Livelihoods in Sudan Amid Armed Conflict Evidence from a National Rural Household Survey A joint socioeconomic impact assessment by: the International Food Policy Research Institute (IFPRI) and the United Nations Development Programme (UNDP) ii EXECUTIVE SUMMARY Analysis of a comprehensive survey of Sudanese rural households conducted from November 2023 to January 2024 by IFPRI and UNDP reveals significant socioeconomic impacts of the ongoing armed conflict on the Sudanese population, underscoring the need for immediate and targeted policy and programmatic interventions. The conflict has severely disrupted rural household incomes and exacerbated existing vulnerabilities related to their housing and access to infrastructure and services. Most households live in inadequate housing conditions, with disparities in access to water, electricity, and sanitation services posing additional challenges. Rural households’ low access to assets, including agricultural land, further complicates their livelihoods. The conflict, primarily concentrated in urban areas, particularly Khartoum, has triggered mass migration, with significant numbers relocating to states like Aj Jazirah and Gedaref. These migrants, often from relatively better-off backgrounds, face substantial income losses, necessitating basic needs support and enhanced provision of public services, particularly for the large families that are more likely to migrate. Agriculture, a critical sector for rural livelihoods, has been significantly affected across all states. Most households reported not cultivating land during the summer season of 2023 due to the conflict. The sharp reduction in the area of crops planted underscores the need for support for farming activities, particularly for smallholder households. The survey highlights extensive exposure to shocks among rural households, with personal shocks, such as illnesses among household members, being the most common. Natural and climatic shocks, although less prevalent, alongside conflict-related shocks, like theft and violence, emphasize the complex challenges faced by these communities. Market access and disruptions have further impacted rural households, with a considerable proportion of rural households unable to sell or buy goods, primarily due to high prices and sharp reductions in income for most households. These market challenges, coupled with the overall economic instability, necessitate interventions aimed at maintaining and improving market accessibility and functionality to promote recovery and resilience. The findings from the analysis of the survey data lend support to designing and implementing comprehensive strategies that address the immediate needs of displaced populations and other rural households affected by income losses and market disruptions. Enhancing public services, supporting livelihoods, building resilience through shock-responsive social protection systems, agricultural and economic interventions, and ensuring equitable access to resources and markets for all households, particularly those headed by women and vulnerable groups, are the principal policy recommendations that emerged from this analysis. This study of rural household livelihoods amid the armed conflict in Sudan provides a foundation for targeted interventions and policy reforms aimed at mitigating the conflict’s impacts and fostering long-term resilience and economic stability. iii CONTENTS Executive Summary ............................................................................................................. i 1) Introduction ..................................................................................................................... 1 2) Methodology .................................................................................................................... 4 2.1 Survey design and sample size determination ........................................................ 4 2.2 Sampling strategy ................................................................................................... 5 2.3 Enumerator training and data collection ................................................................. 5 2.4 Implementation challenges ..................................................................................... 5 3) Demographic Profile and Migration Dynamics ............................................................. 7 3.1 Demographic characteristics of the respondents .................................................... 8 3.2 Residential changes and migration ........................................................................10 4) Economic Resilience .....................................................................................................18 4.1 Main sources of income .........................................................................................18 4.2 Challenges faced in income-generating activities ..................................................24 5) Food Security and Coping Mechanisms .......................................................................27 5.1 Food security situation ...........................................................................................27 5.2 Coping mechanisms to maintain livelihoods ..........................................................31 6) Household and Agricultural Assets ..............................................................................35 6.1 Housing type and tenure .......................................................................................35 6.2 Households’ access to services .............................................................................38 6.3 Ownership of assets ..............................................................................................43 6.4 Ownership of agricultural land ...............................................................................45 7) Market Access and Disruptions ....................................................................................50 7.1 Market access .......................................................................................................50 7.2 Main challenges related to selling or buying goods ................................................53 8) Exposure to shocks .......................................................................................................56 8.1 Types and frequency of shocks experienced by households .................................56 8.2 Shifts in the level of physical security across states ...............................................57 8.3 Types of shocks vis-à-vis household characteristics ..............................................59 9) Conclusions and Implications .......................................................................................62 9.1 Conclusions ...........................................................................................................62 9.2 Implications and recommendations .......................................................................62 References ..........................................................................................................................64 About the authors ..............................................................................................................67 Acknowledgments ..............................................................................................................67 iv TABLES Table 2.1 Share of Sudan’s population and number of sample households by state ............. 