................................................70 3.5. The Landscape of Social Protection...........................................................................................74 CHAPTER 4. POLICY CONSIDERATIONS.....................................................................76 References...........................................................................................................................................84 Annexes..............................................................................................................................................88 REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 7 FIGURES Figure ES.1. GDP consumer prices, and poverty................................................................................14 Figure ES.2. Impact of floods on households.......................................................................................18 Figure ES.3. Impact of conflict on food insecurity..............................................................................18 Figure I.1. Mean temperature, 1901–2021........................................................................................24 Figure I.2. Standardized precipitation-evapotranspiration index, 1950–2022.............................24 Figure I.3. GDP per capita and average consumer prices, 2011–22...............................................25 Figure 1.1. Poverty rates, the poverty gap, and the squared poverty gap, 2021–22......................30 Figure 1.2. Inequality measures, 2021................................................................................................31 Figure 1.3. Consumption share, by quintile and location................................................................31 Figure 1.4. Educational attainment, poverty, and location..............................................................33 Figure 1.5. Households, by poverty and sources of livelihood........................................................33 Figure 1.6. Share of population who spend at least one hour to reach an agglomeration of 10,000 people or more.......................................................................................................37 Figure 1.7. Effects of travel times to urban agglomerations on consumption and poverty.........38 Figure 1.8. Changes in the poverty and extreme poverty rates, 2016/17–2022, percent..............40 Figure 1.9. Multidimensional poverty rates, 2016/17–2022, percent.............................................40 Figure 1.10. Gini index, 2016/17 and 2022..........................................................................................41 Figure 1.11. The per capita consumption growth rate (growth incidence curves), 2016/17–22.....42 Figure 1.12. Growth-inequality decompositions, 2016/17–22..........................................................42 Figure 1.13. Decomposition of the changes in the poverty rate, 2016/17–22..................................43 Figure 2.1. Food insecurity, 2010–21/22............................................................................................46 Figure 2.2. Trends in the food insecurity, 2019–21/22.....................................................................46 Figure 2.3. Food insecurity, by state...................................................................................................46 Figure 2.4. Food insecurity, by location.............................................................................................47 Figure 2.5. Food insecurity, by camp..................................................................................................47 Figure 2.6. Food insecurity, by sex of household head.....................................................................49 Figure 2.7. Food insecurity, by educational attainment of household head..................................49 Figure 2.8. School attendance, by food insecurity............................................................................49 Figure 2.9. Food insecurity, by wealth quintile.................................................................................50 Figure 2.10. Food insecurity, by type of shelter....................................................................................50 Figure 2.11. Food insecurity, by type of cooking fuel.........................................................................50 REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 8 Figure 2.12. Food insecurity, by type of lighting source.....................................................................50 Figure 2.13. Food insecurity, by main household activity..................................................................51 Figure 2.14. Main mode of food acquisition by residential areas......................................................52 Figure 2.15. Main mode of food acquisition by states........................................................................54 Figure 2.16. Accessibility to markets, by location and food insecurity ...........................................55 Figure 2.17. The effect of death and violence on the ability of households to obtain money or food.................................................................................................................................55 Figure 2.18. Household exposure to fatalities, by food insecurity and food consumption score.. 56 Figure 2.19. Household exposure to climate shocks, by food insecurity and climate indicators..57 Figure 3.1. Percentage of households that experienced at least one shock during the previous six months, by state...........................................................................................................59 Figure 3.2. Percentage of households that experienced specific shock by areas of residence......60 Figure 3.3. Flood hazard and food insecurity....................................................................................61 Figure 3.4. Population affected by drought and flood, 2009–22......................................................62 Figure 3.5. Drought risk and food insecurity....................................................................................63 Figure 3.6. Conflict events and fatalities, 2011–23...........................................................................64 Figure 3.7. Poverty and access to basic services, by location...........................................................66 Figure 3.8. Percentage of households subject to one shock or more in the last six 6 months, by quintile and location....................................................................................................66 Figure 3.9. Households reporting impact of shocks on their ability to access income or food, percent................................................................................................................................67 Figure 3.10. The effects of flooding on households.............................................................................69 Figure 3.11. Impact of conflict on food insecurity (Proportion)......................................................70 Figure 3.12. Livelihood coping strategies, by location and wealth quintile.....................................72 Figure 3.13. The livelihood coping strategy exhaustion rate..............................................................73 Figure 3.14. Three main programs among household respondents, by location and welfare quintile, percent.................................................................................................................74 REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 9 MAPS Map ES.1. Poverty, extreme poverty, and the distribution of the poor..........................................15 Map 1.1. Poverty rates and the distribution of the poor, by state................................................35 Map 1.2. Poverty rate and the distribution of the poor, by county..............................................35 Map 1.3. Extreme poverty rate and the distribution of the extreme poor, by county...............36 Map 1.4. Density of population and economic activity................................................................36 Map 1.5. Travel times to basic services and the closest agglomeration, minutes.......................38 Map 1.6. South Sudan High Frequency Survey coverage.............................................................40 Map 2.1. Food insecurity, by livelihood zones and counties, percent.........................................48 Map 3.1. Populations exposed to floods.........................................................................................61 Map 3.2. Drought risk, 1998–2014..................................................................................................63 Map 3.3. Conflict and IDPs in South Sudan...................................................................................64 Map 3.4. IPC phases, October–November 2022............................................................................65 Map 3.5. IDP camps: travel times to urban areas with populations > 50,000, minutes............65 Map 3.6. Emergency livelihood coping strategies, by state and livelihood zone.......................73 TABLES Table 1.1. Poverty estimates and imputation results, 2021–22......................................................29 Table 1.2. Multidimensional poverty index, by area.......................................................................34 Table 1.3. Contribution of dimensions to the multidimensional poverty index, by area...........34 Table 3.1. Indicative seasonal cropping calendar............................................................................68 Table 3.2. Ten livelihood coping strategies–food security, by residential area............................71 REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 10 ACRONYMS ACLED Armed Conflict Location and Event Data CARI Consolidated Approach to Reporting Indicator CCKP Climate Change Knowledge Portal CEM Country Economic Memorandum CFSAM Crop and Food Security Assessment Mission DTM Displacement Tracking Matrix EIA Energy Information Administration FAO Food and Agriculture Organization of the United Nations FEWSNET Famine Early Warning System Network FSI Food Security Index FSNMS+ Food Security and Nutrition Monitoring System–Plus GDP gross domestic product HBS Household Budget Survey HFS High Frequency Survey HRW Human Rights Watch ICA Integrated Center Analysis IDP Internally Displaced Person IPC Integrated Food Security Phase Classification MAFS Ministry of Agriculture and Food Security MDA Ministries, Departments, and Agencies NBS National Bureau of Statistics NGO Non-Governmental Organization NSDS National Strategy for Development of Statistics OHCHR Office of the United Nations High Commissioner for Human Right PEA Poverty and Equity Assessment R-ARCSS Revitalised Agreement on the Resolution of the Conflict in Republic of South Sudan SCD Systematic Country Diagnostic SDR Special Drawing Rights SWIFT Survey of Well-Being via Instant and Frequent Tracking UNMISS United Nations Mission in South Sudan WEO World Economic Outlook Database WFP World Food Program Note: The local currency, the South Sudan Pound (SSP), is used for currency amounts wherever possible. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 11 ACKNOWLEDGMENTS This report has been prepared by a multisectoral team at the World Bank led by Franck M. Adoho (Senior Economist, TTL). Several colleagues have provided valuable inputs for the drafting of the report, including Tomoyuki Sho, Nobuo Yoshida, and Takaaki Masaki (chapter 1), Thierry Hounsa (chapters 1–3), Eiman Osman (chapter 2 and 3), Romeo Gansey (chapters 1 and 3), Ando Rahasimbelonirina (chapter 1, and data cleaning), and Essama Nssah. Florence Poni, Tsehaynesh H. Michael Seltan, and Joyce Wani Gamba provided logistical assistance and administrative support during the preparation of this report. The report was completed under the collective guidance and leadership of Country Directors Ousmane Dione and Maryam Salim, Regional Director Hassan Zaman, Practice Managers Pierella Paci, and Rinku Murgai, Country Managers Firas Raad and Charles Undeland, and Lead Economist Tehmina Khan. The team sincerely appreciates the team members, who have all generously shared their substantive inputs, knowledge, and advice. Their dedication and contributions have been instrumental in ensuring the quality and comprehensiveness of the Poverty and Equity Assessment (PEA). The success of the PEA would likewise not have been possible without the expertise, contributions, and collaboration of South Sudan’s International Organization for Migration and World Food Programme teams. Our appreciations to all staff of the National Bureau of Statistics, development partners and stakeholders in South Sudan who participated in multiple consultations. The report has benefited from feedback provided at different stages by the following peer reviewers: Clarence Tsimpo N. (Senior Economist, EAWPV), Minh Cong Nguyen (Senior Economist, EPVGE), Shohei Nakamura (Economist, EEAPV), Nandini Krishnan (Lead Economist, ESAPV), and Tom Bundervoet (Lead Economist, EAEPV). Robert Zimmermann edited the report. The report was designed and typeset by Ha Doan. The team would like to thank everyone at the World Bank who contributed to the completion of this report as a successful exercise. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 12 EXECUTIVE SUMMARY This report is the first to assess poverty and inequality in South Sudan using nationally representative data since the country’s independence in 2011. A poverty assessment report was prepared in 2013 using the 2009 data for Sudan, and, in 2016/17, new poverty estimates were produced with data covering only 7 of the 10 states of South Sudan. Using the most recent 2022 Household Budget Survey (HBS) and the first nationally representative household survey data since South Sudan’s independence, this poverty assessment finds that the share of the population living below the national poverty line of SSP358,724 a year is 75.9 percent.1 It also indicates widespread extreme poverty; 67.3 percent of the population is living on less than SSP298,478 a year, the national food poverty line.2 Poverty and Food Insecurity in a Context of Protracted Conflict Estimates derived by complementing the 2022 survey with the 2021 Food Security and Nutrition Monitoring System–Plus (FSNMS+) indicate that poverty is widespread, especially in rural areas. In 2021, the national poverty rate was estimated at 78.4 percent, and the extreme poverty rate was 71.5 percent. While about 6 in 10 urban population is poor, about 8 rural residents in 10 are poor. The endemic nature of poverty stems from a combination of historical systemic problems—persistent conflict, inadequate state capacity, and poor economic management—aggravated by extreme natural disasters (flooding, drought, and so on). South Sudan’s path to independence and the subsequent period have been marked by persistent armed conflict. The First and Second Sudanese Civil Wars (1950s-70s and 1990s-2005) culminated in the Comprehensive Peace Agreement (CPA) in 2005, leading to Southern Sudan’s secession and independence in 2011. However, internal and external conflicts persisted, leading to a civil war in 2013 due to disputes between political elites, resulting in 400,000 deaths and over 4 million displaced. The Revitalized Agreement on the Resolution of the Conflict 1 Although the 2022 HBS is nationally representative, covering all 10 states, it collected information on only 719 households. The sampling strategy of the 2022 HBS provides point estimates of critical indicators, particularly Photo: © Mayak Akuot / FAO poverty and inequality rates, with sufficient precision at the national level. However, because of the small sample size, the survey data are unsuitable for reliable analysis and estimation of poverty and inequality below the national level. Using imputation techniques, household expenditures are imputed into a more extensive survey, the 2021 Food Security and Nutrition Monitoring System–Plus (FSNMS+), to produce comprehensive poverty profiles and reliable state-level poverty estimates. Most of the analyses of the report are based on 2021 data. See FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 2 The methodological details are available from Adoho, Takamatsu, and Yoshida (2022). 13 in South Sudan (R-ARCSS) was signed in 2018 to end hostilities and prepare for national elections, but implementation has been slow. Violence persists at subnational levels, ethnic divisions deepen, and doubts grow about the commitment of signatories to implement the agreement. South Sudan’s turbulent path to statehood has resulted in a governance system where elite transactions overshadow formal institutions. Both the 2015 and 2023 Systematic Country Diagnostics have identified elite capture of the state as a key factor in the poor performance of South Sudan’s socioeconomic system. This situation has fostered adversarial and zero-sum politics, hindering the development of systems to equitably provide basic services. Consequently, the country is trapped in a cycle of fragility characterized by weak governance, limited livelihood opportunities, and repeated cycles of conflict and violence, which have imposed severe human, social, and economic costs. These challenges are further exacerbated by South Sudan’s extreme vulnerability to water-related risks linked to climate change and its limited capacity to cope with shocks and natural disasters. Climate shocks and recurrent disasters jeopardize the post–civil war recovery and undermine development efforts. Since independence in 2011, South Sudan has suffered severe droughts (2011 and 2015) and floods (2014, 2017, and 2019–22), resulting in high numbers of casualties, displacements, loss of livestock and other livelihood and development impacts. The global climate risk index ranks South Sudan among the 10 most at-risk countries (Eckstein, Künzel, and Schäfer 2021). Heavily reliant on rain-fed subsistence farming and pastoralism, rural communities are particularly affected by extreme weather events and climate-related disasters, which are expected to become more frequent and intense under a warming climate. The compounded effects of persistent cycles of conflict, violence, and disasters have imposed severe human, social, and economic costs on South Sudan. Many lives have been lost, and the population displacements within and outside the country have been massive. Close to two million South Sudanese remain internally displaced, and about the same number are refugees in neighboring countries. Since South Sudan’s independence in 2011, real gross domestic product (GDP) per capita has sharply declined, while the cost of living has continuously risen. In 2022, real GDP per capita was about a third of the value at independence in 2011. Real GDP per capita growth averaged −5.5 percent, contracting in six of the seven years between 2015 and 2022 (figure ES.1, panel a). The average consumer price was 132 times higher in 2022 than in 2011 and 60 times higher than in 2015. These dynamics resulted in widespread poverty and food insecurity. In 2022, around 8 South Sudanese in 10 were living in poverty, and 7 in 10 were living in extreme poverty (figure ES.1, panel b). Between 2016/17 and 2022, South Sudanese living conditions deteriorated, and a growing share of the population was living in poverty and extreme poverty.3 Over this time span, the average household consumption in South Sudan declined by 15 percent, and poverty incidence rose by 6.7 percentage points (from 74.1 percent to 80.8 percent). Household consumption declined at all levels of consumption, suggesting that the impoverishment of South Sudanese households was 3 The poverty and inequality dynamic analyses use the 2022 HBS and the 2016–17 wave 3 data of the High Frequency Survey (HFS). The 2022 HBS covers all 10 states of South Sudan, while the 2016 HFS-Wave 3 covers only seven states. The poverty and inequality trend analyses are based on data on the seven shared states and are not nationally representative. See HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/2914. Executive Summary 14 Figure ES.1. GDP consumer prices, and poverty a. GDP and average consumer prices, 2011–22 b. Poverty and food (extreme) poverty rates, 2021–22 4,000 20,000 80.2 80.5 78.4 73.5 73.0 71.5 3,000 3,014 15,000 13,971 58.9 2,000 1,698 10,000 49.0 1,000 234 5,000 1,103 0 0 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Gross domestic product per capita, Poverty incidence Extreme poverty rate constant prices In ation, average consumer prices Urban Rural Camp National Source: World Bank based on data of WEO (World Economic Source: World Bank estimates based on HBS 2022; FSNMS Outlook Database) October 2023 Edition (dashboard), (Food Security and Nutrition Monitoring System) (dashboard), International Monetary Fund, Washington, DC, https://www. Food Security Indicators Data, CLiMIS, Juba, South Sudan, imf.org/en/Publications/WEO/weodatabase/2023/October. https://climis-southsudan.org/fsi/data. widespread in 2016/17–22. Multidimensional poverty displayed similar trends in the period, and it now affects nearly the entire population (93 percent). Despite the pervasive incidence of poverty, consumption inequality is substantial. In 2021, the mean per capita consumption expenditure was 8.4 times larger among the 90th percentile than the 10th percentile. The wealthiest 20 percent of the consumption distribution accounts for 50.2 percent of total consumption, while the bottom 20 percent accounts for 4.8 percent of total consumption. Altogether, the poor represent almost 80 percent of the population, but accumulates less than 50 percent of total consumption. Poverty is ubiquitous across all states, though the average poverty rate hides substantial disparities across counties (map ES.1). Even in the states with the lowest poverty rates, such as Central Equatoria and Western Bahr el Ghazal, over 70 percent of the population are living under the national poverty line. The poverty rate is even higher in Jonglei, Lakes, Upper Nile, and Warrap, and, in Northern Bahr el Ghazal, the poverty rate exceeds 80 percent. Between Magwi County, which has the lowest incidence of poverty (52 percent), and Fangak County, which has the highest poverty rate (94.8 percent), there is a difference of almost 42 percentage points. The landlocked counties in the center of the country and in the southeast display considerably higher poverty rates. Poverty incidence in Juba and the southern and western counties along the borders with the Central African Republic, the Democratic Republic of Congo, and Uganda ranks among the lowest in the country. Accounting for population density, the states of Jonglei, Northern Bahr el Ghazal, and Warrap are home to more than 40 percent of all South Sudan’s poor. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 15 Map ES.1. Poverty, extreme poverty, and the distribution of the poor Poverty rates by country Distribution of the poor by county Food (extreme) poverty rates by county Distribution of the Food (extreme) poor by county Moderately Food Insecure-LHZ (in percent) Severely Food Insecure-LHZ (in percent) Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Executive Summary 16 Food insecurity has worsened in the past decade and affected nearly three-quarters of South Sudanese in 2022. More than half (53 percent) of South Sudanese fell into the category of moderate food insecurity, and an additional 20 percent are severely food insecure. This corresponds to a 25 percentage point increase in moderate food insecurity (from 28 percent in 2010) and a 10 percentage point rise in severe food insecurity (from 10 percent). Significant disparities in food security are evident by area of residence and livelihood zone. Approximately 75 percent of rural residents and 67 percent of people living in camps experience moderate to severe food insecurity compared with 52 percent among urban residents. The southeastern semiarid pastoral livelihood zone (SS05), the eastern plains sorghum and cattle zone (SS06), and the northeastern maize, cattle, and fishing zone (SS10) exhibit high levels of severe food insecurity, which affects more than 3 residents in 10 compared with 1 or 2 in 10 in other livelihood zones. These zones are characterized by semiarid or arid climates, meager rainfall, and prolonged dry seasons, thereby posing obstacles to crop cultivation and livestock grazing. Limited agricultural productivity, reliance on livestock, restricted market access, susceptibility to shocks and conflicts, instability, and the recurrence of flooding, livestock disease, crop pests, and periodic drought contribute to the food insecurity in these zones (FEWS NET, 2018). The Equatorial maize and cassava zone (SS01) and the Nile Basin fishing and agropastoral zone (SS08) also experience notably high levels of moderate food insecurity, with approximately 6 residents in 10 affected. Low food production is a key driver of food insecurity. Except for vegetables, less than half of rural households produce their own food. About 4 in 10 rural households acquire essential food items like milk and dairy products, cereals, roots, tubers, and legumes from the market. Urban households exhibit a high reliance on market purchases for their food, with 76 to 95 percent of them purchasing food from markets, depending on the food category. In camp settings, food assistance is the main source of staple food, but reliance on the market for food (meat, milk, and vegetables) acquisition is concerning. Long distances and limited available public transportation represent additional significant challenges to food security in South Sudan. Approximately half of South Sudanese households across all areas of residence report that markets are distant or that their transportation options are limited. For most rural residents, accessing the nearest market often requires a commute time of more than an hour, roughly equivalent to 5 kilometers. Shocks are pervasive in this environment of endemic poverty and food insecurity. Most South Sudanese households (70 percent) had been affected by at least one shock during the six months previous to the 2021 FSNMS+ survey. Major shocks include flood, drought, hyperinflation, violence, and conflict. Rising temperature, floods, and droughts are bound to become more frequent and devastating. Exposure to shocks varies by household characteristics, such as location of residence, state, and level of consumption. Weather-related covariate shocks are more frequent in rural areas than in urban areas. In these areas, poor and nonpoor households face distinct sets of shocks although high and rising fuel prices are the dominant shock. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 17 Household vulnerability to poverty is universal and structural rather than deriving from consumption or revenue volatility. Because permanent income prospects are low, 99.7 percent of South Sudan’s population is structurally vulnerable to poverty. At the national level, poverty- induced, structural vulnerability is estimated at 96.5 percent, while transitory, risk-induced vulnerability is estimated at only 3.2 percent. At a poverty-induced vulnerability rate of 99.7 percent, almost all rural residents are structurally poor, while risk-induced vulnerability is important in urban areas (33.2 percent). With respect to climate-related shocks and conflicts, South Sudan presents a high-risk profile based on a few factors. First, located in the fertile Nile Basin, the country enjoys a hot tropical climate with cyclical heavy rains, which exposes it to frequent flooding and drought. Recent weather shocks have disrupted economic activities, especially in the agriculture sector, which is heavily dependent on rainfall. Historically, droughts and floods have had devastating effects on crop yields, with important implications for livelihoods and food insecurity. Weather shocks have contributed to infrastructure destruction and represent an additional burden in public health, including through premature deaths. The vulnerability to climate shocks is compounded by the fact that the country is ill-prepared to weather such shocks. The adaptive bandwidth is thin. Shocks also often critically depress household consumption and constrain the ability of households to obtain income or food. Many South Sudanese face significant welfare stress because of exposure to frequent, multiple shocks. For example, exposure to the flooding in 2021 led to a decline in household consumption of 6 percent and 13 percent in rural and urban areas, respectively.4 At the national level, the impact of flooding on household consumption averaged a 4 percent decline. While the effect on household consumption is statistically significant, it is not sufficiently large to affect poverty status. Flooding has also led to a rise in food insecurity in rural areas (figure ES.2, panel a). The shock had a larger impact on agricultural households than on their counterparts whose main income source is not agriculture. Compared with agricultural households, nonagricultural households experienced a smaller decrease in aggregate consumption, and the poverty impact of the flood shock was more acute among agricultural households. This highlights the fact that nonagricultural households are relatively more resilient to flood shocks compared with agricultural households (figure ES.2, panel b). 4 Chapter 3 presents more detail on the estimates. Executive Summary 18 Figure ES.2. Impact of floods on households a. Impact on consumption and food insecurity, by b. Differential welfare impact, agricultural and location nonagricultural households Urban Food insecurity Rural Poverty National Log-consumption -0.30 -0.20 -0.10 0.00 0.10 Food insecurity Log-consumption -0.20 -0.10 0.00 0.10 0.20 0.30 Note: The reference group is households in non–flood prone Note: The reference group is agricultural households in non– regions. flood prone regions Sources: Estimates based on HBS 2022; HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/2914, and administrative data. Conflict has raised food insecurity substantially in South Sudan. On average, conflict increased food insecurity by 6.5 percent–9.7 percent. Violent events that resulted in high casualties increased food insecurity by 6.5 percent, while violent events alone worsened food insecurity by 8.2 percent. Taken together, these two sets of violent events raised food insecurity by 9.7 percent (figure ES.3). Figure ES.3. Impact of conflict on food insecurity Fatalities Violent events All events 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 Sources: Estimates based on HBS 2022; HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/2914, and administrative data. Frequent exposure to adverse shocks and hardship often had self-reinforcing detrimental effects on household welfare. To cope with adverse shocks, households often relied on responses rooted in social norms and reciprocity rules that provided implicit assurance mechanisms. These behaviors included cash or in-kind interhousehold transfers. Some households resorted to coping strategies that depleted productive capabilities. But many households were running out of coping options because of exposure to frequent and multiple shocks. Household resilience was weakened by the fact that typical coping mechanisms only provided limited protection, especially against community-wide shocks. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 19 The government and people of South Sudan had to grapple with escalating needs in assistance and pervasive vulnerabilities in a context in which social protection interventions were almost exclusively led by donor resources. Social protection often involved fragmented, unpredictable interventions that were often not guided according to the priorities of the National Social Protection Policy Framework. Ongoing efforts to strengthen resilience and improve food security include interventions, such as conditional or unconditional cash transfers, food for assets, microfinance, food and nutrition assistance, livelihood opportunities, and improved agricultural inputs or equipment (that is, quality seeds and tools). Yet, poor coordination among the actors in social protection generates inefficiencies and compromises the sustainability of interventions. There is a clear need to improve mechanisms to identify social protection beneficiaries through data-driven approaches, such as proxy means testing. Executive Summary 20 Policy Considerations This Poverty Assessment has confirmed that poverty is endemic and food insecurity dire in South Sudan despite the country’s abundant wealth in natural resources. The report finds widespread and extreme poverty that stems from a combination of historical and systemic problems, including persistent conflicts and violence, inadequate state capacity to deliver essential services, and poor governance and economic management aggravated by extreme and recurrent natural disasters. The assessment makes clear that household strategies to cope with or guard against risk often exacerbate vulnerability. The capacity of most South Sudanese to withstand future shocks is severely limited or entirely depleted. The prevailing socioeconomic system has consistently eroded the living standards of the South Sudanese. It is therefore critical to change course and do things differently to reduce poverty and build resilience. Addressing Conflict and Inadequate State Capacity to Improve Livelihoods The report concludes that addressing the drivers of fragility, conflict, and violence and inadequate state capacity is critical for improving the livelihoods of conflict-affected people. As highlighted by the 2023 Systematic Country Diagnostic Update, the root causes of insufficient development progress are largely internal factors that can be grouped into two clusters. The first cluster includes the underlying causes of persistent subnational conflict and violence. The second is the persistent mismanagement and misappropriation of South Sudan’s abundant natural capital, most notably, oil. The combination of these two factors is the driving force behind underinvestment in natural, human, and physical capital in South Sudan. It has fueled extreme dependence on aid, an artificial scarcity of resources to finance the delivery of basic services and to maintain government functions, competition over rent extraction, and a broken social contract (World Bank 2024). Promoting justice, security, and enhancing governance across economic sectors are therefore essential to escaping the fragility trap. This will also create the conditions for the South Sudanese population to rebuild and strengthen their livelihoods and for the enhancement of the provision of essential service to the population. Invest in Human Capital Development The inadequate investment in health and education sectors has critically impeded the growth of human capital in South Sudan. Limited access to education and health services in South Sudan significantly hampers human capital development, as evidenced by the country’s Human Capital Index (HCI) score of 0.31, one of the lowest HCIs in the world. This means a child born today will only achieve 31% of their potential productivity as an adult. Poor human capital outcomes currently constrain the country’s productivity and economic potential. To develop and protect human capital in South Sudan, policymakers must improve health, nutrition, and education outcomes. Health The country faces severe health challenges, including the highest neonatal and maternal mortality rates globally, widespread malnutrition, and limited access to health facilities. Poor water, sanitation, and hygiene (WASH) services further constrain human capital, with only 10 percent of households having access to sanitation and over 60 percent using unimproved water REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 21 sources. Many schools lack drinking water and sanitation facilities, negatively impacting learning outcomes. To address the critical health challenges in South Sudan, it is recommended that the government significantly increase its budget allocation for health to meet the 15 percent target pledged by African Union countries. This funding should be directed towards improving health infrastructure, ensuring regular supply of medical essentials, and training more health workers to meet WHO standards. Additionally, expanding water and sanitation services, particularly in rural areas, and promoting climate-resilient solutions are essential. Education Learning outcomes in South Sudan are extremely poor, with 62 percent of primary school-aged children out of school and high illiteracy rates. The education system struggles with inefficiency, high dropout rates, untrained volunteer teachers, low and irregular salaries that deter qualified individuals from teaching, and a severe shortage of teaching and learning materials. Schools are unevenly distributed, with flooding further exacerbating access related challenges. Funding of the sector is inadequate and remains one of the biggest drawbacks to building a viable human capital. The National General Education Policy, aligned with Vision 2040, aims to provide quality education for all by eradicating illiteracy, promoting lifelong learning, gender equity, personal development, national unity, and improving education quality through robust inspection programs. To address the severe educational challenges in South Sudan, it is recommended that the government increase the budget allocation for education to meet the legal requirement of at least 10 percent, while gradually increasing to meet international education financing benchmarks. This increased funding should be directed towards improving teacher salaries and ensuring regular payments to attract and retain qualified educators. Additionally, investment in teacher training programs is crucial to reduce reliance on untrained volunteers. Efforts should also be made to ensure equitable distribution of schools and adequate provision of teaching and learning materials including textbooks for the new competency-based curriculum, particularly in secondary and upper primary classes. Implementing robust inspection programs and focusing on foundational skills like literacy and numeracy will further enhance the quality of education, aligning with the goals of the National General Education Policy and Vision 2040. Improve Food Security This report underlines the dire deterioration in food insecurity in South Sudan. Prioritizing the fight against food insecurity is a key priority. Measures to ensure the stability and security of households are prerequisites for promoting an agriculture capable of feeding rural populations. Investments in road infrastructure would facilitate market integration, connectivity between rural areas and towns, and the delivery of food products from the countryside to the cities, and this would reduce dependence on imports. Building Resilience to Climate Shocks and Disasters The report shows that climate shocks and disasters have had an aggravating effect on the already alarming deprivation among the population of South Sudan. South Sudan is highly Executive Summary 22 vulnerable to climate change and faces significant water-related risks such as droughts and floods, impacting food security and livelihoods. Effective interventions must consider the type of shocks and livelihood sources and require an improved understanding of the disaster risk dynamics, including (a) the changing recurrence and intensity of climate shocks, (ii) the migration, displacement and settlements patterns underlying the evolving exposure landscape, and (iii) the social and physical determinants that define the different levels of vulnerability to climate shocks and disasters. Building resilience to climate shocks is a cross-cutting policy agenda, including water and flood management, agriculture, health, disaster risk management, and land use planning, among others. Related interventions include investments in flood protection, water storage, climate-smart agriculture, and improvements in early warning and hydro-meteorological services, and emergency preparedness and response. At the household level, the ability to adequately prepare for and respond to climate shocks is influenced by access to resources and social support, with household assets being crucial for escaping poverty. Social protection, largely driven by humanitarian efforts, is thus essential for coping with shocks. Adaptive safety nets and index-based insurance can help build resilience and support poverty alleviation through cash or in-kind transfers, addressing credit, savings, and liquidity constraints for the most vulnerable households. Invest in Data and Statistical Capacity to Narrow Large Data and Knowledge Gaps Filling the data gap is essential to reducing the knowledge gap. However, South Sudan’s statistical system is weak and far from meeting the needs of the users of statistical data. Strengthening statistical capacity through institutional development and data collection is key to building the minimum statistical infrastructure needed for economic monitoring and decision-making. No budget has been allocated to the National Bureau of Statistics for statistical operations since 2013. Budget allocations have been approved to cover salaries and operations at the institution, but these have also been delayed. The National Bureau of Statistics lacks qualified staff to support data production. The accumulation of salary arrears affects the commitment of staff to routine work at the bureau, driving the high turnover among the uniquely qualified technical staff. The National Bureau of Statistics, the core institution of South Sudan’s statistical system, also lacks minimum physical infrastructure, equipment, and healthy working conditions. Some offices are infested with termites and falling apart. Few staff members have proper desks and chairs, and meeting rooms are not maintained. These poor working conditions, combined with the low salaries and the nonpayment of salaries, limit the productivity and motivation of staff. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 23 INTRODUCTION I.1. COUNTRY CONTEXT Fragility, conflict, and violence have been a touchstone of South Sudanese life since well before the country’s independence. The southern Sudanese population fought against marginalization and for greater political, economic, and social autonomy for four decades before independence in 2011. Following independence, disagreements among political elites in South Sudan led to a full-scale civil war. This conflict ended in 2018 with the signature of a peace agreement. The agreement ushered in a transitional period that was supposed to end in 2022. Although security has improved recently, violence remains elevated and widespread. Some areas display a high risk of open conflict and persistently high levels of violence. Protracted armed conflict ensnared South Sudan in a fragility trap that fosters institutional failure and poor governance. Pervasive elite capture has created a fractious, self-serving political leadership that tends to privatize public institutions and resources. Weak political institutions have become both the cause and the consequence of poor governance and poor economic outcomes. The government and people of South Sudan are still struggling to achieve progress against corruption and lack of accountability and to align the political elite’s incentives more closely with development priorities. South Sudan’s vulnerability to climate change and natural disasters undermines development efforts. Since independence in 2011, the country has suffered severe droughts (2011, 2015) and floods (2014, 2017, 2019–22), resulting in high numbers of injuries and deaths, displacements, and the loss of livestock, severely impacting people’s livelihoods. The global climate risk index ranked South Sudan among the 10 most affected countries in 2019 because of the severe flooding and heavy rainfall that year, as well as the intense bushfires (Eckstein, Künzel, and Schäfer 2021). Weather data suggest that weather shocks will become more severe in years to come. Historical data show that average temperatures have increased significantly over the last century (figure I.1). The southern states of Central Equatoria and Eastern Equatoria and the Western States of Northern Bahr el Ghazal and Western Bahr el Ghazal have registered the largest temperature increases. Meanwhile, the central states of Lakes and Western Equatoria have experienced the lowest temperature Photo: © Mayak Akuot / FAO increases. Weather conditions are also becoming more volatile and larger in scale.5 The country has been exposed to more episodes of floods and droughts in the last decade than in the six previous decades combined (figure I.2). 