4 Table 3.1 Main demographic characteristics of rural households ........................................... 8 Table 3.2 Destination state for households that migrated from the ten states with highest levels of migration reported, percent of all households .......................................12 Table 5.1 Household food security status based on raw Food Insecurity Experience Score (FIES) and Rasch Model estimates, by state ......................................................28 Table 6.1 Other sources of drinking water ............................................................................39 Table 8.1 Number of shocks households reported experiencing ...........................................57 FIGURES Figure 3.1 Education level of household heads, by employment status ................................. 9 Figure 3.2 Education level of household heads, by state ......................................................10 Figure 3.3 Households that migrated to another state since April 15, 2023 ..........................11 Figure 3.4 Propensity to migrate out of state after the conflict began, by household characteristic ......................................................................................................13 Figure 3.5 Pre-conflict income by migration status and pre-conflict state of residence .........14 Figure 3.6 During the conflict income by migration status and pre-conflict state of residence ...........................................................................................................................15 Figure 3.7 Patterns in changes in income from before to during the conflict, by migration status .................................................................................................................15 Figure 3.8 Within state migration, by current state of residence ............................................16 Figure 3.9 Propensity to migrate within state after the conflict began, by household characteristics ....................................................................................................17 Figure 4.1 Main sources of income before and during the conflict ........................................19 Figure 4.2 Employment transitions after the eruption of the conflict, percent of households that experienced them ........................................................................................20 Figure 4.3 Patterns in changes in income from before to during the conflict, by current state of residence .......................................................................................................20 Figure 4.4 Mean annual income by occupation before and during the conflict ......................21 Figure 4.5 Per capita annual income before and during the conflict, nominal Sudanese pounds, by current state of residence .................................................................22 Figure 4.6 Patterns in changes in income from before to during the conflict, by education level of the household head................................................................................23 Figure 4.7 Proportion of households who reported that their farming work was disrupted, by state ...................................................................................................................24 Figure 4.8 Reason for the disruption to farming work, percent of all households ..................24 Figure 4.9 Challenges reported in generating incomes from crop production, livestock production, and wages .......................................................................................25 v Figure 5.1 Household food security status based on Rasch Model estimates of Food Insecurity Experience Score (FIES), by state .....................................................29 Figure 5.2 Food Insecurity Experience Scale (FIES) categories, by sex of household head .30 Figure 5.3 Food Insecurity Experience Scale (FIES) categories, by patterns in changes in income from before to during the conflict ............................................................30 Figure 5.4 Food Insecurity Experience Scale (FIES) categories, by whether household reported experiencing a shock ...........................................................................31 Figure 5.5 Food Insecurity Experience Scale (FIES) categories, by whether household reported being victimized by violence .................................................................31 Figure 5.6 Number of livelihood coping strategies reported employed by household, by state ...........................................................................................................................33 Figure 5.7 Number of livelihood coping strategies reported employed by households, by sex of household head ..............................................................................................33 Figure 5.8 Number of livelihood coping strategies reported employed by households, by Food Insecurity Experience Scale (FIES) category of household .......................34 Figure 6.1 Type of current dwelling of households ................................................................35 Figure 6.2 Proportion of households who live in inadequate houses, by household head characteristics and local conflict intensity ...........................................................36 Figure 6.3 Proportion of households who report they own their dwelling, by household head characteristics and local conflict intensity ...........................................................37 Figure 6.4 Adequacy of housing quality, by education level of household head ....................37 Figure 6.5 Average number of persons per room, by household head characteristics and local conflict intensity..........................................................................................38 Figure 6.6 Current main source of drinking water .................................................................39 Figure 6.7 Proportion of households who have access to improved source of drinking water, by household head characteristics and local conflict intensity ............................40 Figure 6.8 Proportion of households with in-dwelling water network connections, by household head characteristics and local conflict intensity .................................41 Figure 6.9 Types of toilet facility households normally use ...................................................41 Figure 6.10 Proportion of households with improved toilet facilities, by household head characteristics and local conflict intensity ...........................................................42 Figure 6.11 Sources of electricity .........................................................................................42 Figure 6.12 Proportion of households who report having no access to electricity, by household head characteristics and local conflict intensity .................................43 Figure 6.13 Ownership of household assets .........................................................................44 Figure 6.14 Ownership of household assets, by sex of household head ...............................44 Figure 6.15 Ownership of household assets, by educational status of household head ........45 Figure 6.16 Reported ownership of agricultural land, by sex of household head and state ...45 Figure 6.17 Size of agricultural land owned, by state ............................................................46 Figure 6.18 Area of land cultivated in the last season, by state ............................................47 Figure 6.19 Main crops planted ............................................................................................47 vi Figure 6.20 Most significant challenges experienced regarding crop production, by intensity of conflict locally .................................................................................................48 Figure 6.21 Households that reported their work in farming was disrupted, by state and local conflict intensity ..................................................................................................49 Figure 6.22 Reasons reported for the disruptions to farming ................................................49 Figure 7.1 Households reporting being always able to visit the market, by state...................51 Figure 7.2 Ability to visit the market, by patterns in changes in income from before to during the conflict ..........................................................................................................52 Figure 7.3 Households reporting being always able to visit the market, by perceived own level of security ..................................................................................................52 Figure 7.4 Marketing experiences since conflict began, by sex of head of household ..........52 Figure 7.5 Households reporting an inability to make sales, by state ....................................53 Figure 7.6 Reasons given by households for not being able to make sales ..........................54 Figure 7.7 Households reporting an inability to make purchases, by state ............................54 Figure 7.8 Reasons given by households for not being able to make purchases ..................55 Figure 8.1 Types of shocks reported experienced by households ........................................56 Figure 8.2 Conflict-related shocks experienced by households ............................................57 Figure 8.3 Perception of households of current security situation, by state ...........................58 Figure 8.4 Exposure to shocks, by sex of household head ...................................................59 Figure 8.5 Exposure to shocks, by household size ...............................................................60 Figure 8.6 Exposure to shocks, by household head education level .....................................60 Figure 8.7 Exposure to shocks, by household employment status before the conflict ...........61 1 1) INTRODUCTION On April 15, 2023, Sudan was thrust into turmoil as the Sudanese Armed Forces and the Rapid Support Forces engaged in armed conflict. This conflict started in Khartoum but rapidly extended to the Darfur and Kordofan regions before impacting every state in the nation to varying degrees. A year into the conflict, Sudan finds itself in one of the most significant crises on a global scale, with profound effects on its social and economic structures. This turmoil has disrupted the lives of countless individuals and communities, tearing apart the economic and social fabric that binds the nation. By mid-March 2024, casualties were staggering, with almost 15,000 people dead and over 27,700 injured (ACLED 2024, OCHA 2024). Displacement has reached a crisis point, with more than 8.5 million people displaced, 6.5 million of whom are within Sudan’s borders, marking the conflict in Sudan as the largest displacement crisis worldwide (UNHCR 2024). The majority of internally displaced persons (IDPs) come from Khartoum and Darfur, seeking refuge in numerous locations, but primarily within the Darfur Region and along the River Nile (OCHA 2024). Women, who comprise about 69 percent of IDPs, bear the brunt of the crisis, facing displacement, gender-based violence, and loss of livelihoods and lives (UN Women 2024). An estimated 24.8 million people are in dire need of humanitarian assistance in 2024, nearly half of whom are children (OCHA 2023). The conflict has not only resulted in a loss of life and displacement but has also devastated infrastructure, including healthcare and water supply systems, and disrupted essential services, such as education, electricity, and communications. Healthcare access is alarmingly low, with 65 percent of the population reportedly unable to access medical services and between 70 and 80 percent of health facilities non-operational due to the conflict (OCHA 2024). Education has also been hit hard, with nine out of ten displaced households indicating that educational services are no longer available in their areas of displacement (OCHA 2023). The conflict’s impacts extend beyond immediate human suffering to also affect poverty and livelihoods in the longer term by destroying assets, limiting access to essential services, and reducing the workforce through death or injury. This disruption of economic activities leads to unemployment, inflation, and the collapse of social safety nets, exacerbating the vulnerability of the population to poverty. The situation in Sudan may echo findings from other conflict-affected regions, where conflict aggravates the impact of economic shocks and undermines the capacity of households to cope with adversities, further entrenching poverty. For instance, in Afghanistan, D’Souza and Jolliffe (2013) observed that conflict exacerbates the impact of economic shocks, such as food price spikes, on vulnerable populations. Their findings underscored that higher levels of conflict are associated with larger declines in food security, as conflict limits households’ ability to cope with economic adversities. Similarly, Goodhand (2001) noted that conflict has a more severe impact on poverty than other external shocks, primarily due to the deliberate destruction of livelihoods. Chronic insecurity fosters chronic poverty, with impacts varying significantly across different demographics, including sex, age, ethnicity, and region. This variability underscores the multifaceted nature of conflict’s impact on poverty. The Rwandan genocide of the 1990s provides a stark example of how conflicts can reshape poverty dynamics across a country. Justino and Verwimp (2012) found that the violent events of the 2 1990s significantly impacted household poverty, particularly in provinces that were previously better off. The destruction of houses and loss of land were critical factors that increased the likelihood of falling into poverty, showcasing the direct ties between conflict-induced shocks and poverty. Justino (2009) further explored the relationship between conflict and household economic status, revealing an endogenous relationship where the poorer a household is at the conflict’s onset, the higher the probability of its participation in or support for armed groups. This finding emphasizes the vicious cycle between poverty and conflict, where vulnerability to poverty and violence increases the likelihood of households supporting armed groups, further destabilizing the region and perpetuating poverty. Households in conflict zones resort to various coping and livelihood strategies to survive, ranging from altering dietary intake to selling assets, borrowing money, engaging in new income-generating activities, migrating, or relying on community support and humanitarian aid. However, these strategies often have detrimental long-term effects, trapping households in cycles of vulnerability and dependency. For instance, in Western Bahr el Ghazal state in the Republic of South Sudan, to cope with food insecurity, households employed strategies that included sending members to eat elsewhere; engaging in fishing, hunting, or gathering wild