5 The downward trend in the standardized precipitation-evapotranspiration index demonstrates that weather conditions have become drier in South Sudan. Introduction 24 Figure I.1. Mean temperature, 1901–2021 Figure I.2. Standardized precipitation-evapo- transpiration index, 1950–2022 30.00 2.5 2.0 29.00 1.5 28.00 1.0 0.5 27.00 0.0 26.00 -0.5 25.00 -1.0 -1.5 24.00 -2.0 1901 1910 1928 1937 1946 1955 1964 1973 1982 1991 2000 2009 2018 1919 -2.5 1950 1955 1960 1965 1970 1975 1979 1984 1989 1994 1999 2004 2008 2013 2018 Annual Mean 5-yr average Sources: World Bank calculations; data of CCKP (Climate Sources: World Bank calculations; data of CCKP (Climate Change Knowledge Portal), World Bank, Washington, DC, Change Knowledge Portal), World Bank, Washington, DC, https://climateknowledgeportal.worldbank.org. https://climateknowledgeportal.worldbank.org. The legacy of five decades of recurring conflict and violence and the recent historical flooding and naturals disasters have imposed severe human, social, and economic costs. In addition to the loss of lives, conflict has triggered significant displacements. It is estimated that close to two million people remain internally displaced, and about the same number are refugees in neighboring countries. Displacement entails the disruption of livelihoods. Prolonged conflict has destroyed physical infrastructure (schools, health centers), and the poor governance structure has contributed to a macroeconomic crisis that is reflected in widening fiscal deficits, distortions in the foreign exchange market, and high, persistent inflation. The economy is unstable and exhibits significant vulnerabilities. The economy has been contracting since 2015. Growth in real gross domestic product (GDP) per capita averaged -5.5 percent in 2015–22, contracting in six of the seven years (figure I.3). Since independence in 2011, real GDP per capita has severely declined, while the cost of living has risen steadily. In 2022, real GDP per capita was about a third of the value at independence, while average consumer prices were 132 times higher than the 2011 value. These dynamics point to a decline in household welfare. The unstable economic performance since 2011 results mainly from poor governance, flareups of violence and conflict, weather-related shocks, including flooding and drought, and the COVID-19 pandemic. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 25 Figure I.3. GDP per capita and average consumer prices, 2011–22 3,500 20,000 3,000 3,014 2,500 15,000 13,971 2,000 1,698 10,000 1,500 1,000 5,000 1,103 500 234 0 0 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Gross domestic product per capita, constant prices In ation, average consumer prices Source: World Bank using WEO (World Economic Outlook Database) October 2023 Edition (dashboard), International Monetary Fund, Washington, DC, https://www.imf.org/en/Publications/WEO/weo-database/2023/October. The economy of South Sudan is heavily dependent on the oil sector, which is beset by governance challenges. The oil sector accounts for more than 60 percent of the economy’s GDP, nearly all exports, and 95 percent of government revenues. However, the potential of the sector to support economic recovery and resilience is severely constrained by governance challenges stemming from the opacity and lack of accountability in the sector. These governance issues, which include off-budget revenue and expenditure practices, have facilitated the misappropriation of oil revenue. Only limited oil revenue flows into the budget.6 A substantial share of oil revenues are absorbed by compensation agreements, the repayment of oil-backed loans, oil-backed public investments, and transfers to Sudan.7 Almost half of all budget allocations are directed toward public administration, infrastructure, security, and rule of law, leaving few resources for service delivery and building human capital through effective interventions in health care, nutrition, agricultural services, and education or for investing in institutional strengthening and local capacity building on which sustainable development and durable peace depend (World Bank 2023). I.2. DATA CHALLENGES AND SOLUTIONS IN POVERTY MEASUREMENT South Sudan is a data-scarce environment, in which the production of nationally representative microdata is infrequent and poorly harmonized. This poverty assessment draws data mainly from three household surveys, namely, the Household Budget Survey (HBS), 2022, wave 3 of a series of high frequency surveys (HFS), 2016/17, and Food Security and Nutrition Monitoring System-Plus (FSNMS+), 2021 (see annex A, section A.1). The 2022 HBS is the first and most recent nationally representative household consumption survey since independence. It covered all 10 states, but compiled information on only 719 households. The sampling strategy 6 The Treasury took in an estimated 8 percent of GDP in oil revenue in fiscal year 2022 (a third of gross oil revenue that year) after debt servicing, oil-for-roads program spending, and transfers to Sudan and the Petroleum Ministry. 7 Although the government completed its obligations under the transitional financial arrangement in February 2022, the transfers to Sudan—amounting to about 3.5 percent of GDP in fiscal year 2022—have continued. Introduction 26 supplies point estimates of crucial indicators with sufficient precision at the national level, especially poverty and inequality rates. However, because of the small sample size, the data are unsuitable for reliable poverty and inequality analysis below the national level. The analysis in this poverty assessment used imputation techniques to estimate household expenditures into a more extensive survey, the 2021 FSNMS+, to produce comprehensive poverty profiles and reliable poverty estimates for each state (see annex A, section A.2).8 The FSNMS+ is a nationally representative household survey implemented in 2021 among around 19,000 households. It gathered a wide variety of household information and was designed to be representative at the county level. The FSNMS+ survey offers only limited options in topics covered by a traditional poverty assessment. It does not collect information on individuals. Hence, it does not allow employment, labor market analysis, educational attainment, human capital, intrahousehold inequality, inequality of opportunity, income mobility, or disaggregated gender and poverty analysis to be carried out across individuals. This limits the coverage of the poverty assessment. Nonetheless, the 2021 FSNMS+ and the 2022 HBS offer other options for analysis, such as the calculation of reliable point estimates of poverty and inequality across diverse geographical areas. The 2022 HBS poverty estimates show that the share of households with annual per capita consumption expenditure below the poverty line of SSP358,724 was 75.9 percent, and 67.3 percent of the population were living in extreme poverty measured using the food poverty line of SSP298,478 a year. The methodological details are available in Adoho, Takamatsu, and Yoshida (2022). (Chapter 1 summarizes the findings of the imputations and the 2021 poverty estimates.) Prior to the 2022 HBS, consumption data with complete geographic coverage of South Sudan were also not available. Available datasets came from four waves of high frequency phone surveys implemented between 2015 and 2017. The latest official poverty statistics on South Sudan are drawn from wave 3 of the HFS (implemented between 2016 and 2017), covering 7 of the 10 states. Differences in survey design pose a challenge in any rigorous analysis of the dynamics of poverty and inequality. The consumption modules differ in the 2022 HBS and the HFS wave 3 at least on the following points: (1) All households in HFS wave 3 skip one noncore module to save interview time, and household consumption expenditure for the missing noncore module are imputed. The HBS collected data on all consumption items in all households. (2) The HFS recall period is fixed at seven days for food items, while the recall period for purchased goods in the HBS may differ by item and household. (3) HFS wave 3 collected data from January to February 2017, while the HBS collected data from April to June 2022. (4) Daily calorie intake per capita cannot be estimated from the HFS, but can be estimated from the HBS. To restore the comparability of the poverty statistics in the HBS and HFS, the Survey of Well-Being via Instant and Frequent Tracking (SWIFT) 2.0 was adopted (see annex A, section A.2). 8 This is the methodology of the Survey of Well-Being via Instant and Frequent Tracking (SWIFT) (Yoshida et al. 2022). See also FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https:// climis-southsudan.org/fsi/data. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 27 This Poverty Assessment is organized into four chapters. Chapter 1 presents the poverty and inequality profile and the spatial dimensions of poverty and explores the drivers of poverty. Chapter 2 assesses the extent and evolution of food security, its spatial patterns, and drivers. Chapter 3 analyzes household vulnerability to shocks and the impact of climate shocks and conflicts on household welfare. It ends with an assessment of household shock coping mechanisms. Chapter 4 concludes the report with recommendations to make poverty reduction strategies more effective in addressing the problems and needs of the poor and vulnerable. Introduction 28 CHAPTER 1. POVERTY AND INEQUALITY: PROFILE AND TRENDS Photo: © Mayak Akuot / FAO 29 Monetary poverty is endemic in South Sudan. The latest poverty estimates indicate that poverty incidence is significant, and the depth and severity of poverty are alarming, particularly in rural areas. Poverty is pervasive across all states. Landlocked counties in the center and southeast of the country display considerably higher poverty rates. Multidimensional poverty is quasi-universal. Vulnerability to poverty is widespread, structural, and less closely tied to the volatility of consumption or revenue. Over 2016/17– 22, living conditions deteriorated, and a growing share of the population was living in poverty or extreme poverty. Poverty Incidence According to the 2022 HBS poverty estimates, 75.9 percent of the population was living in households with annual per capita consumption expenditure below the poverty line of SSP358,724, and 67.3 percent of the population was living in extreme poverty, that is, below the food poverty line of SSP298,478 a year (table 1.1).9 Table 1.1. Poverty estimates and imputation results, 2021–22 percent 95% confidence Interval National Lower bound Upper bound Poverty incidence 2022 75.9 70.0 81.8 2021 78.4 72.7 84.2 Extreme poverty rate 2022 67.3 60.9 73.7 2021 71.5 65.2 77.7 Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis- southsudan.org/fsi/data. The national poverty and extreme poverty rates stood, respectively, at 78.4 percent and 71.5 percent in 2021. While these point estimates differ between the 2021 and 2022 surveys, they all fall within the 95 percent confidence interval of the 2022 HBS estimates, suggesting that the imputation results are robust. There is thus no statistically significant difference between the 2021 and the 2022 poverty rates. Because the 2022 HBS only collected information on 719 households and is therefore unsuitable for drawing conclusions about poverty and inequality below the national level, the remainder of the report is based 9 See Adoho, Takamatsu, and Yoshida (2022) for the methodological details. Chapter 1. Poverty and Inequality: Profile and Trends 30 on the 2021 FSNMS+, a nationally representative survey of approximately 19,000 households. This facilitates more in-depth subnational analyses (see annex A, section A.3 for methodology and estimations). 1.1. POVERTY AND INEQUALITY PROFILE 1.1.1. POVERTY PROFILE Estimates from simulations based on the 2021 FSNMS+ indicate that poverty is widespread, particularly in rural areas. In 2021, the national poverty rate was estimated at 78.4 percent and the extreme poverty rate was 71.5 percent. While roughly 6 urban dwellers in 10 are poor (58.9 percent), about 8 rural residents in 10 are poor (figure 1.1, panel a). Extreme poverty affected nearly 50.0 percent and 73.5 percent of the urban and rural populations, respectively. Extreme poverty rates are also higher in camps and in rural areas than in urban areas. Figure 1.1. Poverty rates, the poverty gap, and the squared poverty gap, 2021–22 a. Poverty and extreme poverty rates, 2021 b. The depth and severity of poverty, 2021 80.2 80.5 78.4 44.6 73.5 73.0 71.5 41.3 42.9 58.9 49.0 29.4 28.0 25.1 25.2 13.8 Poverty incidence Extreme poverty rate Poverty gap Squared poverty gap Urban Rural Camp National Urban Rural Camp National Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. The depth and severity of poverty were staggering. The poverty gap was 42.9 percent, suggesting that the average consumption of the poor is about 57 percent the national poverty line.10 Poverty was less deep in urban areas. The average consumption of the urban poor was three-quarters (74.9 percent) of the national poverty line compared with 55.4 percent among the poor in rural areas,and about 59 percent in camps (figure 1.1, panel b). The severity of poverty, which measures inequality among the poor, was 28.0 percent.11 The poverty in rural areas and in the camps was much more severe than the poverty in urban areas. The square of the poverty gap in urban areas, rural areas, and the camps was, respectively, 13.8 percent, 29.4 percent, and 25.2 percent (figure 1.1, panel b). 1.1.2. CONSUMPTION INEQUALITY 10 The poverty gap is the ratio by which the mean income or consumption of the poor falls below the poverty line. It is expressed as a percentage of the poverty line and measures the depth of poverty among a population. 11 The severity of poverty, or the average value of the square of the depth of poverty (the poverty gap), measures inequality among the poor, that is, the extent to which the poor are not all equally poor. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 31 Although a large majority of the population is poor, consumption inequality is still substantial. The Gini coefficient is 0.45 at the national level and in rural areas; it remains at 0.40 in urban areas; and it is 0.36 in the camps (figure 1.2). This is relatively high compared with 0.354 in Sub-Saharan Africa. The p90/p10 index of 8.4 at the national level indicates that the average consumption of the 90th percentile of the population consumption or welfare distribution was 8.4 times larger than the corresponding average for the 10th percentile. Inequality measured by the p90/p10 index is highest for rural areas. Figure 1.2. Inequality measures, 2021 10.0 0.45 0.45 0.50 0.40 8.3 8.4 8.0 0.36 0.40 6.6 Gini Coe cient 6.0 5.3 0.30 Ratios 4.0 2.7 3.0 2.9 3.0 3.0 0.20 2.4 2.3 2.6 2.0 0.10 0.4 0.4 0.4 0.4 0.0 0.00 Camp Urban Rural National p90/p10 p75/p25 p90/p50 p10/p50 Gini Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Note: p = consumption percentile. There is also a substantial consumption gap between the wealthiest and the poorest 20 percent of the consumption distribution (figure 1.3). The wealthiest 20 percent accumulates 50.2 percent of total consumption, while the poorest 20 percent accumulates 4.8 percent. Figure 1.3. Consumption share, by quintile and location 45.6 42.8 50.2 50.2 Poorest Quintile Quintile 2 22.9 22.8 Quintile 3 21.8 21.9 Quintile 4 15.4 16.1 14.0 14.0 Richest Quintile 10.4 9.1 11.5 9.1 5.7 4.8 6.8 4.8 Urban Rural Camp National Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Chapter 1. Poverty and Inequality: Profile and Trends 32 Women face serious disadvantages on several socioeconomic outcomes in South Sudan. Restrictive gender and social norms limit the choices and opportunities available to women and girls in South Sudan. In particular, there are strong cultural biases against girls’ education across all states. School attendance is much lower for girls than for boys, particularly in rural South Sudan. It is estimated that that almost three-quarters of primary school-aged girls in South Sudan do not receive a primary level education. Only 29 percent of adult females were believed to be literate in 2018 (compared to 40 percent for men). Women bear a disproportionately heavy burden of familial responsibility and are less likely than men to be employed as waged workers. The country ranks in the bottom third for women’s empowerment (World Bank, 2023). Widespread gender-based violence (GBV), such as domestic violence, sexual violence, and early marriage, marginalizes and disempowers women. Gender equality matters a great deal for the achievement of development outcomes. It is intrinsically valuable and therefore should be considered a legitimate development objective. Indeed, the 5th Sustainable Development Goal (SDG 5) calls for the achievement of gender equality and the empowerment of all women and girls. Furthermore, gender equality has an instrumental value to the extent that it enhances productivity, improves development outcomes for the next generation and makes institutions more representative. There are indeed productivity gains to be obtained when women’s skills and talents are developed and used fully. There is also evidence that when women control household resources, the resulting patterns of spending benefit children. Finally, empowering women economically, politically and socially can lead to better policy choices and make institutions more representative. 1.1.3. SOCIOECONOMIC CHARACTERISTICS OF THE POOR Educational outcomes are dismal, and low educational attainment is associated with poverty The outcomes in education are poor: nearly 70 percent of the population has never attended school. The gap between urban and rural areas is large. Among rural residents, 72.3 percent have never attended school, compared with 39.5 percent of urban dwellers and 53.0 percent in the camps (figure 1.4, panel a). Among individuals who have attended school, only 6.9 percent have attained at least secondary education. Individuals with low educational attainment or no education are more likely to be poor. Poverty rates gradually decline with level of education. Lower poverty rates are typically associated with households in which the head has higher educational attainment (figure 1.4, panel b). The probability of being poor is greatest among individuals living in households in which the head has no education (83 percent) and lowest among individuals living in households in which the head has attended university (42 percent). About 65 percent of the heads of nonpoor urban households completed primary education, whereas this is true of only 36 percent of the heads of poor urban households. Agriculture is the leading employer, but farmers are more likely to be poor Agriculture is the most important source of income. Around 71 percent of households rely on agriculture as the main source of income, and nearly 10 percent of households receive social and humanitarian support. The remaining 14.4 percent are in commerce, and 4.8 percent are wage employees. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 33 Figure 1.4. Educational attainment, poverty, and location a. Educational attainment, by location b. Poverty rate, by education of household head 0.6 1.4 82.8 7.7 5.4 5.6 6.9 11.4 74.1 21.5 21.6 22.6 30 56.2 31.3 42.2 72.3 69.1 53 39.5 Urban Rural Camp National No education Primary Education No Primary Secondary University Secondary Education University education Education Education Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Poverty incidence is highest among agricultural households (81 percent) and lowest among wage earners (62.8 percent). Moreover, 73.5 percent of poor individuals rely on agriculture as the main source of income compared with 62.8 percent of the nonpoor (figure 1.5, panel a). The share of wage workers is small among nonpoor households (8.3 percent), while wage workers account for only 3.9 percent of poor households. Figure 1.5. Households, by poverty and sources of livelihood a. Household poverty status, by livelihood b. Household livelihood, by poverty rate 8.4 81 77.8 14.2 3.9 8.3 14.2 68.3 14.8 62.8 73.5 62.8 Non Poor Poor Agriculture Commerce Agriculture Commerce Wage (Social) Wage worker (Social) Support worker Support Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Chapter 1. Poverty and Inequality: Profile and Trends 34 1.1.4. MULTIDIMENSIONAL POVERTY Most South Sudanese grapple with many types of deprivation in education, access to essential services, asset ownership, and housing quality. Indeed, multidimensional poverty, which accounts for deprivations along these dimensions, is quasi-universal in South Sudan (92.6 percent).12 The analysis finds that multidimensional poverty stood at 95.3 percent in rural areas, 31 percentage point higher than in urban areas (table 1.2). (See annex B for a description of the methodology description). Table 1.2. Multidimensional poverty index, by area Type National Urban Rural Camp Adjusted headcount ratio, M0 0.794 0.443 0.83 0.707 Standard error, M0 0.001 0.002 0.001 0.002 Incidence, H (%) 92.6 63.9 95.3 92.3 Intensity, A (%) 85.8 69.3 87.0 76.5 Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. None of the four dimensions of deprivation clearly dominates in determining multidimensional poverty in South Sudan. At the national level, access to public goods and amenities, asset ownership, and housing quality contribute over a quarter each (27.0 percent) to multidimensional poverty. Monetary poverty and education follow, at 24.2 percent and 22.0 percent each (table 1.3). Efforts to reduce multidimensional poverty require acting simultaneously on several fronts. Table 1.3. Contribution of dimensions to the multidimensional poverty index, by area Indicators National Urban Rural Camp Monetary poverty 24.2 27.4 23.9 29.8 Education 22.1 19.6 22.2 22.3 Public assets 26.8 27.1 27 15.9 Private assets 26.9 25.9 26.9 32.1 Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 1.2. SPATIAL DIMENSION OF POVERTY Poverty is ubiquitous across all states. Even in the states with the lowest poverty rates, such as Central Equatoria and Western Bahr el Ghazal, more than 70 percent of the population is living under the national poverty line (map 1.1). However, in Jonglei, Lakes, Northern Bahr el Ghazal, Upper Nile, and Warrap, the poverty rates were close to or above 80 percent. Jonglei, Northern Bahr el Ghazal, and Warrap are also three of the largest states in the country and, altogether, account for more than 40 percent of the total poor. 12 The analysis is based on the multidimensional poverty index developed by Alkire and Foster (2011). It consists of a composite index constructed by weighing several poverty indicators characterizing a range of deprivation in four dimensions, namely, monetary poverty, health, education, and living conditions. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 35 Map 1.1. Poverty rates and the distribution of the poor, by state a. Poverty rates, by state b. Distribution of the poor, by state, percent Source: World Bank estimates based on HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. The average poverty incidence rate across the states hides substantial disparities among the counties. The incidence of poverty varied from 52.0 percent in Magwi County to 94.8 percent in Fangak County. Map 1.2 shows that the landlocked counties (highlighted in dark red) in the center of the country and in the southeast display considerable poverty rates. The poverty rates in Juba (68 percent) and in the southern and western counties along the border with the Central African Republic, the Democratic Republic of Congo, and Uganda rank among the lowest in the country. However, taking the underlying populations into consideration reveals that the greatest density of the poor is in Juba, Malakal, the southern counties, such as Magwi and Yei, the northern counties toward the border with Sudan. and the southeastern counties. Poverty and extreme poverty display similar geographic patterns across (maps 1.2 and 1.3). Counties with high (low) poverty incidence also record high (low) extreme poverty. Map 1.2. Poverty rate and the distribution of the poor, by county a. Poverty rate, by county b. Distribution of the poor, by county, percent Chapter 1. Poverty and Inequality: Profile and Trends 36 Map 1.3. Extreme poverty rate and the distribution of the extreme poor, by county a. Extreme poverty rate, by county b. Distribution of the extreme poor, by county, percent A low density of economic activity is associated with high poverty incidence Economic activity (wealth) is geographically concentrated around two locations, indicating the existence of extreme spatial variation in wealth across South Sudan. Seen from space, the level of economic activity (as captured by nighttime lights) spikes in the two locations: one around the capital city, Juba, and the other around Bentiu, which has the first oil refinery built in the country and the largest camp for internally displaced persons (IDPs) (EIA 2024) (map 1.4). Juba is the primary urban center and economic focal point of South Sudan. It is home to 15 percent of the urban population of the country (UN-Habitat 2023) and hosts most businesses. Map 1.4. Density of population and economic activity a. Population density b. Nighttime lights, 2021 Sources: Elvidge et al. 2021; Population Counts (dashboard), WorldPop, University of Southampton, Southampton, UK, https:// www.worldpop.org/project/categories?id=3. Economic activities in urban centers do not automatically translate into improvements in welfare. Job opportunities are scarce in urban areas. Nearly half of urban households have experienced job losses since 2013. This threatens the prospects for peace and foments crime and violence (World Bank 2021). The lack of essential services and infrastructure, such as the lack of REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 37 electricity and the scarcity of water, constrains private sector development, including job creation (World Bank 2023). Connectivity and poverty: more poor than nonpoor are isolated Another key factor that feeds into the spatial disparity of welfare is connectivity. South Sudan’s transport sector has one of the poorest performance records in the world.13 The cost of transporting goods is among the highest globally. Much of the road system suffered extensive damage or was neglected during the war. The current state of the road network is deficient. Only 2 percent of the estimated 12,642 kilometers of roadway is paved (UN-Habitat 2023). Approximately half of the population must travel for more than an hour to reach an agglomeration of 10,000 population or more. The share is higher (58 percent) among the bottom 10 percent of the welfare distribution than among the top 10 percent (37 percent) (figure 1.6). A lack of accessibility is associated with higher poverty. Just as most of economic activities are spatially concentrated in a few key cities and urban agglomerations, access to school and health facilities is much greater in those urban areas (Map 1.5, a and b). As households move further from urban areas, their welfare levels decline while poverty rates increase. The inadequate connection within and among cities, as well as with rural areas and trade hubs in neighboring countries, presents an additional obstacle (map 1.5 c; figure 1.7). Due to its low road density compared to other regions, there are limited chances for trade and links between urban and rural areas, which hinders the creation of jobs and access to urban markets. Figure 1.6. Share of population who spend at least one hour to reach an agglomeration of 10,000 people or more. 57.7 54.5 52.5 49.8 48.5 48.5 47.9 45.4 43.5 40.7 36.8 Poorest decile D2 D3 D4 D5 D6 D7 D8 D9 Wealthiest All -D1 decile -D10 Sources: Data at South Sudan (dashboard), City Population, Thomas Brinkhoff, Oldenburg, Germany, https://www.citypopulation. de/en/southsudan/cities/; Weiss et al. 2018, 2020. 13 Road density is 15 meters per 1,000 square meters compared with an average 101 meters in the region. Freight tariffs, at US$0.20 per ton-kilometer, are among the highest in the world. Average truck speed is 6.4 kilometers per hour, slower than the average speed of a donkey. Transport costs of about 54 percent and the cost of logistics account for some of the highest freight tariffs anywhere (World Bank 2023). Chapter 1. Poverty and Inequality: Profile and Trends 38 Map 1.5. Travel times to basic services and the closest agglomeration, minutes a. Closest health care facility b. Closest education facility c. Closest urban area with > 50,000 population Sources: Macharia et al. 2017; South Sudan (dashboard), City Population, Thomas Brinkhoff, Oldenburg, Germany, https:// www.citypopulation.de/en/southsudan/cities/; South Sudan: Schools and Enrollment Data (portal), Centre for Humanitarian Data. Hague Humanity Hub, United Nations Office for the Coordination of Humanitarian Affairs, the Hague, the Netherlands, https://data.humdata.org/dataset/south-sudan- schools-and-enrolment-data-2015-sssams; Weiss et al. 2018, 2020. Figure 1.7. Effects of travel times to urban agglomerations on consumption and poverty Consumption (ln) Poverty 30min-60min 1-2 hour > 2hours -1 -.5 0 .5 1 -1 -.5 0 .5 1 Estimated E ects Sources: HBS 2022; FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data; South Sudan (dashboard), City Population, Thomas Brinkhoff, Oldenburg, Germany, https://www.citypopulation.de/en/southsudan/cities/; Weiss et al. 2018, 2020. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 39 1.3. VULNERABILITY TO POVERTY Household vulnerability to poverty is universal and structural rather than related to consumption or revenue volatility.14 Virtually all South Sudanese households (99.7 percent) face a substantial likelihood of remaining poor or falling into poverty. Because of permanent low consumption prospects, the population is structurally vulnerable to poverty. Poverty-induced (or structural) vulnerability, which is related to low human capital development and low asset ownership, is estimated at 96.5 percent, while the risk-induced (or transitory) vulnerability of the population is estimated at only 3.2 percent.15 Low endowment represents the main driver of rural vulnerability, while shocks are the main source of vulnerability in urban areas. Even households led by highly educated or salaried workers are overwhelmingly vulnerable to poverty. Female- and male-headed households face a similar likelihood of future poverty. Disparities in the likelihood of vulnerability relate mainly to the educational attainment of the household head. Vulnerability is pervasive, ranging from 100 percent among households with heads with no education to a remarkable 95 percent among households with heads who are well educated (secondary or higher). Household-specific shocks contribute as much as community-specific shocks to overall vulnerability. But household-specific shocks tend to contribute more to household vulnerability in urban areas and camps compared with rural areas. The ratio of household-specific shocks to community-specific shocks varies between 1.0 (rural), 1.6 (urban), and 1.0 (the national level). 1.4. POVERTY AND INEQUALITY TRENDS 1.4.1. POVERTY AND EXTREME POVERTY HAVE INCREASED Poverty and inequality trend analyses have covered only part of South Sudan. The analyses used the 2022 HBS and the 2016–17 HFS wave 3. The latter collected data only on 7 states of the 10: Central Equatoria, Eastern Equatoria, Lakes, Northern Bahr El Ghazal, Warrap, Western Bahr El Ghazal, and Western Equatoria (the blue states in map 1.6). The poverty data are therefore comparable only for these seven states over time. 14 Vulnerability to poverty is defined as the ex ante probability that a household remains in or falls into poverty in the two years following the survey. A given household may be vulnerable because it does not have sufficient assets or skills to enjoy acceptable levels of consumption (poverty-induced) or because it faces greater uncertainty about future streams of income (risk-induced). Such uncertainty may stem from exposure to severe or frequent shocks, such as illness, unemployment of a household member, price fluctuations, or a natural disaster, that cause a drop in household consumption. (See annex C for additional details.) 15 Vulnerability to poverty is the sum of poverty-induced vulnerability and risk-induced vulnerability. A household is structurally or poverty-induced vulnerable if its estimated mean consumption is below the poverty line. Among structurally vulnerable households, vulnerability is driven by permanently low consumption prospects (Günther and Harttgen 2009). A household is risk-induced or transitorily vulnerable if its estimated mean consumption is above the poverty line, but subject to high estimated consumption variability. Chapter 1. Poverty and Inequality: Profile and Trends 40 Map 1.6. South Sudan High Frequency Survey coverage Sources: HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/ index.php/catalog/2914; High Frequency Survey: wave 4 and Crisis Recovery Survey 2017, South Sudan (dashboard), Microdata Library, World Bank, Washington, DC, https://microdata. worldbank.org/index.php/catalog/3392. Living conditions deteriorated in 2016/17–22, and an increasing share of the population fell into poverty and extreme poverty. Between 2016 and 2022, in the seven states covered by the 2016 HFS, average household consumption declined by 15 percent, and poverty incidence increased by 6.7 percentage points (from 74.1 percent to 80.8 percent). The increase in the national poverty rate was primarily driven by a rise in rural poverty by 7.3 percentage points (from 75.4 percent to 82.7 percent). Urban poverty rose slightly, by 1.1 percentage points (from 64.9 percent to 66.0 percent) (see annex D, table D.1).16 Likewise, the extreme poverty rate increased by 4.1 percentage points (from 66.1 percent to 70.2 percent) (figure 1.8).17 Poverty depth and severity also worsened (see annex D, figure D.7). Figure 1.8. Changes in the poverty and extreme poverty Figure 1.9. Multidimensional rates, 2016/17–2022, percent poverty rates, 2016/17–2022, percent 80.8 82.7 74.1 75.4 93.4 95.9 88.7 92.9 64.9 66.0 66.1 70.2 67.8 72.6 54.751.6 66.6 55.8 All (7 states) Urban Rural All (7 states) Urban Rural Poverty Extreme poverty Urban Rural All 7 States 2016/17 2016/17 2022 Sources: Estimates based on HBS 2022; HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/2914. 16 The HFS wave 3 does not cover the camps. Poverty trends among the camps is therefore not included in this analysis. 17 The extreme poverty rate measures the share of a population living below the food poverty line of SSP298,478 per person per year, equivalent to SSP818 a day. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 41 Nonmonetary poverty displayed a similar upward trend. Between 2016 and 2022, all dimensions of well-being deteriorated in the seven states that had been covered by the HFS wave 3 leading to an increase by 4.2 percentage points in the multidimensional poverty rate, mostly driven by a decline in urban areas (see figure 1.9). 1.4.2. CONSUMPTION INEQUALITY DECLINED Consumption inequality narrowed across areas of residence. The Gini coefficient decreased by 0.042 percentage points (from 0.45 to 0.41) between 2016–17 and 2022 (figure 1.10). The decline was especially acute in rural areas, where the Gini index dropped from 45.5 to 40.5, compared with a reduction from 40.6 to 38.2 in urban areas. In urban areas, inequality narrowed because the lowest welfare percentiles exhibited greater consumption growth than the rest of the distribution. In rural areas, inequality declined because the wealthier percentiles were more severely affected by the widespread reduction in consumption. Figure 1.10. Gini index, 2016/17 and 2022 45.1 45.5 41.1 40.6 38.2 40.5 All (7 states) Urban Rural 2016/17 2022 Sources: Estimates based on HBS 2022; HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/2914. 1.5. DRIVERS OF POVERTY 1.5.1. CONSUMPTION GROWTH, INEQUALITY, AND POVERTY Household consumption declined across all welfare percentiles in 2016/17–22, suggesting that impoverishment was widespread among South Sudanese households. Persistent inflation, combined with multiple, recurring shocks, contributed to a significant erosion in purchasing power in South Sudan during the period. Figure 1.11 shows growth incidence curves, which display the annualized growth rate of per capita household consumption for every percentile between 2016 and 2022. Overall, in the seven states, the curves are below the zero line and trending downward, indicating that household consumption fell across the welfare distribution. The decline was greater among the wealthiest households. This reflects the lack of consumption (economic) growth that underlies the rise in poverty in rural areas and at the national level. However, the entire distribution experienced positive consumption growth in urban areas. This signifies an average increase in urban incomes and the positive effect of economic growth on urban poverty reduction. Chapter 1. Poverty and Inequality: Profile and Trends 42 Figure 1.11. The per capita consumption growth rate (growth incidence curves), 2016/17–22 − − The increase in poverty was driven by declining household consumption, while inequality narrowed. Figure 1.12 decomposes changes in the poverty rate into the impact of the growth in average consumption and changes in inequality, that is, redistribution. Overall, poverty expanded by 6.7 percentage points between 2016 and 2022. However, if inequality had not narrowed, poverty would have increased slightly more (6.9 percentage points) (figure 1.12). On average, household per capita consumption fell by 15 percent in 2016/17–22. If household consumption had remained unchanged, the narrowing in inequality would have led to a decline by 0.2 percentage points in poverty incidence. Figure 1.12. Growth-inequality decompositions, 2016/17–22 8.3 6.9 6.7 7.3 Growth Component 4.5 Inequality Component 1.1 Change in poverty incidence - 0.2 - 1.0 - 3.4 All (7 states) Urban Rural Sources: Estimates based on HBS 2022; HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/2914. Note: Because of the small sample size of the 2022 HBS, the subnational poverty and inequality estimates are unreliable. Subnational estimates are indicative. See chapter 2 for a comprehensive analysis of poverty based on reliable subnational estimates. Similar patterns are observed in rural areas, where a decline in household consumption drove the rise in the poverty rate by 7.3 percentage points. In rural areas, the growth effect generated an 8.3 percentage point increase in poverty, whereas the distribution effect contributed to a 1.0 percentage point decline (see figure 1.12). This is because rural consumption inequality was largely narrowed through the redistribution in favor of the percentile distribution around the poverty line. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 43 In urban areas, the poverty rate did not change substantially in 2016/17–22. The poverty rate rose by only 1 percentage point. Consumption growth contributed to a 3.4 percentage point decrease in poverty, whereas inequality (the redistribution effect) generated a 4.5 percentage point rise in poverty. If the growth rate in urban areas between 2016/17 and 2022 had been achieved in a distribution-neutral manner, the urban poverty rate would have been 4.5 percentage points lower than the current estimate. This rather counterintuitive finding emerges because urban consumption inequality was mostly improved through the expansion in consumption among households with consumption well below the poverty line, and thus the improvement had no effect on the reduction in the poverty rate. 1.5.2. THE CONTRIBUTION TO POVERTY OF CHANGES IN HOUSEHOLD ENDOWMENTS Changes in poverty in 2016/17–22 resulted from several factors that drove household consumption in opposite directions. The factors that led to a reduction in poverty in 2016/17–22 include (1) improved access to physical capital (transportation, agricultural tools in rural areas, and mobile phones), (2) improved access to basic services (electricity, drinking water), (3) a shift from agricultural activities to services, and (4) a tiny improvement in women’s access to education. Meanwhile, the effects of recurrent conflict, unfavorable climate conditions, population growth, fewer job opportunities, and low human capital accumulation depressed household consumption in 2016/17–22 resulting in a higher incidence of poverty. Over 2016/17–22, the factors that contributed to an increase in poverty outweighed those that tended to reduce poverty. The total national poverty rate thus rose by 6.7 percentage points from the 74.1 percent in 2017. The model explains 4.0 percentage points of the rise in the poverty rate, corresponding to 59.5 percent of the overall poverty increase. Figure 1.13 illustrates the contribution of the various factors to the poverty increase. Annex E presents a detailed description of the decomposition results summarized in figure 1.13. Figure 1.13. Decomposition of the changes in the poverty rate, 2016/17–22 Estimated increase in poverty rate between 2017-22 6.71 Change in poverty rate explained by the model 4.00 Change in poverty unexplained by the model 2.72 Reccurent con icts 3.52 Population Growth 1.66 Education attainment by households' head 1.00 Climate shocks ( ooding or drought) 0.98 Change in women's occupations 0.48 Urbanization (urban residency) 0.12 Women's access to education - 0.03 Change in households heads' occupations - 0.54 Change in access to basic infrastructure - 0.90 Phisycal Capital Accumulation -2.30 -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 Sources: Estimates based on HBS 2022; HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank.org/index.php/catalog/2914. Chapter 1. Poverty and Inequality: Profile and Trends 44 CHAPTER 2. FOOD SECURITY Photo: © Mayak Akuot / FAO 45 Food insecurity has reached high levels in South Sudan. Understanding the effects of food insecurity on household welfare is crucial to poverty reduction in South Sudan. This chapter assesses the severity of food insecurity in South Sudan, the potential drivers, and the profile of food insecure households using data from the HBS and FSNMS+.18 It relies on the World Food Programme food security index (FSI). The FSI embodies two critical dimensions: the sufficiency of current food consumption within households and the economic vulnerability and depletion of assets among these households (WFP 2015).19 2.1. THE PREVALENCE AND PROFILE OF FOOD INSECURITY South Sudan’s food security situation has deteriorated compared to a decade ago, with nearly three-quarters of households experiencing high food insecurity levels in 2022. The FSI shows significant shifts in food security in South Sudan after 2010, with a remarkable 25 percentage point increase in moderate food insecurity (from 28 percent to 53 percent), a 10 percentage point rise in severe food insecurity (from 10 percent to 20 percent), and a 3 percentage point increase in both categories after 2020 (figure 2.1). In 2022, more than half (53 percent) of South Sudanese fell into the category of moderate food insecurity, signifying a notable food consumption gap or an ability to meet minimal food needs only through irreversible coping strategies, such as selling productive assets or seeds intended for planting. Additionally, a substantial share of South Sudanese (20 percent) were facing extreme food insecurity, reflecting either an extreme food consumption gap or a severe loss of livelihood assets, such as through the sale of their homes or land (figure 2.2). 