foods; selling animals or assets; borrowing money; and even migrating entirely (Sassi 2021). In Afghanistan, women and men adopted different problem-solving options in the face of conflict—women were more likely to seek income-generation opportunities, while men focused directly on meeting their food security and housing needs in-kind (Cardozo, et al. 2005). This sex differentiation in coping strategies underscores the importance of considering diverse household roles and preferences when assessing adaptive strategies. The effectiveness of these coping strategies varies significantly across contexts. While some strategies may temporarily alleviate the impact of conflict on household welfare, they often have long-term negative consequences. For instance, selling productive assets or livestock can provide immediate relief but diminishes future income-generating potential, trapping households in a cycle of vulnerability and dependency (Ndip and Touray 2019, Sassi 2021). Moreover, strategies like reducing food intake, withdrawing children from school, or engaging in risky occupations can have detrimental effects on the health and the future educational progress of household members, further exacerbating poverty and vulnerability (Shemyakina 2022, Olanrewaju and Balana 2023). The conflict in Sudan poses a significant threat to food security, particularly in areas of the country economically reliant on agriculture (Ndip and Touray 2019). In Nigeria, households facing conflict-induced shocks resorted to negative coping strategies, such as consuming less nutritious food, which exacerbated the severity of food insecurity and deteriorated dietary diversity (Olanrewaju and Balana 2023). The studies highlight a critical insight—while coping strategies are essential to households for their immediate survival in conflict zones, they often compromise long-term well-being and resilience. The effectiveness of these strategies is contingent upon the severity and duration of the conflict, the initial socioeconomic status of the household, and the availability of external support, such as humanitarian aid. In contexts where conflict is protracted, as in South Sudan, the continuous reliance on coping mechanisms erodes households’ resilience, leading to a vicious cycle of food insecurity and conflict (Sassi 2021). This scenario contrasts with situations where conflicts are relatively short-lived or where significant external support 3 is available, as seen in some regions of Afghanistan and Gaza (before the current war), where households may better maintain their livelihoods and food security through adaptive strategies (Cardozo, et al. 2005, Brück, D’Errico and Pietrelli 2019). The interplay between conflict and food security is also a critical concern in Sudan, particularly in regions where agricultural output and supply chains form the backbone of local economies and sustenance. Violence and displacement are disrupting agricultural activities, reducing domestic food supply, and raising prices, while increasing vulnerability to further shocks, including those induced by climate change. Between October 2023 and February 2024, about 37 percent of the Sudanese population, or 17.7 million people, were driven into high levels of food insecurity classified as Integrated Food Security Phase Classification (IPC) Phase 3 or above (crisis or worse) (OCHA 2024). The supply of food has been driving up prices across Sudan and is likely to worsen in the next few months. A study done by IFPRI in 2023 showed that about one-third of the over 3,000 farmers surveyed were displaced from their locations. Most of the 40 percent who were unable to prepare for the planting season because of the conflict were not intending to plant later in the season. This was mainly due to the lack of finance for buying agriculture inputs or hiring labor, compounded by bad weather conditions and the poor quality of local seed varieties, among other factors (O. Kirui, et al. 2023a). Prices of cereals significantly increased between April and November 2023, especially in areas heavily affected by the conflict. Sorghum prices increased by 122 percent in the El Fula market in West Kordofan, and similar patterns were observed in several states, including those not directly affected by conflict (WFP 2023). Harvests in 2024 are projected to be significantly below average in the Darfur and Kordofan regions—these areas account for about 40 percent and over 80 percent of the national production of sorghum and millet, respectively. The anticipated lower harvests are likely to further increase grain prices compared to prices in 2023, possibly by double (OCHA 2024). This report, based on the Sudan Rural Household Survey 2023, which was conducted by IFPRI and UNDP, assesses the socioeconomic impact of the conflict on rural Sudanese households. Multiple dimensions of their livelihoods and welfare are examined, including their income and employment, food security, access to markets, household assets, and vulnerability to shocks. The findings underscore the comprehensive adverse effects of the conflict across various facets of Sudanese lives and livelihoods. The detailed analysis can inform targeted policy and programmatic interventions to support affected communities. The outline of this report is as follows. Chapter 2 describes the survey design, sampling strategy, and its implementation. Chapter 3 provides a demographic profile of rural households in Sudan based on the survey data. Chapter 4 examines household income and economic resilience; Chapter 5, food security; Chapter 6, household assets; Chapter 7, market performance and challenges; and Chapter 8, the incidence of various shocks on rural households. Finally, Chapter 9 presents a synthesis of the analyses and provides recommendations for policy and programmatic interventions. 4 2) METHODOLOGY This chapter describes critical elements of the design and implementation of the Sudan Rural Household Survey 2023. It elaborates on the sample design, sample determination, sampling strategy, enumerator training, data collection, and the adaptation strategies employed to address implementation challenges. 2.1 Survey design and sample size determination The Sudan Rural Household Survey 2023, conducted in the midst of a significant national conflict, employed a computer-assisted telephone interview (CATI) methodology to overcome data collection challenges in conflict settings. This approach ensures the continuation of research activities under crisis conditions through permitting a wider set of innovations to respond to and overcome barriers to research in conflict-affected regions. The CATI methodology is pivotal for its adaptability and potential for generating timely insights crucial for planning and response in dynamic contexts. CATI was appropriate for generating information from the survey households on their food security, coping strategies, employment and income, livelihoods, and exposure to shocks. The sample size for the survey was determined at 4,504 households to allow for the drawing of state-level and national inferences from the analyses. The sample was distributed across states based on their share of Sudan’s total population, ensuring a representative cross- section of the Sudanese populace (Table 2.1). This sample size allows for the detection of a 2 percentage point change in poverty incidence as being statistically significant. Table 2.1 Share of Sudan’s population and number of sample households by state Sudan’s population living in state in 2014, percent Sample households in state State number percent of total Khartoum 13.8 621 13.8 Central Darfur 1.8 81 1.8 East Darfur 3.0 135 3.0 North Darfur 7.4 333 7.4 South Darfur 7.6 342 7.6 West Darfur 3.3 149 3.3 North Kordofan 6.7 302 6.7 South Kordofan 2.8 126 2.8 West Kordofan 6.0 270 6.0 Sennar 3.9 176 3.9 Aj Jazirah 15.6 702 15.6 Blue Nile 3.9 176 3.9 White Nile 5.2 234 5.2 Northern 2.5 113 2.5 River Nile 4.0 180 4.0 Gedaref 5.1 230 5.1 Kassala 4.3 194 4.3 Red Sea 3.1 140 3.1 Total 100.0 4,504 100.0 Source: Authors’ compilation. Note: Sample households are those for which telephone numbers were obtained. The full sample was achieved after an extension of the survey period and repeated calls. 5 Being more heavily populated than other states, Aj Jazirah, Khartoum, South Darfur, North Darfur, and North Kordofan have the largest sub-samples by state. Over half of the survey respondents (51.1 percent) reside in these five states. 2.2 Sampling strategy The survey adopted a strategic approach to sample selection, leveraging a database of telephone numbers linked to previous Food Security Assessment Surveys of the World Food Programme (WFP). The WFP database included 29,724 telephone contacts located mainly across rural Sudan. A random stratified sampling method was employed, utilizing the states for stratification. Within each state, respondents were selected with equal probability, ensuring the representativeness of our sample at the state level. The survey’s sampling strategy involved selecting sample members from a list of households or individuals with telephone numbers. The distribution across states was based on their share of Sudan’s total population. The sampling frame was complemented by the data collection company’s telephone number database, particularly in states where the WFP database was insufficient. The survey company provided an additional 24,800 telephone contacts. This sampling approach was favored because it would allow for the utilization of WFP pre-conflict data for comparisons to the situation before the conflict broke out, where possible. Although WFP’s database is representative across localities and states, a caveat is needed here as we acknowledge the potential for sample bias that may result in exclusion from the sample of poorer households or households without telephone numbers. Hence, the results of some socioeconomic outcome variables might be underestimated or overestimated depending on the type of variables. For example, the survey may underestimate negative outcomes (from a development perspective), such as poverty and food insecurity, while overestimating positive outcomes, such as asset ownership or employment. 2.3 Enumerator training and data collection A team of 34 enumerators, along with two supervisors, underwent comprehensive training to prepare for the survey. Training focused on the principles of interviewing, professional and ethical standards, and an in-depth review of the survey instrument. The training was conducted virtually, emphasizing the use of Sudanese Arabic to align with respondent demographics. Data collection commenced on November 9, 2023, with enumerators employing the CATI application to facilitate efficient and accurate data entry. The application facilitated a seamless flow to the survey process, real-time data monitoring, and quality checks, ensuring the integrity and validity of the collected data. The process included mechanisms for respondent opt-in and scheduling, ensuring respectful and effective engagement. The survey was concluded in the first week of January 2024. 2.4 Implementation challenges The survey faced several challenges, including translation and cultural adaptation, data collection in remote areas, respondent displacement, incentivizing participation, and network instability. Each challenge was met with adaptive strategies, such as extending data collection periods, enriching the contact pool, and providing incentives to the respondents. 6 These efforts underscored the complex realities of conducting survey research in conflict- affected areas and the importance of flexibility and innovation in overcoming these obstacles. 7 3) DEMOGRAPHIC PROFILE AND MIGRATION DYNAMICS Conflict significantly influences household demographics and migration patterns, leading to changes in family structures, displacing populations, and causing demographic shifts through both internal and external migration. Buvinić et al. (2012) observe that these demographic transformations, prompted by sex-specific mortality and morbidity, affect marriage and fertility patterns. This shift can create new avenues for political participation among groups previously marginalized. Households respond to these changes by adjusting marriage and fertility rates, engaging in migration, and redistributing labor across their members. Stress and separation can lead to a decline in fertility, altering the social fabric of communities. Conte and Migali (2019) associate the intensity and geographical reach of conflict-induced lethal violence with an increase in asylum applications, indicating how the severity of conflict alters perceptions of threat and influences forced migration patterns. Beyond direct violence exposure, economic and political instability also drives forced international migration. Seven (2022) contests the view that migration during civil conflicts is purely a reaction to violence, proposing that individuals exert agency in their decision-making. Even amid violence, the aspiration for a better future may motivate some to remain, suggesting that migration responses to conflict are shaped by personal choices and perceptions rather than being strictly deterministic. Ekoh et al. (2021) examine the effects of displacement on family structures and roles in Nigeria, showing that displacement can drastically undermine the family’s ability to care for its older members. The erosion of family support networks due to displacement and loss underscores the deep impact of conflict-induced displacement on familial and social bonds. Raleigh (2011) discusses how conflict, poverty, and indirect factors like livelihood vulnerability and ecological instability influence migration in developing countries. Conflicts frequently arise in areas where communities are dependent on natural resources, making them susceptible to both conflict and environmental shifts. This situation points to a complex relationship between conflict, economic uncertainty, and migration, with civilians facing multiplied risks. Birch, Carter, and Satti (2024) delve into the socioeconomic consequences of conflict in Sudan, highlighting the sustained marginalization of peripheral areas and exploitation by politically influential elites. This scenario fuels ongoing political instability and conflict, perpetuating interregional disparities and shaping migration tendencies. The degradation of socioeconomic and educational infrastructure aggravates the difficulties faced by families and communities, prompting shifts in household demographics and migration behaviors. These insights shed light on the intricate effects of conflict on household demographics and migration patterns. The dynamics of displacement, alterations in family structure, and demographic shifts underscore the intricate connections between conflict, economic circumstances, and individual decision-making. Recognizing these patterns is vital for addressing the needs of populations impacted by conflict and for crafting focused interventions to aid displaced and at-risk groups. 