18 The HBS was conducted during April–June 2022. It covered a sample of 719 households across all 10 states of South Sudan on household consumption expenditure, thereby providing nationally representative estimates of poverty and inequality. FSNMS+, a nationwide survey, was conducted in October 2021 and January 2022. It covered 14,215 households across the 79 counties in the country’s 10 states, and it collected data on food security and livelihoods; water, sanitation, and hygiene; health care; education; social protection; and shelter. See FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 19 The FSI is calculated using the Consolidated Approach for Reporting Food Security (WFP 2015). It classifies individuals or households into four key food security classifications: food secure, marginally food secure, moderately food insecure, and severely food insecure (see annex F for the description of the methodology). Chapter 2. Food Security 46 Figure 2.1. Food insecurity, 2010–21/22 Figure 2.2. Trends in the food insecurity, 2019–21/22 56 53 53 48 47 50 2019 6 25 47 23 % of individuals % of individuals 37 37 28 30 30 32 26 2020 6 26 50 17 23 20 15 14 17 10 10 10 12 6 2021/22 4 23 53 20 3 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Food secure Marginally food secure Moderately food insecure Moderately food Severely food insecure Severely food insecure insecure Sources: 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data; WFP et al. 2021. Six states experienced worsening food insecurity in 2020–22. The incidence of moderate and severe food insecurity was most pronounced in Jonglei, at 88 percent, closely followed by Western Equatoria, at 82 percent. Both states witnessed the most substantial deteriorations: 22 and 23 percentage point increases, respectively, beginning in 2020. They were followed by Warrap (10 percentage point rise), Unity (8 percentage points), Western Bahr el Ghazal (7 percentage points), and Lakes (4 percentage points). The high levels of food insecurity in Jonglei and Western Equatoria may be primarily attributed to the persistent conflict and security challenges plaguing these places. The conflicts are disrupting agricultural activities, hindering farming, and breaking food supply chains, leading to inadequate access to reliable food sources. These challenges likewise represent major obstacles to the effective delivery of humanitarian assistance (HRW 2023) (figure 2.3). Figure 2.3. Food insecurity, by state 13 14 11 16 20 30 20 29 19 25 20 % of individuals 55 46 44 53 69 61 52 41 59 58 50 29 30 30 31 23 15 17 20 21 25 11 6 9 4 1 3 4 2 5 4 5 5 South Jonglei Western Western Lakes Central Upper Unity Northern Eastern Warrap Sudan Equatoria Bahr el Equatoria Nile Bahr el Equatoria Ghazal Ghazal Food secure Marginally food secure Moderately food insecure Severely food insecure Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 47 Food insecurity disproportionately affected rural and camp residents. Approximately three- fourths of rural residents and 67 percent of camp dwellers experienced moderate to severe food insecurity, in contrast to the 52 percent observed among urban residents. Moreover, only 25 percent of rural residents and 33 percent of camp inhabitants are classified as food secure or marginally food insecure, compared with 48 percent in urban households. However, the food situation in urban areas is also difficult. More than 44 percent of urban residents were facing moderate food insecurity (figure 2.4). Rural residents were struggling with exceptionally high levels of severe food insecurity, affecting more than one South Sudanese in five and highlighting the fact that rural areas are home to 84 percent of the poor in South Sudan. Figure 2.4. Food insecurity, by location Figure 2.5. Food insecurity, by camp 8 8 9 5 1 13 11 22 36 % of individuals 44 45 60 56 69 62 54 40 37 36 22 24 15 21 23 21 12 11 11 5 9 11 3 Rural Urban Camp Juba IDP Juba IDP Naivasha Bentiu IDP Malakal Camp 3 Camp 1 IDP Camp Camp PoC Food secure Marginally food secure Severely food insecure Moderately food Moderately food Severely food insecure insecure insecure Marginally food secure Food secure Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Food insecurity varies across IDP camps: the Juba camps and Bentiu are experiencing the highest levels of food insecurity. In the Juba and Bentiu camps, 7 residents in 10 face moderate to severe food insecurity. In comparison, the Naivasha IDP camp reports a lower, but worrisome rate of 5 residents in 10, while 4 in 10 residents of the Malakal Protection of Civilians site (PoC) are dealing with moderate to severe food insecurity (figure 2.5). Substantial disparities in food security are evident across livelihood zones in South Sudan. The FSI highlights that zones characterized by challenging conditions, such as the southeastern semiarid pastoral zone (SS05), the eastern plains sorghum and cattle zone (SS06), and the northeastern maize cattle and fishing zone (SS10), exhibit high levels of severe food insecurity that affect more than residents more than 3 residents in 10 compared with 1 or 2 in 10 in other livelihood zones. These areas have semiarid or arid climates, scarce rainfall, and prolonged dry seasons. These characteristics pose obstacles to crop cultivation and livestock grazing. Additionally, livelihood zones SS06 and SS10, along with the equatorial maize and cassava zone (SS01) and the Nile Basin fishing and agropastoral zone (SS08), also experience notably high levels of moderate food insecurity, with approximately 6 residents in 10 affected within these zones (Map 2.1). Chapter 2. Food Security 48 Map 2.1. Food insecurity, by livelihood zones and counties, percent a. Moderately food insecure, by livelihood zone b. Severely food insecure, by livelihood zone c. Moderately food insecure, by county d. Severely food insecure, by county Source: World Bank calculations based on 2022 HBS; FEWS NET 2018. Households headed by a man or an individual with no education are more likely to be food insecure, and the prevalence of food insecurity decreases as the educational attainment of the household head rises. In male-headed households, 76 percent of the household members experience moderate to severe food insecurity, compared with 69 percent in female-headed households (figure 2.6). In households with a head who is illiterate or who has attained only a primary education, members are more likely to be food insecure. Meanwhile, households headed by individuals with secondary schooling or higher show a greater share of members enjoying higher, more acceptable levels of food security (figure 2.7). REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 49 Figure 2.6. Food insecurity, by sex of Figure 2.7. Food insecurity, by educational attain- household head ment of household head 22 14 13 22 18 48 % of individuals % of individuals 49 46 51 54 54 30 49 30 21 26 3 8 11 3 20 Never attended Primary Secondary Vocational 3 5 school/incomplete and above training Male-headed Female-headed primary Severely food insecure Moderately food Food secure Marginally food secure insecure Moderately food Severely food insecure Marginally food secure Food secure insecure The more food insecure the household, the less likely the children are to attend school. Among children ages 3–17, school attendance is significantly lower in food-insecure households. The share of children in this age-group who attend school declines gradually from 56 percent in food-secure households to 36 percent in severely food-insecure households (figure 2.8). Figure 2.8. School attendance, by food insecurity 54 46 42 36 % of individuals Food secure Marginally Moderately Severely food secure food insecure food insecure Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Monetarily poor households are overwhelmingly food insecure. Among the bottom 80 percent of the poor, 76 percent experienced greater food insecurity, while 66 percent of the nonpoor were either moderately (51 percent) or severely (15 percent) food insecure (figure 2.9). Even among the relatively more well off, food insecurity remains a challenge. Chapter 2. Food Security 50 Figure 2.9. Food insecurity, by wealth quintile Figure 2.10. Food insecurity, by type of shelter 15 20 26 23 10 22 % of individuals 41 54 55 49 54 51 37 23 17 24 4 3 4 12 27 Tukul Rakooba Improvised/ Permanent 21 7 Communal/ Semi/ 4 Emergency Concrete B80 T20 building Food secure Marginally food secure Food secure Marginally food secure Moderately food Severely food insecure Moderately food Severely food insecure insecure insecure Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Individuals living in homes constructed of rudimentary materials or without electricity tend to be food insecure. Inhabitants of rudimentary dwellings, such as rakoobas and tukuls, report higher levels of food insecurity relative to inhabitants of concrete structures. Thus, 81 percent of individuals living in rakoobas are moderately or severely food insecure, while half of the individuals living in concrete structures are food secure (see figure 2.10). Households that use charcoal or gas for cooking display better food security outcomes. There is a 15 percentage point difference between these households and households relying on firewood, cow dung, or grass (50 percent versus 76 percent, respectively) (figure 2.11). Households with access to electricity tend to be more food secure. Approximately 60 percent of households with electricity, generators, or solar power are food secure or marginally food secure, compared with only 20 percent and 18 percent, respectively, among households relying on paraffin or firewood and among households with no lighting (figure 2.12). Figure 2.11. Food insecurity, by type of Figure 2.12. Food insecurity, by type of lighting cooking fuel source 10 18 16 6 21 25 40 35 % of individuals 49 55 64 54 39 38 29 18 16 20 21 2 2 6 12 3 Para n/ No Torch/ Electricity/ Firewood/Cow dung/ Charcoal/Gas Firewood/ lighting phone Generator/ Grass/No cooking Grass/ Gas/ Candle Solar power Food secure Marginally food secure Food secure Marginally food secure Moderately food Severely food insecure Moderately food Severely food insecure insecure insecure Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 51 Individuals living in households primarily depending on food assistance and external support experience higher levels of food insecurity compared with households engaged in economic activities. In households in which the heads depend on charity or are economically inactive, 81 percent of the members face moderate or severe food insecurity. Even skilled and salaried workers, among whom the food security rate is the highest (41 percent), a significant share (59 percent) are food insecure, suggesting that the food crisis is dire in South Sudan (figure 2.13).Additionally, more than 70 percent of agricultural households face moderate or severe food insecurity. Figure 2.13. Food insecurity, by main household activity 16 11 25 23 32 30 48 55 50 58 48 51 32 21 24 19 17 15 4 4 4 9 2 3 Hunting Support from Traders/ Food assistance/ Agriculture Skilled/salaried others/begging/ shop owner/ Sale of food worker incative unskilled casual assistance Food secure Marginally food secure Moderately food insecure Severely food insecure Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 2.2. THE UNDERLYING CAUSES OF FOOD INSECURITY 2.2.1. LOW HOUSEHOLD OWN FOOD PRODUCTION A substantial number of rural households in South Sudan face challenges in producing sufficient food and depend largely on market purchases to meet their needs. Except for vegetables, fewer than 50 percent (Figure 2.14) of rural households engage in subsistence production for key food staples. For cereals, roots, and tubers, approximately 45–46 percent of households rely on own production, while market purchases remain a critical component of food acquisition. Market dependency is particularly pronounced for non-staple commodities such as meat, oils, sugar, and spices, with over half of rural households purchasing these items. Furthermore, essential staples, including milk, cereals, roots, and legumes, are procured from markets by roughly 40% of households. The limited capacity for own food production poses a substantial threat to food security, exacerbating vulnerability to external shocks such as price volatility and supply chain disruptions. Households with insufficient production capacity face heightened risks of food insecurity, as their reliance on market mechanisms renders them sensitive to fluctuations in purchasing power and availability. Increasing agricultural productivity and self-sufficiency requires investments in Chapter 2. Food Security 52 productive assets, such as fertile land and agricultural machinery, as well as access to critical inputs like labor, credit facilities, and agricultural extension services. Without such support, achieving a balanced and nutritionally adequate diet remains a significant challenge for rural households. Urban households exhibit a high reliance on market purchases for their food. Urban households demonstrate a high dependency on market for food acquisition, with 76 percent to 95 percent relying on market purchases across various food categories. Yet approximately 19 percent of urban households produce their own vegetables, while around 10 percent secure staple foods through food assistance programs. In camp settings, food assistance serves as the primary source of staple food, with 69 percent of households receiving cereals, grains, and roots through such programs. However, the significant reliance in camps, on markets for other food categories, such as meats (72 percent), milk (54 percent), and vegetables (73 percent), raises concerns regarding food accessibility and affordability. The heavy dependence on external assistance for staples, coupled with market reliance for non-staple foods, underscores the vulnerability of urban and camp-based households to supply chain disruptions and price volatility. Figure 2.14. Main mode of food acquisition by residential areas a. Percentage of households by main mode of food acquisition in rural area Vegetables and Leaves 60 21 0 19 Milk and other dairy 48 39 3 10 Cereals and grains 46 33 15 6 Roots and tubers 45 37 4 15 Legumes/nuts 41 40 12 7 Fruits 41 28 0 31 Meat, sh and eggs 14 54 0 31 Oil/fat/butter 9 64 20 7 Sugar or sweet 7 86 0 7 Condiments/Spices 7 84 2 7 Own production Market (Purchase cash or credit) Food assistance Others Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. b. Percentage of households by main mode of food acquisition in urban area Sugar or sweet 2 95 12 Meat, sh and eggs 2 93 23 Condiments/Spices 3 92 32 Oil/fat/butter 2 90 7 1 Milk and other dairy 6 89 33 Legumes/nuts 11 81 6 2 Cereals,grains,roots 11 77 10 1 Vegetables and Leaves 19 77 22 Fruits 19 76 23 Own production Market (Purchase cash or credit) Food assistance Others REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 53 c. Percentage of households by main mode of food acquisition in camps area Vegetables and Leaves 5 73 17 6 Sugar or sweet 3 76 19 3 Condiments/Spices 2 75 20 4 Meat, sh and eggs 2 72 21 5 Fruits 2 62 29 7 Milk and other dairy 3 54 37 6 Legumes/nuts 4 40 52 4 Oil/fat/butter 2 33 62 3 Cereals, grains, roots 2 25 69 3 Own production Market (Purchase cash or credit) Food assistance Others There are notable variations among states in how households acquire their food. Figure 2.15 shows that reliance on own production of food varies according to the agropastoral profile of the states across the country. In five states—Warrap, Western Equatoria, Central Equatoria, and Eastern Equatoria—rural households mostly rely on their own production for cereals and grains. This contrasts sharply with Unity, Upper Nile, and Jonglei, where only a few rural households depend on their own production for these needs. In rural Upper Nile, half of the households purchase cereals and grains from markets, while in rural Jonglei, nearly 60 percent of households rely on food assistance. States with higher reliance on market for cereals and grain consumption (e.g. Upper Nile, Lakes, Northern and Western Bahr el Ghazal) exhibit potential vulnerability in food security due to market volatility or purchasing power limitations. In five states (Central Equatorial and Western Equatorial, Unity, Western bahr el ghazal, and Jonglei), roots and tubers that households consume mostly come from their own production, while market purchase represents the most common source of roots and tubers in the remaining five states. States like Central Equatorial and Western Equatorial exhibit high reliance on own production of roots and tubers (71%). Conversely, own production of roots and tubers is very limited in Northern Bahr el Ghazal, Upper Nile, and Warrap, where around 70, 56, and 55 percent of rural households rely on market for roots and tubers consumption. Households mostly get milk and dairy products from their own production. This is especially true in Unity, Eastern Equatoria, Warrap, Lakes, and Jonglei. In Northern Bahr el Ghazal, Upper Nile, Central Equatorial, and Western Bahr el Ghazal, these products are mostly purchased. Food assistance is rather uncommon for dairy products in all states. Chapter 2. Food Security 54 Figure 2.15. Main mode of food acquisition by states a. Percentage of households by main acquisition mode of cereals and grains in rural areas 9 3 2 8 6 7 4 4 3 6 3 5 1 9 10 2 3 7 22 15 23 29 32 38 39 45 45 48 57 33 25 50 65 65 61 50 48 13 46 41 40 33 25 20 Warrap Western Central Eastern Northern Lakes Western Unity Upper Jonglei National Equatoria Equatoria Equatoria Bahr el Bahr el Nile Ghazal Ghazal Own production Market (Purchase cash or credit) Food assistance Others b. Percentage of households by main acquisition mode of roots and tubers in rural areas 5 7 4 1 1 15 0 21 19 21 21 16 21 15 24 22 3 0 10 4 42 1 1 0 18 14 40 37 16 45 55 56 70 71 71 65 53 50 45 40 33 23 19 9 Central Western Unity Western Jonglei Eastern Lakes Warrap Upper Northern National Equatorial Equatoria Bahr el Equatoria Nile Bahr el Ghazal Ghazal Own production Market (Purchase cash or credit) Food assistance Others 2.2.2. LOW ACCESS TO THE MARKET The long distances and the lack of availability of transportation to reach markets are significant challenges to establishing food security in South Sudan. Roughly half of South Sudanese households report that markets are distant or that transportation options are limited. This is particularly true in urban areas and camps (66 percent and 78 percent, respectively). However, rural households are also significantly affected. Among rural households, 55 percent require more than an hour to reach the nearest market, which is roughly equivalent to a distance of 5 kilometers (figure 2.16, panel a). Food-insecure households are more likely to travel longer distances to reach marketplaces compared with food-secure households (54 percent versus 45 percent) (figure 2.16, panel b). Investments in transportation infrastructure are needed to reduce high transportation costs for producers, buyers, and traders. Better access to markets would also incentivize rural residents to produce more to seize greater economic opportunities. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 55 Figure 2.16. Accessibility to markets, by location and food insecurity a. Market too distant, no means of transport b. The nearest market or grocery is more than an hour away 55 54 53 50 % of households 44 % of households 79 67 29 49 50 51 57 47 48 16 8 Severely food insecure insecure secure Food secure insecure insecure Moderately food Marginally food Severely food Moderately food secure Camp Camp South Sudan Urban South Sudan Urban Food secure Rural Rural Marginally food Location FSI Location FSI Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 2.2.3. PERSISTENT CONFLICT Protracted conflict and acts of violence severely affect the affordability of food among households. In 2022, households experiencing conflict-related shocks, such as the death of a working adult, other violence, raiding, or looting, were more likely to exhibit sharp declines in their ability to access money or food. More than 70 percent of severely food-insecure households saw their ability to acquire money or food erode because of the death of a working adult or because of other violence-related shocks, compared with 61 percent and 39 percent of food-secure households (figure 2.17). Figure 2.17. The effect of death and violence on the ability of households to obtain money or food a. The impact of the death of a working adult b. The impact of insecurity, raiding, and looting 39 % of households % of households 61 68 68 72 66 71 73 43 31 26 25 14 25 22 20 9 7 7 7 11 7 9 13 Food Marginally Moderately Severely Food Marginally Moderately Severely secure food food food secure food food food secure insecure insecure secure insecure insecure No impact Small Decrease Large decrease No impact Small Decrease Large decrease Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Chapter 2. Food Security 56 Households experiencing substantial food insecurity have been more highly exposed to fatalities. The higher the share of households suffering fatalities, the worse the food insecurity of households. A year (three years) before the 2022 HBS or FSNMS+ survey, 5 (17) percent of severely food-insecure households had been exposed to fatalities, compared with 4 (14) percent of food-secure households. While the FSI shows moderate differences (figure 2.18, panel a), a closer examination of the food consumption score (a proxy indicator for household caloric availability), one of the key components of the FSI, reveals a strong correlation between fatalities and food consumption (figure 2.18, panel b). More households with poor consumption patterns at the time of the survey were exposed to fatalities compared with households with acceptable consumption scores: 5 percent versus 3 percent one year before the survey, and 17 percent versus 13 percent for the three-year period before the survey. Figure 2.18. Household exposure to fatalities, by food insecurity and food consumption score a. Exposure to fatalities, by food insecurity b. Exposure to fatalities, by food consumption score 17 % of households % of households 16 17 15 14 14 13 4 4 5 5 5 4 3 Food Marginally Moderately Severely secure food food food Poor Borderline Acceptable secure insecure insecure 1 years before survey 1 years before survey 3 years before survey 3 years before survey Source: Estimates based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 2.2.4. CLIMATE SHOCKS Food-insecure households experience drought more frequently. According to the standardized precipitation-evapotranspiration index, a drought index that combines precipitation and temperature data to assess drought conditions, 20 percent of highly food-insecure households in South Sudan are located in areas severely affected by drought, compared with 14 percent among food-secure households (figure 2.19, panel a). Six months prior to the survey, the shares were still high: 12 percent and 14 percent of moderately and severely food-insecure households reported that they had been affected by drought, irregular rains, or prolonged dry spells. Other climate-related shocks also affected food-insecure households, such as flooding (15 percent), crop destruction due to floods, and excessive rain (12 percent each), compared with only 4 percent, 7 percent, and 8 percent, respectively, among food-secure households (figure 2.19, panel b). REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 57 Figure 2.19. Household exposure to climate shocks, by food insecurity and climate indicators a. Standardized precipitation-evapotranspiration index, a year before the survey 20 20 16 14 % of households 8 9 7 7 6 6 5 4 5 4 3 4 Food secure Marginally food secure Moderately food secure Severely food secure Extremely/Very wet Moderately wet Moderately dry Severly/Extremely dry b. Climate shocks, six months before the survey 14 15 14 13 12 11 11 12 12 12 11 10 % of households 8 7 7 4 2 3 3 1 Food secure Marginally food secure Moderately food secure Severely food secure Too much rain Houses ooded Crops destroyed by oods Markets Flooded Drought/irregular rains, prolonged dry spell Sources: Calculations based on climate data; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Chapter 2. Food Security 58 CHAPTER 3. SHOCKS AND RESILIENCE Photo: © Mayak Akuot / FAO 59 This chapter focuses on shocks and resilience in South Sudan. It is organized around five sections. The first section analyzes the shocks facing South Sudanese households. Section 2 presents the profile of those who are most vulnerable to shocks. Section 3 investigates the impacts of shocks on household welfare by combining both self-reported and data-driven measures. Section 4 details coping strategies to deal with the shocks. The last section surveys the landscape of social protection and strategies to reduce poverty and strengthen resilience. 3.1. THE NATURE AND OCCURRENCE OF SHOCKS Over the past decade, many South Sudanese have experienced multiple shocks. Shocks have been widespread and frequent in South Sudan. Among households, 70 percent have reported that they suffered at least one shock in the six months before the 2021 FSNMS survey (figure 3.1). Most shocks arose from natural, economic, health, and sociopolitical issues. COVID-19 remained an important shock, especially through the accompanying measures, such as lockdowns. Figure 3.1. Percentage of households that experienced at least one shock during the previous six months, by state 100 2.5 90 80 2.0 2.0 70 1.8 1.6 1.6 60 1.4 1.5 1.5 1.4 1.4 1.3 50 40 1.0 1.0 30 20 0.5 10 0 0.0 Bahr el Ghazal Bahr el Ghazal Northern Equatoria Equatoria Equatoria Eastern Western Western Jonglei Central Warrap Lakes All Unity Experienced at least one shock No shocks Mean number of shocks (rhs) Source: Estimates based on 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/ fsi/data. Chapter 3. Shocks and Resilience 60 3.1.1. FOOD PRICES High food prices were the most common shock households experienced (30 percent). Some households also reported high fuel prices (figure 3.2). While the shares reporting these shocks were relatively small, fuel prices often play an important role in increasing economy-wide inflationary pressures. While fuel price rises exacerbated inflation, South Sudan, as a crude oil exporter, also received more revenue because of the increasing prices. This could have been used to negate the fallout from rampant inflation. Figure 3.2. Percentage of households that experienced specific shock by areas of residence High food prices Flood Reduced income of household members Drought/irregular rains Insecurity/raiding/looting High prices of fuel/transport 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 Camp Rural Urban All Source: Estimates based on 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 3.1.2. CLIMATE SHOCKS AND NATURAL DISASTERS Floods affect 27 percent of households. South Sudan is a flood-prone country, ranking seventh worldwide in the share of the population vulnerable to river floods. Floods are the most recurrent climate-related shocks facing communities. They are associated with the heavy seasonal rainfall, which typically peaks between July and November. Some of the poorest states in South Sudan, particularly in the Greater Upper Nile, are also the most vulnerable to recurrent floods and droughts. The seasonality and intensity of the rainy season are changing, resulting in more frequent and extreme flooding in many parts of the country. Over the past two decades, floods have impacted many communities and thousands of South Sudanese, especially riparian settlements along the White Nile and its tributaries. States in the Greater Upper Nile are frequently exposed to floods, including Jonglei, Northern Bahr El Ghazal, Unity, Warrap, and Western Bahr El Ghazal states (map 3.1). The heavy rains and unusual climate conditions have also contributed to the serious desert locust outbreak, which threatens food security and livelihoods across the region and could lead to more suffering, displacement, and conflict (World Bank 2020). Indeed, the same flood- prone areas also suffer from substantial food insecurity (figure 3.3). REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 61 Map 3.1. Populations exposed to floods a. Population exposed to long-term flood hazards, b. Estimated population per 100 m2 exposed to the percent 2021 floods Sources: Global Flood Map (portal), Fathom, Bristol, UK, https://www.fathom.global/product/global-flood-map/; Population Counts (dashboard), WorldPop, University of Southampton, Southampton, UK, https://www.worldpop.org/project/ categories?id=3; UNOSAT Dashboard: Flood Monitoring and Population Exposure over South Sudan, version June 2, 2022, United Nations Institute for Training and Research, Geneva, https://unitar.org/maps/map/3554. Note: Panel a: The flood depth threshold of 15 centimeters for a return period of 100 years is used to define the areas of flood hazard. Panel b: All flood extent maps were combined and overlaid with a high-resolution population map. Figure 3.3. Flood hazard and food insecurity Sources: Global Flood Map (portal), % of people in acute food insecurity 0.8 Fathom, Bristol, UK, https://www.fathom. global/product/global-flood-map/; Population Counts (dashboard), WorldPop, 0.6 University of Southampton, Southampton, UK, https://www.worldpop.org/project/ categories?id=3; UNOSAT Dashboard: Flood Monitoring and Population Exposure 0.4 over South Sudan, version June 2, 2022, United Nations Institute for Training and Research, Geneva, https://unitar.org/maps/ 0.2 map/3554. Note: The figure shows the correlation between the share of the population exposed to long-term flood hazard and 0 25 50 75 100 acute food insecurity (IPC Phase 3 +) % of people exposed to ood hazard (October–November 2022). Floods have become more frequent and devastating. Since 2019, the country has experienced a series of floods affecting around one million South Sudanese each year.20 While recurrent flooding benefits communities by supplying abundant fish and water for crops, excessive flooding causes damage to infrastructure and heavy losses in agricultural output in many communities. Other household assets, such as cattle, are often destroyed or lost in the aftermath of flooding. Climate- 20 EM-DAT (International Disaster Database), Centre for Research on the Epidemiology of Disasters, Institute of Health and Society, Université Catholique de Louvain, Brussels, accessed November 6, 2023, https://www.emdat.be/. Chapter 3. Shocks and Resilience 62 related shocks are projected to reduce agricultural production by 20 percent and exacerbate water scarcity in South Sudan.21 In 2016 and 2019, outbreaks of cholera and measles followed large-scale floods. Droughts tend to occur cyclically (every five years) in South Sudan. Between 2009 and 2022, there were three large-scale droughts, in 2009, 2016, and 2021. Compared with floods, droughts typically affect wider geographic areas and a larger number of people (figure 3.4) with devastating impacts on agricultural production, food security, and water supply, among others. Figure 3.4. Population affected by drought and flood, 2009–22 10.00 Million 5.00 - 2008 2009 2010 2012 2013 2016 2019 2020 2021 2022 Drought Flood Sources: Global Flood Map (portal), Fathom, Bristol, UK, https://www.fathom.global/product/global-flood-map/; ICA (Integrated Context Analysis) South Sudan, 2016: Drought Risk, 1998–2014 (dataset), Humanitarian Data Exchange, United Nations Office for the Coordination of Humanitarian Affairs, New York, https://data.humdata.org/dataset/wfp_ica_ssd_2016/resource/1e9c5a38- 299f-494e-9007-d16b267d419c; Population Counts (dashboard), WorldPop, University of Southampton, Southampton, UK, https://www.worldpop.org/project/categories?id=3; UNOSAT Dashboard: Flood Monitoring and Population Exposure over South Sudan, version June 2, 2022, United Nations Institute for Training and Research, Geneva, https://unitar.org/maps/map/3554. Note: The population affected is defined as people requiring immediate assistance during a period of emergency, that is, requiring basic survival needs such as food, water, shelter, sanitation, and immediate medical assistance. Around 12 percent of households reported some experience of drought in 2021. The 2009 drought affected around 4.4 million South Sudanese, and the most recent drought (2021) reached 7.7 million people. While the 2016 drought involved relatively fewer South Soudanese (3.6 million), it had more severe consequences. The droughts of 2009 and 2021 led to food shortages, but the 2016 drought caused widespread famine. Difficulties in deploying a response to the disaster in the middle of conflict might have exacerbated the impacts of the 2016 drought. Considering the role of agriculture in livelihoods and its heavy dependence on rainfall, climate change is bound to negatively impact many South Sudanese (map 3.2; figure 3.5). 21 See IRISS (Improving Resilience in South Sudan): Strategies and Technologies to Build Resilience against Droughts and Floods (web page), BRACED Knowledge Manager, Overseas Development Institute, London, http://www.braced.org/about/about-the-projects/ project/?id=4dfc5e51-173e-4fe6-a97a-7edc5bb515d1. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 63 Map 3.2. Drought risk, 1998–2014 Figure 3.5. Drought risk and food insecurity % of people in acute food insecurity 80 60 40 20 Low Medium High Drought hazard Sources: ICA (Integrated Context Analysis) South Sudan, Sources: ICA (Integrated Context Analysis) South Sudan, 2016: Drought Risk, 1998–2014 (dataset), Humanitarian Data 2016: Drought Risk, 1998–2014 (dataset), Humanitarian Data Exchange, United Nations Office for the Coordination of Exchange, United Nations Office for the Coordination of Humanitarian Affairs, New York, https://data.humdata.org/ Humanitarian Affairs, New York, https://data.humdata.org/ dataset/wfp_ica_ssd_2016/resource/1e9c5a38-299f-494e-9007- dataset/wfp_ica_ssd_2016/resource/1e9c5a38-299f-494e-9007- d16b267d419c; Population Counts (dashboard), WorldPop, d16b267d419c; Population Counts (dashboard), WorldPop, University of Southampton, Southampton, UK, https://www. University of Southampton, Southampton, UK, https://www. worldpop.org/project/categories?id=3. worldpop.org/project/categories?id=3. Note: The map illustrates three levels of drought risk: low, Note: The figure shows box plots of counties to illustrate the medium, and high. The level is calculated based on the number link between the drought hazard and the variation in food of poor growing seasons and the share of surface area affected insecurity. by one or more poor growing seasons. 3.1.3. CONFLICT AND VIOLENCE Conflict and violence are important components of the debilitating shocks facing households in South Sudan. Around 7 percent of the survey respondents reported they had directly experienced violence. After decades of warfare, the government and people of South Sudan have continued to grapple with protracted bloodshed. The 2023 violence unfolded across all 10 states, confirming the persistence of the strife that had outlived the formal agreement to end hostilities reached in September 2018, although, since 2022, the conflict has been less deadly (figure 3.6). Nonetheless, it represents a large-scale humanitarian crisis, with millions of people forcibly displaced and facing heightened food insecurity. Chapter 3. Shocks and Resilience 64 Figure 3.6. Conflict events and fatalities, 2011–23 200 3500 3000 150 Fatalities 2500 Events 2000 100 1500 50 1000 500 0 0 2011m5 2011m11 2012m9 2013m2 2013m7 2013m12 2014m5 2014m10 2015m3 2015m8 2016m1 2016m6 2016m11 2017m9 2018m2 2018m7 2018m12 2019m5 2019m10 2020m3 2020m8 2021m1 2021m6 2021m11 2022m9 2023m2 2012m4 2017m4 2022m4 Violent events All events Fatalities Source: Calculations based on data of CCKP (Climate Change Knowledge Portal), World Bank, Washington, DC, https:// climateknowledgeportal.worldbank.org/. Areas of intense conflict are located in some of the poorest states in the Greater Upper Nile region in the northeast. States in the region are not only among the poorest, but also especially vulnerable to conflict and violence. Between May and October 2022, communities in the region experienced two periods of intense conflict. In the northern states of Jonglei and Upper Nile, armed battles started along the White Nile in August 2022 and continued until December 2022. In Greater Pibor, there were significant outbreaks of violence in December 2022 and January 2023 that led to reprisals in northern Jonglei in February and March 2023. These conflict events occurred in counties that were already vulnerable to food insecurity and risks of natural disaster, such as flooding, and they were accompanied by substantial population displacement (maps 3.3 and 3.4). Map 3.3. Conflict and IDPs in South Sudan a. Location of conflicts, 2013–22 b. Number of IDPs, December 2021 Sources: ACLED (Armed Conflict Location and Event Data Project) (dashboard), Robert S. Strauss Center for International Security and Law, Austin, TX, http://www.acleddata.com/; DTM South Sudan (Displacement Tracking Matrix, South Sudan) (dashboard), Round 12, International Organization for Migration, Geneva, https://dtm.iom.int/south-sudan; OCHA 2022. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 65 Map 3.4. IPC phases, October–November 2022 a. People in acute food insecurity, IPC Phase 3+ b. IPC phase distribution Source: IPC 2022. Though they have better access to services, displaced populations in IDP camps experience a higher rate of poverty and significant human capital deficits compared with residents of urban areas. More than 2 million people have been displaced since 2013 (OCHA 2022). The most significant driver of internal displacement is the desire for security. Most IDPs were forced to leave their original homes primarily because of armed conflict, which accounts for 79 percent of IDP households. Consequently, in selecting a camp location, IDPs tend to assign precedence to security rather than other factors, such as humanitarian assistance. More than 90 percent of IDP households chose their current camps because of the security they provided (World Bank 2019). The IDP camps in South Sudan are all located in highly urbanized areas (map 3.5). IDP camp households have nearly Map 3.5. IDP camps: travel times to urban universal access to improved drinking water areas with populations > 50,000, minutes sources and exhibit higher rates of improved sanitation facilities, although their access to electricity is relatively limited compared with urban residents (figure 3.7). Despite the much better access they possess to services relative to rural households, the poverty rate is lower among IDP camp dwellers (71 percent) than among either rural or urban residents (World Bank 2022b). The heads of IDP camp households are also much less likely than urban residents to have completed primary education. These patterns all appear to be largely consistent with the findings of an earlier study using data Sources: 2022 HBS; 2021 data of FSNMS (Food Security and from 2017 (World Bank 2019). The gaps persist Nutrition Monitoring System) (dashboard), Food Security in a comparison between IDP camp households Indicators Data, CLiMIS, Juba, South Sudan, https://climis- and nearby urban households. Urban forced southsudan.org/fsi/data. Note: Travel times (in minutes) were calculated based on the displacement brings forth difficulties, such as friction map provided by Weiss et al. (2018, 2020). Chapter 3. Shocks and Resilience 66 the immense strain on urban land, housing, basic infrastructure, services, and social dynamics that local and national governments must address. Figure 3.7. Poverty and access to basic services, by location 10 Share of people 8 6 Rural 4 Urban 2 IDP camp 0 Primary Improved Improved Electricity Poverty education sanitation water Sources: 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 3.2. PROFILE OF THE INDIVIDUALS AND LOCATIONS MOST VULNERABLE TO SHOCKS The 10 states do not face shocks of the same intensity or the same shocks. The share of households that experienced at least one shock is the largest in Northern Bahr el Ghaza, but smaller in neighboring Western Bahr el Ghazal, the state with the lowest incidence of shock, though the share was still around 55 percent. Households must often contend with multiple shocks. The mean number of shocks facing households ranged from 1.0 (Western Bahr el Ghazal) to 2.0 (Northern Bahr el Ghaza). Western Bahr el Ghazal was an outlier, with a markedly lower incidence of shocks (figure 3.1). The shocks differed across regions. High food prices were more prevalent in Northern Bahr el Ghazal (43 percent), while flooding was the main shock in Unity and Jonglei (51 percent and 73, respectively). Households in Central Equatoria were more likely than households elsewhere to face insecurity, including violence, raiding, and looting. Figure 3.8. Percentage of households subject to one shock or more in the last six 6 months, by quintile and location 26 29 30 32 35 28 45 57 Experienced at 74 71 70 68 65 72 least one shock 55 43 No shocks Poorest 2nd 3rd 4th Richest Urban Rural Camp Expenditure quintile Area of residence Source: Calculations based on 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 67 The poorest households were more likely than the richest households to undergo shocks (74 percent versus 65 percent). The lived experience in South Sudan suggests that poverty is induced by shocks. Communities in flood-prone areas, for example, endure chronic poverty because of recurring exposure to shocks. For these communities, shocks contribute substantially to the poverty, and the lack of resources to apply lower-risk coping mechanisms leads to additional losses. Exposure to shocks tends to result from constraints on household choice. It is nonetheless extensive even among the richest households, underlining the pervasive nature of shocks and the vulnerability in South Sudan. Rural households are more likely than urban households to face shocks (see figure 3.8). 