8 3.1 Demographic characteristics of the respondents This section considers the demographic composition of rural households in Sudan, focusing on age, sex, the relationship of individuals to the household head, and education levels. Men make up 87 percent of household heads (Table 3.1). Within the hierarchy of household members, 46 percent of those surveyed identify as heads of households, followed by spouses (25 percent), children (sons or daughters) at 16 percent, and smaller percentages comprising of parents or parents-in-law and siblings. Most household heads are married. Table 3.1 Main demographic characteristics of rural households Variables Total Men Women Sex of household head, percent 100.0 87.0 13.0 Educational level of household head, percent Low education level 35.6 36.8 34.3 Medium education level 43.4 43.0 43.7 High education level 21.1 20.1 22.0 Marital status of household head, percent Single [never married] 9.0 10.5 7.4 Married 85.7 87.9 83.4 Widowed 3.9 1.0 6.8 Divorced/separated 1.4 0.5 2.3 Age of household head, years Mean 35.7 38.7 32.6 Q1, 25th percentile 25 28 24 Median/Q2 34 37 30 Q3, 75th percentile 44 48 40 Relationship to household head, percent of individuals Head 45.9 78.5 12.4 Spouse 25.1 3.8 46.9 Son or daughter 16.3 11.8 21.0 Son-in-law or daughter-in-law 0.2 0.0 0.4 Grandchild or great-grandchild 0.1 0.0 0.1 Parent or parent-in-law 5.1 2.6 7.6 Brother or sister 6.0 2.8 9.4 Grandparent 0.0 0.0 0.0 Adopted or fostered or stepchild 0.2 0.0 0.4 Other relative 1.0 0.4 1.7 Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. The average age across the sample is 36 years, with a median of 34 years (Table 3.1). Notably, one-quarter of heads of households are under 25 years of age, and three-quarters are under 44 years. Other analysis of the survey data shows that households with younger heads are slightly more inclined to live in areas of high conflict intensity—the average age of the household head in high-conflict zones was 35 years, compared to 37 years in lower- conflict zones. The education variable for household heads, classified into three categories based on years of schooling completed, reveals that just over one-third possess a low level of education (Table 3.1). In contrast, 43 percent have obtained a medium level, and 21 percent have a high level of educational attainment. When disaggregated by sex of the household head, female heads of household are found to have achieved a marginally higher level of education than men. 9 Figure 3.1 Education level of household heads, by employment status Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Figure 3.1 illustrates the relationship between employment status and educational attainment among household heads. Daily wage earners tend to have low levels of education—only 12 percent hold a high level of education. In contrast, salaried employees predominantly have higher education, with only 13 percent of household heads with salaried employment having only a low education level. Self-employed individuals show a fairly balanced distribution across low and medium educational attainment levels, while few have a high level of education. Landowners tend to have medium or high levels of education. Surprisingly, those without employment or income were found to be least likely to have a low level of education—most household heads who reported being unemployed had achieved a medium or high level of education. Geographically, educational levels vary significantly across states (Figure 3.2). Khartoum boasts the highest proportion of highly educated household heads, while Central Darfur has the lowest. Conversely, Central Darfur records the highest share of household heads who only achieved a low level of education, while Khartoum has the lowest share. This distribution underscores the significant disparities in educational attainment and employment status within the population, influenced by both geographical location and the nature of employment. 43.3 12.9 40.4 17.8 24.5 35.6 44.7 34.0 45.3 44.1 41.8 43.3 12.0 53.1 14.3 38.1 33.7 21.1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Daily wage-work Salaried worker Self employed Landowner No employment TOTAL Percent of household heads in category Low education level Medium education level High education level 10 Figure 3.2 Education level of household heads, by state Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. 3.2 Residential changes and migration In this section, we delve into the migration dynamics triggered by the conflict, scrutinizing both pre-conflict location of residence and migration patterns during the conflict along with the characteristics of migrating households. Figure 3.3 presents the proportion of households that relocated to a different state, categorized by their pre-conflict domicile. Notably, Khartoum experienced the highest rate of outmigration, with a striking 57 percent of its households relocating during the current conflict, marking it as the state with the most significant displacement. River Nile, South Kordofan, and Northern states also saw considerable movement, albeit at a much lower scale compared to Khartoum. North Kordofan and South Darfur also accounted for a notable share of the migrating households, illustrating the diverse geographic spread of migration patterns across the country. 28.7 40.8 42.0 22.3 18.0 38.0 43.4 25.7 51.5 47.7 52.2 43.4 50.2 51.1 40.6 41.1 58.6 12.1 35.6 48.1 44.3 44.6 43.8 47.7 44.2 38.1 48.3 36.5 39.8 37.7 42.4 43.2 38.0 43.5 49.5 37.9 43.7 43.4 23.2 14.9 13.4 33.9 34.4 17.8 18.5 26.0 12.1 12.5 10.1 14.3 6.6 10.8 15.9 9.4 3.6 44.2 21.1 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Percent of household heads in category Low education level Medium education level High education level 11 Figure 3.3 Households that migrated to another state since April 15, 2023 Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Among households that migrated, 79 percent were originally from Khartoum, making it the principal source of migration. Aj Jazirah was the top destination, attracting 31 percent of all migrants. This was followed by White Nile and River Nile, each hosting about 8 percent of the migrant households, with Sennar and Gedaref each welcoming 6 percent. Table 3.2 delineates the destination choices of migrating households from the ten states with the highest reported levels of household migrating. Specifically, 37 percent of households that left Khartoum found their new homes in Aj Jazirah, i.e., 21.1 percent of the 57.0 percent of households that left Khartoum. River Nile and White Nile states both received just under 9 percent of households that left Khartoum. Sennar and Northern states received a smaller share of households that migrated from Khartoum, with the rest of the migrant households from Khartoum dispersing to most other states in small numbers. Migration from other states than Khartoum was less pronounced, at 10 percent of households or lower, without a predominant destination emerging for these migrants. 1.3 3.4 3.5 11.3 9.5 5.1 5.7 2.6 0.0 2.7 10.5 7.7 3.4 7.7 3.0 4.8 4.8 57.0 16.9 0 10 20 30 40 50 60 Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Percent of households 12 Table 3.2 Destination state for households that migrated from the ten states with highest levels of migration reported, percent of all households State of origin Destination state Khartoum Central Darfur East Darfur South Darfur North Kordofan South Kordofan Blue Nile White Nile Northern River Nile Khartoum NA -- -- 1.0 1.5 -- -- -- -- 1.6 Central Darfur -- NA -- -- -- -- -- -- -- -- East Darfur 1.0 -- NA 0.7 -- -- 1.1 1.0 -- -- North Darfur 1.9 -- 1.4 2.2 -- -- -- -- -- -- South Darfur 1.8 4.8 -- NA 0.8 -- -- 1.0 -- -- West Darfur 0.5 -- -- -- -- -- -- -- -- -- North Kordofan 1.7 -- -- 0.7 NA 4.8 -- -- 4.7 -- South Kordofan 0.6 -- -- -- 1.6 NA -- -- -- -- West Kordofan 1.2 -- 1.9 0.8 2.3 2.8 -- -- -- -- Sennar 4.0 -- -- 0.8 -- -- 2.3 1.1 -- 1.7 Aj Jazirah 21.1 -- 1.5 0.8 0.8 -- -- 2.0 -- 1.6 Blue Nile 1.6 -- -- -- -- -- NA -- -- -- White Nile 5.1 -- -- -- -- 2.8 -- NA 4.8 -- Northern 3.8 -- -- -- 0.8 -- -- -- NA 1.5 River Nile 4.9 -- -- 0.7 -- -- -- -- -- NA Gedaref 3.3 -- -- -- -- -- 1.1 -- -- 3.3 Kassala 2.6 -- -- -- -- -- 1.1 -- -- 1.6 Red Sea 2.0 -- -- -- -- -- -- -- -- -- Households that migrated 57.0 4.8 4.8 7.7 7.7 10.5 5.7 5.1 9.5 11.3 Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Note: NA = “not applicable”. -- = “No household recorded as moving from origin to destination state”. Distinct household characteristics shed light on migration patterns following the outbreak of the conflict. Figure 3.4 highlights that households led by females, those with younger heads, and those with heads that are single (never married) or divorced, as well as households with higher education levels and larger sizes, exhibited a higher propensity to migrate. In contrast, daily wage earners and self-employed individuals demonstrated a lower likelihood of migration. A key factor influencing migration was the initial state of residence, especially distinguishing between states with low or high conflict intensity. These data reveal that living in a state of high conflict intensity serves as a significant push factor for migration, with many migrants relocating to states with lower conflict intensity during the current period of conflict. 13 Figure 3.4 Propensity to migrate out of state after the conflict began, by household characteristics Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Note: Share of each group that migrated. For example, 16.4 percent of male-headed and 20.1 percent of female-headed households migrated. Further analysis reveals differences in income levels before and during the conflict and how these interacted with the propensity to migrate by state. Figure 3.5 and Figure 3.6 compare the average pre-conflict and during the conflict incomes for migrants and non-migrants based on their pre-conflict state of residence. Migrants had an average pre-conflict income of 200,446 SDG, notably higher than the 137,196 SDG for non-migrants. This trend of higher incomes among migrating households is particularly pronounced in Khartoum, aligning with its significant outmigration rate, as detailed in Table 3.2. These findings underscore the multifaceted nature of migration decisions during the current period of conflict, highlighting the role of household demographics, socioeconomic status, and the intensity of conflict in shaping migration patterns. The tendency of migrants to seek refuge in low-conflict-intensity states during the current period of conflict, coupled with the observed income disparities, paints a complex picture of the socioeconomic underpinnings of migration in the aftermath of conflict.1 1 Red Sea state had a very small share of migrating households (1.3 percent). Therefore, the very high incomes of migrating households in the state seen in Figure 3.5 and Figure 3.6 likely are outliers. 14.9 19.6 23.4 4.4 19.0 15.2 16.3 36.2 31.7 14.8 26.9 13.1 29.3 16.5 11.3 20.6 10.5 15.7 28.1 15.1 15.8 23.8 20.1 16.4 0 10 20 30 40 High conflict intensity Post-conflict residence, low conflict intensity High conflict intensity Pre-conflict residence, low conflict intensity More than 10 members 5 to 10 members Household of less than 5 members No employment Landowner Self employed Salaried worker Daily wage worker, household head High education Medium education Low education, household head Divorced Widowed Married Single household head 45 years and over 30 to 44 years Household head aged 18 to 29 years Female Male household head Percent of households with characteristic that migrated out of state 14 Figure 3.5 Pre-conflict income by migration status and pre-conflict state of residence Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Figure 3.6 illustrates that the average income for both migrants and non-migrant households are lower after the onset of the conflict, in general, than in the pre-conflict period. However, while income levels declined dramatically on average during the current period of conflict, the income of migrating households tends to be still somewhat higher than that of those who did not migrate. This was also true for Khartoum, which was the pre-conflict residence of the majority of migrating households. 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Annual household income, Sudanese pounds Migrated Did not migrate 15 Figure 3.6 During the conflict income by migration status and pre-conflict state of residence Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. However, when this pattern of changes in income is examined categorically in Figure 3.7, we see that most households witnessed declines in income post the eruption of the conflict on April 15. In contrast to the pattern seen in Figure 3.6 based on average incomes, households that migrated are seen to have been twice as likely to have witnessed a complete loss of income relative to those who did not migrate. Figure 3.7 Patterns in changes in income from before to during the conflict, by migration status Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. As depicted in Figure 3.8, approximately 5 percent of households relocated within their state. Khartoum reporting the highest rate of internal displacement, over 20 percent, followed by Blue Nile, West Darfur, North Darfur, and Sennar. 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Annual household income, Sudanese pounds Migrated Did not migrate 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% All households Did not migrate Migrated Percent of households in category Increased Remained the same Declined Completely lost 16 Figure 3.8 Within state migration, by current state of residence Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Figure 3.9 details the propensity of households to migrate internally within a state based on their characteristics. Notably, male-headed households exhibited a marginally higher intra- state migration rate compared to female-headed ones. Households with younger heads, heads that are single, heads with higher education levels, salary workers, landowners, and those with larger families showed a greater inclination to migrate internally. Additionally, states experiencing higher conflict intensity saw increased rates of migration within their boundaries. 2.7 2.2 3.2 1.1 0.0 2.4 7.1 1.1 6.0 4.4 2.6 4.3 6.7 0.7 6.5 2.6 0.0 20.3 4.9 0 5 10 15 20 25 Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Percent of households 17 Figure 3.9 Propensity to migrate within state after the conflict began, by household characteristics Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. 