3.3. IMPACTS OF SHOCKS ON HOUSEHOLDS 3.3.1. SELF-REPORTED IMPACTS Shocks in South Sudan too often critically constrain the ability of households to obtain income or food (figure 3.9). Among households responding to the FSNMS survey, 63 percent reported that the shocks they had experienced in the previous six months usually reduced this ability. Another 27 percent reported that this ability was only slightly reduced. A small share of households (9 percent) said shocks had no impact on this ability to obtain income. Floods, insecurity, and droughts were more likely to result in large declines in the ability to get income or food. High prices led to a small decrease in this ability among around 30 percent of households. Figure 3.9. Households reporting impact of shocks on their ability to access income or food, percent High food prices 80 60 High prices of fuel/transport Flood 40 20 0 Reduced income of Insecurity/raiding/looting household members Drought/irregular rains No impact Small decrease in income/food Large decrease in income/food Other Source: Calculations based on 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. COVID-19 countermeasures such as lockdowns and travel restrictions caused substantial welfare losses among the South Sudanese. Many individuals stopped working temporarily or permanently during and in the aftermath of the crisis. A slim majority of households (52 percent) Chapter 3. Shocks and Resilience 68 lost all or part of their incomes in the months following the stringent lockdown. Households deriving their livelihoods from nonfarm activities were affected the most. Around 47.0 percent of these households experienced a decline in incomes, while another 12.5 percent completely lost incomes. Incomes from farming declined or dried up among, respectively, 38 percent and 11 percent of households. Food insecurity, which was already acute, rose in response to COVID-19. The crisis compelled many households to engage in extreme coping strategies, such as skipping meals or going without food for a day (Finn et al. 2020). The impacts of conflict on households in South Sudan are multifaceted and interrelated. Ongoing hostilities have forced millions of South Sudanese to flee their homes and seek refuge internally or across borders. Conflict has disrupted agricultural activities, leading to food production deficits and widespread food insecurity. Many households suffering from malnutrition and hunger are struggling to obtain necessities. The economy has been severely affected by conflict. The resulting output contraction has generated widespread poverty (Etang-Ndip, Hounsa, and Pape 2022). Conflict has also exacerbated ethnic rivalries, and this has large potential adverse effects on state building and social trust. 3.3.2. THE IMPACT OF FLOODS ON HOUSEHOLD WELFARE The analysis involved the estimation of the impact of flooding on household welfare by exploiting information about the timing of the floods in 2021 and the spatial differences in exposure to the floods (see annex G). The flood episode occurred from May to October 2021. It was a large- scale unexpected event and has been recorded as a major weather shock, affecting approximately 835,000 people.22 May to October not only corresponds to the rainy season in South Sudan, but also to important stages in the cropping cycle for most crops (planting, growing, harvesting) (table 3.1). Table 3.1. Indicative seasonal cropping calendar Mar Apr May June July Aug Sept Oct Nov Dec Jan Feb Rainfall Dry season Wet season Dry season Unimodal Main crop Land Growing season Harvest rainfall zone Long-cycle preparation crops and planting Growing season Harvest Dry Rainfall Wet season Dry season season Land Bimodal First crop preparation Growing season Harvest rainfall and planting zone Second Land Growing and third preparation Harvest season crops and planting Source: FAO 2015. 22 See EM-DAT (International Disaster Database), Centre for Research on the Epidemiology of Disasters, Institute of Health and Society, Université Catholique de Louvain, Brussels, https://www.emdat.be/. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 69 The 2021 flood led to a reduction in household consumption, confirming the household self- assessments of the impact of the flood on household income.23 Exposure to flooding caused a decline in household consumption of 6 percent and 13 percent in rural and urban areas, respectively. At the national level, the decline averaged 4 percent. While the effect on household consumption is statistically significant, it is not sufficiently enough to influence poverty status. Flooding has also led to a rise in food insecurity in rural areas (figure 3.10, panel a). Figure 3.10. The effects of flooding on households Urban Food insecurity Rural Poverty National -0.30 -0.20 -0.10 0.00 0.10 Log-consumption % of households -0.20 -0.10 0.00 0.10 0.20 0.30 Food insecurity Log-consumption % of households Source: Calculations using nationally representative surveys and administrative data. Note: Reference group, panel a: households in non–flood prone areas; panel b: agricultural households in non–flood prone areas. The shock had a larger impact on agricultural households than on households among which the main income source is not agriculture.24 Compared with agricultural households, nonagricultural households experienced a smaller decline in aggregate consumption, and the poverty impact of the flood shock was more acute among agricultural households (figure 3.10, panel b). This underlines that nonagricultural households are relatively more resilient than agricultural households to flood shocks. 23 Figure 3.10, panel a, illustrates the impact of floods on household well-being. The estimated coefficients come from a double difference model that assesses changes in outcomes for households living in areas heavily affected by the flood shock—from before to after the weather shock—compared with changes in outcomes for households living in less affected areas. Figure 3.10, panel b, depicts how the climate shock affected nonagricultural households and agricultural households differently. The coefficients come from a triple difference model. The model estimates how much larger the effect of the weather shock is on nonagricultural households compared with the reference group of agricultural households. The results indicate that nonagricultural households have suffered less than agricultural households in terms of consumption levels and poverty. However, there is no significant difference between agricultural and nonagricultural households in terms of the impact of the weather shock on food security (see estimation methodology in annex B). Two waves of representative household surveys collected before and after the flood episode of May–October 2021 were used to estimate the impact of the episode. Preflood data were drawn from the HFS, wave 3, which covered 1,848 households between September 2016 and March 2017. Postflood data were derived from 19,098 households interviewed between September 2021 and October 2021 during the FSNMS, round 27. Data collected closer to the 2021 flood event would have been preferable, but these did not exist. Both surveys include information on food insecurity and household sociodemographic characteristics, but lack comparable consumption data. Survey-to- survey imputation (SWIFT 2.0) was used to construct comparable consumption aggregates based on the 2022 HBS. Differences in the amount of exposure to the flooding are captured by residence in areas not prone to flood. The geoclimatic profile of South Sudan, combined with the history of flooding, provides a means to identify such locations. See FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data; HFS (High Frequency Survey 2016, wave 3, South Sudan, 2016–2017) (dashboard), World Bank, Washington, DC, https://microdata.worldbank. org/index.php/catalog/2914. 24 The analysis also examined the effects on households that depended on agriculture for income. First, a distinction was made between households relying on agriculture as the main source of income and households that derived their income mainly from nonagricultural activities. This helped test whether the impact of the shock was heterogenous with respect to sectoral income. Chapter 3. Shocks and Resilience 70 3.3.3. THE IMPACT OF CONFLICT ON FOOD INSECURITY Conflict has substantially increased food insecurity in South Sudan. The analysis estimated the impact of conflict on food insecurity by using a double difference approach, such as what was used to assess the impact of flood on household welfare (annex H). On average, conflict increased food insecurity by 6.5-9.7 percent (figure 3.11). Violent events that resulted in high casualties increased food insecurity by 6.5 percent, while violent events alone worsened food insecurity by 8.2 percent. Taken together these two forms of violence increased food insecurity by 9.7 percent. Figure 3.11. Impact of conflict on food insecurity (Proportion) Fatalities Violent events All events 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 Source: Calculations using nationally representative surveys and administrative data. 3.4. HOW ARE SOUTH SUDANESE COPING WITH SHOCKS? A change in dietary composition was the most common coping strategy. More than 60 percent of households had to rely on food that they did not prefer, but that was less expensive. Around 55 percent limited the size of portions at meals (annex I, table I.1). Around half of households reduced the number of meals in a day. Because most households experienced frequent exposure to shocks, sustained use of these coping strategies can have severe effects on nutrition, especially among young children. In encountering shocks, many South Sudanese have few coping strategies. Most typical strategies are not available. Over a third of households reported that asking for community help or sending household members to share a meal with other households were not options available to them (36 and 38 percent, respectively), and 38 percent of households did not have any seeds to sell. To reduce the impact of adverse shocks, households often rely on response behaviors rooted in social norms and reciprocity rules that provide implicit assurance mechanisms. Such behaviors include interhousehold transfers in cash or in kind. Around 26 percent of households sent members to other households at mealtime, and a similar share of households (26 percent) asked for help from community members. Some households relied on coping strategies that deplete productive capabilities (annex I, table I.1). But these mechanisms only provided limited protection, especially against shocks within communities. Frequent exposure to adverse shocks and hardship often have self-reinforcing detrimental effects on household welfare. Vulnerable households tend to suffer substantial negative impacts if shocks occur, pushing them deeper into hardship from which recovery is often difficult. Actions taken by vulnerable households to cope with or guard against risk often exacerbate the vulnerability of the households, reducing their ability to cope with future shocks. For example, in facing adverse REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 71 shocks, many low-income households draw down on their productive assets, such as seeds or livestock, compromising their income-generating capacity. Following the livelihood coping strategy index approach, table 3.2 groups household coping strategies in the face of food scarcity into three categories according to the seriousness of the consequences, that is, stress, crisis, and emergency.25 Table 3.2. Ten livelihood coping strategies–food security, by residential area Residential area Strategies Urban, camp Rural Send household members to eat with Send household members to eat with 1 another household because of a lack of another household food or money to buy food Sell more animals than usual for this Sell more animals than usual for this 2 Stress time of year or spend savings time of year Borrow money or purchase food on 3 Borrow cash credit Gather foods in the wild more than 4 Sell household assets, goods normal Reduce expenses on goods for resale Ask other community members for 1 or on business or petty trade or support in food agricultural inputs, and so on Reduce expenditures on health Send more household members than Crisis 2 (including drugs) and education normal to cattle and/or fishing camps. Sell productive assets or means Sell or eat seeds intended for planting 3 of transport (sewing machine, this season wheelbarrow, bicycle, car, and so on) Sell house or land or sell or slaughter the Sell or slaughter the last of your cows 1 last of your cows and goats and goats Travel back to the village or out of town Travel to another village to look for 2 to look for or search for (begging) food or search for (begging) food or other Emergency or other resources resources Use community leaders or a local court to collect debts or bride wealth Engage in illegal income activities (theft, 3 and dowry, or engage in illegal income prostitution) activities (theft, prostitution) 25 Ten coping strategies are identified, six of which are common across rural, urban, and camp settings, while four are unique. Stress coping includes strategies, such as sharing meals, selling animals, borrowing cash, and selling assets (and rural households also gather foods in the wild). Crisis coping in urban and camp contexts involves reducing expenses and selling assets. In rural areas, coping includes seeking community support, engaging more household members in cattle raising or fishing, and using seeds for consumption. Emergency coping entails selling property, slaughtering cows, traveling to beg for food, and engaging in illegal income-generating activities. Chapter 3. Shocks and Resilience 72 Over half of South Sudanese households employ emergency coping strategies in the face of shocks. Among households, 70 percent employ crisis (15 percent) and emergency (55 percent) coping strategies, highlighting the concerning trend of diminishing future productivity. In contrast, urban (35 percent) and camp (29 percent) residents report lower reliance on these strategies. Camp residents exhibit the highest share (46 percent) among households using no coping strategies, underscoring the limited capacity of camp residents to undertake action. Households in the bottom four quintiles employ crisis and emergency coping strategies at a rate 10 percentage points higher relative to the top quintile (70 percent versus 60 percent). (figure 3.12). Figure 3.12. Livelihood coping strategies, by location and wealth quintile a. Location b. Wealth quintile 35 29 44 % of households 53 55 55 % of households 13 19 12 9 16 15 15 15 11 10 10 46 37 10 22 20 29 21 South Urban Rural Camp B80 T20 Sudan No Coping Stress Crisis Emergency No Coping Stress Crisis Emergency Source: Calculations based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. The eastern part of the country exhibits a greater prevalence of households resorting to emergency coping strategies. Among states, Jonglei (68 percent) and Upper Nile (63 percent) have the highest shares of households employing emergency coping mechanisms. Northern Bahr el Ghazal also shows a notable rate: 62 percent of households rely on emergency coping strategies. Among the livelihood zones, more than 70 percent of households in the southeastern semiarid pastoral zone (SS05), eastern plains sorghum and cattle zone (SS06), and northeastern maize, cattle, and fishing zone (SS10) adopt emergency coping strategies. This pattern may be attributed to the persistent conflict in these areas, which weakens the ability of households to cope over time, compelling them to resort to irreversible coping strategies (map 3.6). REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 73 Map 3.6. Emergency livelihood coping strategies, by state and livelihood zone a. State b. Livelihood zone Source: Calculations based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. Households are increasingly finding that coping is challenging. The exhaustion rate—the share of households that have already depleted the means of using their coping mechanisms over the previous 12 months (Sassi 2021)—is high.26 Approximately 50 percent of households employing emergency coping strategies, particularly those involved in illegal activities or selling productive assets, have already exhausted these mechanisms. This suggests that their capacity to withstand future shocks is severely limited or entirely depleted. Households resorting to selling productive assets may negatively affect an already fragile household food system because these households rely heavily on agriculture and their own production (figure 3.13). Figure 3.13. The livelihood coping strategy exhaustion rate a. Urban and camps b. Rural Em_IllegalAct -47 17 Em_IllegalAct -48 22 Em_Begged -31 25 Em_Begged -34 39 Em_ResAsset -44 18 Em_ProdAsset -48 18 Crisis_ProdAsset -33 21 Crisis_Seed -28 40 Crisis_Health -20 34 Crisis_Cattle -37 26 Crisis_AgriCare -24 29 Crisis_Community -24 32 Stress_DomAsset -32 23 Stress_WildFood -27 44 Stress_BorrowCash -22 33 Stress_BorrowCash -22 39 Stress_Animals -37 20 Stress_Animals -36 36 Stress_EatOut -26 26 Stress_EatOut -26 34 -60 -40 -20 0 20 40 -60 -40 -20 0 20 40 60 Exhaustion Rate Share of HH using LCS Exhaustion Rate Share of HH using LCS Source: Calculations based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. 26 The exhaustion rate is the number of households that depleted the means of enacting a specific coping strategy, divided by the total number of households attempting to use or using that particular coping strategy. Chapter 3. Shocks and Resilience 74 3.5. THE LANDSCAPE OF SOCIAL PROTECTION The government and people of South Sudan have been obliged to grapple with pervasive vulnerability and an escalating need for assistance. Conflict, hyperinflation, and a currency crisis have combined to erode household welfare profoundly. The government began developing the National Social Protection Policy Framework in 2011. The main goal was to “respond to and address the multiple vulnerabilities faced by South Sudanese citizens, with a particular focus on the poorest and most excluded sectors” (World Bank 2018, v). The framework assigns the Ministry of Gender, Child, and Social Welfare with the responsibility for the strategic planning, coordination, development, and implementation of social protection programs. Key social sector actors, such as the Ministry of General Education and Instruction and the Ministry of Agriculture and Food Security, are required to integrate social protection into existing interventions and to design and implement adequate sectoral social protection programs. Despite this legal and institutional framework, the country still lacks a government-led national safety net that supports the most vulnerable. Social protection interventions have been almost exclusively led by humanitarian partners and overwhelmingly funded by donors. Fiscal year 2018/19 annual expenditure on social protection interventions in South Sudan was around US$117 million, of which 99.7 percent was provided by donor funding. Social protection program interventions are often fragmented and unpredictable and are not regularly guided by the priorities of the national framework. Poor coordination among the actors generates inefficiencies and compromises sustainability. There is a clear need to improve mechanisms to identify social protection beneficiaries through data-driven approaches, such as proxy means testing. Ongoing efforts to strengthen resilience and improve food security have centered on particular interventions. These include (un)conditional cash transfers, food for assets, microfinancing, food and nutrition assistance, livelihood opportunities, and improved agricultural inputs or equipment (for example, agricultural quality seeds and tools). Most interventions are, however, constrained by coverage gaps because of insecurity and the difficulty of reaching conflict-affected and remote areas. A view on the uptake of such interventions was provided by the 2021 FSNMS survey, which asked households if they had received any assistance from any among a long list of ongoing social protection programs. Figure 3.14. Three main programs among household respondents, by location and welfare quintile, percent 92 55 29 28 28 29 30 35 30 Urban Rural Camp Poorest 2nd 3rd 4th Richest Area of residence Expenditure quintile All General food for all Food for assets Agricultural inputs Source: Calculations based on 2022 HBS; 2021 data of FSNMS (Food Security and Nutrition Monitoring System) (dashboard), Food Security Indicators Data, CLiMIS, Juba, South Sudan, https://climis-southsudan.org/fsi/data. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 75 The direct distribution of food aid is a major form of assistance. Around 30 percent of the surveyed households in 2021 were beneficiaries of the food-for-all intervention. Most camp-based households received direct food aid. Households in rural areas were the least likely to benefit from this intervention. Food assistance represents a critical intervention in most conflict-affected and remote areas. Food assistance beneficiaries are most numerous in the state of Unity (62 percent), followed by Jonglei (58 percent) and Lakes (39 percent). The targeting of the food-for-all program might require improvement. Currently, the share of program beneficiaries increases, the wealthier the consumption quintile. Nationwide, the food-for-assets intervention has benefited 4 percent of households. The largest uptake is in the Lakes (7 percent). Programs that widen access to high-quality seeds have reached 7 percent of households. These programs represent the second most common intervention according to household respondents in the 2021 FSNMS survey. The largest share of recipients was in Central Equatoria and Eastern Equatoria (18 and 17 percent, respectively). Chapter 3. Shocks and Resilience 76 CHAPTER 4. POLICY CONSIDERATIONS Photo: © Mayak Akuot / FAO 77 The Poverty and Equity Assessment finds that, due to fragility and elite capture, South Sudan has consistently failed to improve the living standard of its population. As a result, conflict and violence, poverty and food insecurity are endemic. Therefore, the challenge facing policymakers in South Sudan is to design and implement, on a sustainable basis, incentive-compatible policy interventions that move the country out of fragility to prosperity and resilience for all. The World Bank Group’s strategy for poverty reduction focuses on promoting inclusive and broad-based growth and fostering economic and environmental resilience to reduce vulnerability to shocks27. It is argued here that institutional reforms and human capital development are critical for South Sudan to leverage its natural endowments, lay the foundations for inclusive and broad-based growth, and support its exit from chronic fragility. The achievement of broad-based economic growth calls for an expansion of livelihood opportunities. Fostering resilience requires interventions to increase the capacity of the economy, communities and individuals to cope with and recover from the impacts of shocks. It also calls for the development of appropriate risks management mechanisms. Finally, effective policymaking is based on credible evidence. Thorough analysis, high-quality data and statistics contribute to credible evidence that informs policymaking. The ability of a country to base policymaking on credible evidence depends primarily on how well it succeeds in developing and utilizing the capacity of its National Statistical System (NSS)28. The Revised National Development Strategy for 2021-2024 has acknowledged that the statistical system of South Sudan has limited capacity to meet their statistical needs for development policymaking. Statistical capacity development is therefore an important policy consideration is South Sudan. Addressing Conflict and Inadequate State Capacity to Improve Livelihoods To move out of fragility and promote prosperity and resilience for all, South Sudan needs to address the drivers of conflicts and violence and build inclusive institutions that work for the common good. Institutions are social arrangements designed to control and coordinate the behavior of participants in the life of society through explicit and implicit rules of the game of social interaction. These rules ultimately determine the degree of access that people have to resources, and their ability to transform such resources into wellbeing. 27 World Bank Group. 2019. Guidelines on Poverty and Equity Assessments (Revised March 22, 2019). Washington, DC: The World Bank Group. 28 B. Essama-Nssah. 2023. Theory of Change for Statistical Capacity Development. Unpublished. Washington, DC: The World Bank Group. Chapter 4. Policy Considerations 78 Institutions cannot work for the common good in a society where the state apparatus is viewed as resource to be exploited by those who can control it. A credible commitment to peace and security is essential for breaking out of fragility and building institutions. Ultimately, getting out of fragility is a step-by-step process that requires building checks and balances to restrain those who control the power of the state, and building a sense of common purpose to achieve long-term mutually beneficial outcomes. In the case of South Sudan, the critical first step is to get the current elites to credibly commit to peace and security. Recognizing that weak institutions and recurring cycles of conflict have prevented the country from attaining its full potential, the 2022 CEM argues that “addressing the underlying causes of the conflict, and restoring peace and stability in line with the provisions in the Revitalized Agreement for the Resolution of Conflict in South Sudan (R-ARCSS) is an absolute prerequisite for recovery, resilience, and long-term growth”. The R-ARCSS does provide the fundamental blueprint for institutional building and economic reforms in South Sudan. It outlines the economic and public financial management (PFM) architecture to ensure the functionality of the requisite institutional, legal and policy frameworks29. The report concludes that addressing the drivers of fragility, conflict, and violence and inadequate state capacity is critical for improving the livelihoods of conflict-affected people. As highlighted by the 2023 SCD Update, the root causes of insufficient development progress are largely internal factors that can be grouped into two clusters. The first cluster includes the underlying causes of persistent subnational conflict and violence. The second is the persistent mismanagement and misappropriation of South Sudan’s abundant natural capital, most notably, oil. The combination of these two factors is the driving force behind underinvestment in natural, human, and physical capital in South Sudan. It has fueled extreme dependence on aid, an artificial scarcity of resources to finance the delivery of basic services and to maintain government functions, competition over rent extraction, and a broken social contract (World Bank 2024). Promoting justice, security, and governance across economic sectors is essential to escaping the fragility trap. This will also create the conditions for the South Sudanese population to build livelihoods and enhance the provision of essential services to the population. Invest in Human Capital Development The inadequate investment in health and education sectors has critically impeded the growth of human capital in South Sudan. Limited access to education and health services in South Sudan significantly hampers human capital development, as evidenced by the country’s Human Capital Index (HCI) score of 0.31, one of the lowest in the world. This means a child born today will only achieve 31% of their potential productivity as an adult. Government spending on health and education is low, with donors being the primary funders. The young population, with 74% under 30 and 41% under 15, presents an opportunity for economic growth if investments are made in their human capital and skills development. However, poor human capital outcomes currently constrain the country’s productivity and economic potential. 29 World Bank Group. 2022. Directions for Reform: A Country Economic Memorandum for Recovery and Resilience in South Sudan. Washington, DC: The World Bank Group. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 79 Health To develop and protect human capital in South Sudan, policymakers must improve health, nutrition, and education outcomes. The country faces severe health challenges, including the highest neonatal and maternal mortality rates globally, widespread malnutrition, and limited access to health facilities. Poor water, sanitation, and hygiene (WASH) services further constrain human capital, with only 10 percent of households having access to sanitation and over 60 percent using unimproved water sources. Many schools lack drinking water and sanitation facilities, negatively impacting learning outcomes. Addressing these issues requires expanding water services, increasing sustainable access to groundwater, and promoting climate-resilient solutions. Additionally, the health system suffers from chronic underfunding, inadequate infrastructure, supply shortages, and a lack of trained health workers, necessitating significant investment and capacity building. To address the critical health challenges in South Sudan, it is recommended that the government significantly increase its budget allocation and spending for health to meet the 15 percent target pledged by African Union countries. This funding should be directed towards improving health infrastructure, ensuring regular supply of medical essentials, and training more health workers to meet WHO standards. Additionally, expanding water and sanitation services, particularly in rural areas, and promoting climate-resilient solutions are essential. The South Sudan Health Sector Transformation Project (HSTP) can play a pivotal role in these efforts by expanding access to a basic package of health and nutrition services, strengthening health sector stewardship, and improving overall health system capacity. This project aligns with the South Sudan Health Sector Strategic Plan (HSSP 2023-2027) and can help achieve sustainable improvements in health outcomes. Education Learning outcomes in South Sudan are extremely poor, with 62 percent of primary school-aged children out of school and high illiteracy rates. The education system struggles with inefficiency, high dropout rates, untrained volunteer teachers, low and irregular salaries that deter qualified individuals from teaching, and a severe shortage of teaching and learning materials. Schools are unevenly distributed, with flooding further exacerbating access related challenges. Funding of the sector is inadequate and remains one of the biggest drawbacks to building a viable human capital. Education budget both as a percentage of GDP or total government expenditure is significantly low and has consistently failed to meet the minimum education financing international benchmarks of 4-6% of GDP or at least 20% of total government expenditure. The National General Education Policy, aligned with Vision 2040, aims to provide quality education for all by eradicating illiteracy, promoting lifelong learning, gender equity, personal development, national unity, and improving education quality through robust inspection programs. To address the severe educational challenges in South Sudan, it is recommended that the government increase the budget allocation for education to meet the legal requirement of at least 10 percent, while gradually increasing to meet international education financing benchmarks. This increased funding should be directed towards improving teacher salaries and ensuring regular payments to attract and retain qualified educators. Additionally, investment in teacher training programs is crucial to reduce reliance on untrained volunteers. Efforts should also Chapter 4. Policy Considerations 80 be made to ensure equitable distribution of schools and adequate provision of teaching and learning materials including textbooks for the new competency-based curriculum, particularly in secondary and upper primary classes. Implementing robust inspection programs and focusing on foundational skills like literacy and numeracy will further enhance the quality of education, aligning with the goals of the National General Education Policy and Vision 2040. Improve Food Security This report underlines the dire and deteriorating food insecurity situation in South Sudan. Food insecurity is driven by several factors, including low food production, which has resulted in reliance on food purchases from the market instead of self-production. Poor road networks, the lack of access to transport, and low market integration play crucial roles in the relatively high food prices and food availability. Insecurity impedes not only the movement of the population to access the market (inputs and staples), but also the movement of goods, leading to food deficits and price increases. Conflict, intercommunal violence, and climate shocks induce populations to flee their homes and search for protection in other places. Households adapt to these instabilities by planting crops that take the shortest time to grow, especially vegetables. To tackle the severe food insecurity in South Sudan, a comprehensive strategy is necessary. Boosting local food production by adopting better agricultural techniques and developing infrastructure is vital. Improving road networks and transportation systems will enhance market access and lower food costs. Furthermore, ensuring safety to enable the free movement of people and goods is crucial. Mitigating climate shocks through resilient farming practices and effective water management will lessen the effects of floods and droughts. Joint efforts from the government, development partners, Non-Governmental Organizations (NGOs), and local communities are essential to establish a sustainable and inclusive food security plan. This strategy should be integrated with existing initiatives like the Comprehensive Agriculture Master Plan (CAMP) to ensure cohesive and efficient execution. Expand Livelihood Opportunities South Sudan’s economy, dominated by oil and agriculture, lacks diversification, limiting livelihood opportunities and making it vulnerable to global commodity price and climate shocks. Diversification is essential for economic growth and resilience, requiring efficient resource allocation to higher productivity sectors. Addressing governance challenges in the oil sector can improve its development impact, while the agricultural sector, with its vast potential, can enhance food security and diversify the economy. Currently, only a small fraction of arable land is cultivated, but increasing this could transform food production and export potential. Improving irrigation, access to agricultural inputs and technologies, and addressing land tenure issues are critical for agricultural development. The livestock sector also offers opportunities for enhancing food security and farm incomes, despite facing significant challenges. REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 81 Building Resilience to Climate Shocks and Disasters The report shows that climate shocks and disasters have had an aggravating effect on the already alarming deprivation among the population of South Sudan. South Sudan is highly vulnerable to climate change and faces significant water-related risks such as droughts and floods, impacting food security and livelihoods. Effective interventions must consider the type of shocks and livelihood sources and require an improved understanding of the disaster risk dynamics, including (a) the changing recurrence and intensity of climate shocks, (ii) the migration, displacement and settlements patterns underlying the evolving exposure landscape, and (iii) the social and physical determinants that define the different levels of vulnerability to climate shocks and disasters. Building resilience to climate shocks is a cross-cutting policy agenda, including water and flood management, agriculture, health, disaster risk management, and land use planning, among others. Related interventions include investments in flood protection, water storage, climate-smart agriculture, and improvements in early warning and hydro-meteorological services, and emergency preparedness and response. At the household level, the ability to adequately prepare for and respond to climate shocks is influenced by access to resources and social support, with household assets being crucial for escaping poverty. Social protection, largely driven by humanitarian efforts, is thus essential for coping with shocks. Adaptive safety nets and index-based insurance can help build resilience and support poverty alleviation through cash or in-kind transfers, addressing credit, savings, and liquidity constraints for the most vulnerable households. Develop Statistical Capacity Both the 2022 CEM and the 2023 SCD Update have noted that South Sudan faces significant data challenges that severely constrain policymaking in that country. Effective policymaking requires credible evidence based on proper analysis of high-quality data and statistics. Public intent data are data collected with the intent of serving the public good by informing the policy process that entails: (i) the identification of societal problems that deserve the attention of public authorities, (ii) the design and implementation of effective interventions aimed at those problems, (iii) monitoring, and evaluation of the effects of those interventions on the target populations. Such data can improve lives by contributing helping improve policymaking and service delivery, prioritize allocation of scare resources, and hold governments accountable while empowering citizens to make better decisions through accurate information and relevant knowledge30. The data challenges facing South Sudan are reflected in data gaps that are commonly observed in low-income countries, namely: (i) inadequate coverage, (ii) low quality and (iii) limited usability31. Only limited official statistics have been compiled in South Sudan since 2015. The available population census is significantly outdated. This undermines the foundation of the statistical system and limits its role in shaping political representation and resource allocation. The production of nationally representative microdata is infrequent and poorly harmonized. The 2022 Household Budget Survey (HBS) which is the first and most recent nationally representative 30 World Bank Group. 2021. World Development Report 2021: Data for Better Lives. Washington, DC: World Bank Group. 31 World Bank Group (2021). Chapter 4. Policy Considerations 82 household consumption survey since independence is based on a small sample size, only 719 households, even though it covers all 10 states. This small sample size makes poverty and inequality analysis at the subnational level significantly unreliable. There is no well documented official GDP figure for South Sudan. There has been no national labor force survey. There is no business census and agricultural data is lacking. In the critical area of human capital development, there is no information on learning outcomes, and on health and nutrition. There is no social registry that would potentially enhance cross-sectoral response to shocks in South Sudan. The information on countrywide coverage of transport infrastructure network is outdated. The weaknesses of the hydrometric network combined with a lack of a hydrometeorological telemetry system limit the design and implementation of reliable system for managing water-related risks. The few macroeconomic data produced by the National Bureau of Statistics and the Bank of South Sudan are usually delivered with significant delays and are of poor quality. A lack of budget transparency also affects the quality of fiscal data. In the face of these data gaps, the Revised National Development Strategy for 2021-2024 has acknowledged that the statistical system of South Sudan has limited capacity to meet the statistical needs for development policymaking. The root causes of such gaps are essentially deficiencies in financing, technical capacity, governance and demand for data32. No budget has been allocated to the National Bureau of Statistics for statistical operations since 2013. Budget allocations have been approved to cover salaries and operations at the institution, but these have also been delayed. The National Bureau of Statistics lacks qualified staff to support data production. The accumulation of salary arrears affects the commitment of staff to routine work at the Bureau, leading to high turnover among the uniquely qualified technical staff. The National Bureau of Statistics, the core institution of South Sudan’s statistical system, also lacks minimum physical infrastructure, equipment, and healthy working conditions. These poor working conditions, combined with the low salaries and the nonpayment of salaries, limit the productivity and motivation of staff. Finally, there is a lack of coordination among Ministries, Departments, and Agencies (MDAs) and other producers of official data33. To address the statistical needs of the country, the government adopted in 2013 the National Strategy for Development of Statistics (NSDS) 2014–2019 to serve as a coordination framework and strategic business plan. The main strategic objectives of the NSDS include: (i) strengthening the legal and institutional framework, (ii) improving human resource (HR) capacity for production and management of statistics, (iii) developing statistical infrastructure, (iv) developing and managing data, and (v) improving physical infrastructure and equipment. To support the implementation of the NSDS, the World Bank Group financed a SDR 6 million Statistical Capacity Building Project (P144139) which became effective in February 2015 and closed in June 2020. The project was designed to focus on making statistical data easier to find and use34. The project consisted of five components at appraisal: (i) legal reforms and institutional 32 World Bank Group (2021). 33 World Bank Group. 2020. Implementation Completion and Results Report IDA-55370 on a Credit in the Amount of SDR 5.9 million (US$ 9Million Equivalent) to the Republic of South Sudan for the Statistical Capacity Building Project. Washington, DC: The World Bank Group. 34 World Bank Group (2020) REPUBLIC OF SOUTH SUDAN - POVERTY AND EQUITY ASSESSMENT 83 development; (ii) Human resource (HR) development and training; (iii) Information and Communication Technology (ICT) and statistical infrastructure development; (iv) data development and dissemination; and (v) project management. The overall outcome was rated moderately satisfactory. Furthermore, the development outcomes were still facing the same key risks identified at the design stage. These risks relate to: (i) inter- communal violence and macroeconomic challenges, (ii) sustained government funding, (iii) commitment of the MDAs, (iv) weak management and implementation capacity of the NBS and MDAs, (v) lack of recognition across the NSS of the coordination role of the NBS. This assessment suggests that more remains to be done to ensure that the NSS in South Sudan has the capacity to produce, disseminate, and use valuable data. Chapter 4. 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