6.0 3.3 5.2 4.8 4.4 0.0 6.7 2.6 7.8 5.1 8.3 4.1 4.2 2.3 4.7 4.6 8.2 3.7 4.6 8.5 4.2 5.0 0 2 4 6 8 10 High conflict intensity Current residence, low conflict intensity More than 10 members 5 to 10 members Household of less than 5 members No employment Landowner Self employed Salaried worker Daily wage worker, household head High education Medium education Low education, household head Divorced Widowed Married Single household head 45 years and over 30 to 44 years Household head aged 18 to 29 years Female Male household head Percent of households with characteristic that migrated within state 18 4) ECONOMIC RESILIENCE Conflicts profoundly disrupt socioeconomic structures, impacting employment opportunities, income sources, and labor market behaviors. The destruction of businesses and agricultural land and other assets leads to immediate job losses, while disrupted trade routes and markets complicate economic recovery and sustainable employment. In the Republic of South Sudan, conflicts have caused significant livelihood disruptions, from farming to trading, due to looting and destruction (Malual 2008). Civil conflict victims, like in Colombia, face challenges in income generation, with displacement leading to notable declines in labor income and consumption (Ibáñez and Moya 2006, 2010). Similarly, in Rwanda, conflict intensity correlates with lagging economic performance and consumption, impacting returns to land and labor during the recovery phase (Serneels and Verpoorten 2013). Displacement not only affects migrants but also alters labor conditions in host communities, influencing female labor participation and bargaining power without improving their status (Calderón, Gonzalez and Londoño 2011). In Tajikistan, the civil war’s effect on education and labor outcomes reveals a gap in educational attainment among women, affecting their employment and wages (Shemyakina 2011). The ongoing conflict in Sudan has severely disrupted employment and livelihoods, causing widespread economic instability. The labor force, especially the less educated, has seen income reductions above 50 percent, with the mining sector experiencing a dramatic income drop of over 90 percent (Siddig, Raouf and Ahmed 2023). The crisis has also exacerbated entry into employment, particularly formal jobs, extending unemployment periods for educated individuals while pushing many into informal activities (Assaad, Krafft and Wahby 2023). Agricultural activities and industrial production, particularly in agro-processing firms, have also declined, resulting in job losses (O. Kirui, et al. 2023a, 2023b). This chapter explores the conflict’s complex effects on rural households’ employment and livelihoods across Sudan. It examines shifts in income-generating activities due to the conflict, the transition to and from agriculture, changes in employment status, and increased dependency on remittances. It analyzes income source alterations across occupations and the impact of educational attainment on income stability, highlighting challenges in farming and broader obstacles to income generation, including market access and labor availability. Through this analysis, the chapter aims to understand the economic transformations caused by the conflict, laying the foundation for policy recommendations and interventions to support economic recovery and enhance long-term resilience. 4.1 Main sources of income The conflict has notably altered household income sources, shifting from a reliance on salaried work in the non-agriculture sectors to unemployment (Figure 4.1). There is also a slight increase in wage work in the agricultural sector (crop farming). This decreased proportion of households earning income from non-agricultural activities underscores the conflict’s disruptive impact and potential long-term damage to the industrial, service, and public sectors. 19 Figure 4.1 Main sources of income before and during the conflict Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. There also has been a notable rise in households citing remittances as their primary income or income—rising from 1 percent before the conflict to 4 percent during the conflict, signaling increased dependency on external financial support. This trend reflects the distress and adaptation of families facing diminished local income opportunities. Moreover, the growing number of individuals reporting ‘No employment and income’ starkly highlights a surge in the number of those facing severe barriers to obtaining any income during the conflict. The increased reliance on remittances and rising unemployment rates point to a weakened domestic economy, necessitating targeted policy interventions. Such responses should focus on sectoral development and foster sustainable employment opportunities in both agricultural and non-agricultural enterprises to counter the adverse effects of increased unemployment and dependency on external financial support. A considerable share of households experienced a shift in their income-generating activities, with 15 percent transitioning from employment to no employment, highlighting the severe job losses and economic disruption caused by the conflict (Figure 4.2). There also has been a more limited shift from non-agricultural to agricultural work, likely due to the scarcity of non- agricultural jobs during the current period of conflict. Conversely, movement from agricultural to non-agricultural activities has been minimal (4 percent), suggesting limited disturbances in farming practices that would drive any pursuit of non-agricultural income alternatives. Furthermore, a smaller yet notable trend is seen in the transition from salaried to wage employment, indicating a move from stable formal jobs to more insecure informal labor arrangements. 0% 5% 10% 15% 20% 25% 30% No income-generating employment Remittances Gifts, donations, pensions, assistance Renting land, properties, sharecropping Own household's non-farm enterprise Own household's fishing or aquaculture Own household's livestock business Work on own household's farm Share work, non-agriculture Salaried work, non-agriculture Sharecrop farming Salaried work, fishing / aquaculture Salaried work, livestock Salaried work, crop farming Wage work, non-agriculture Wage work, fishing / aquaculture Wage work, livestock Wage work, crop farming Percent of households During Before 20 Figure 4.2 Employment transitions after the eruption of the conflict, percent of households that experienced them Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Note: The transitions are not mutually exclusive. Figure 4.3 details the conflict’s impact on household incomes across Sudan, presenting patterns in changes in income levels from before to during the conflict by state. Nationally, income has dropped for 60 percent of the sampled households, underscoring the conflict’s broad negative economic effects. A small fraction of rural households report income increases. Alarmingly, 21 percent of rural households nationally report having lost their income entirely, with the highest incidences in Khartoum, the conflict’s epicenter, and Aj Jazira state. Income levels for about one-sixth of rural households remained stable, a minority compared to those who suffered declines. This highlights the widespread economic damage brought by the conflict. In summary, Figure 4.3 paints a bleak economic picture for Sudanese rural households under the current conflict, pointing to an urgent need for economic recovery efforts, particularly in the most affected states. Figure 4.3 Patterns in changes in income from before to during the conflict, by current state of residence Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. 36.9% 15.1% 7.1% 5.2% 3.9% 0% 10% 20% 30% 40% All households reporting change in income activity From employment to no employmet or income From nonagricultural to agricultural From salaried to wage work From agricultural to nonagricultural Percent of households experiencing employment transition 26.3 14.0 11.7 15.4 10.1 10.7 14.1 14.1 11.2 7.6 13.8 17.2 40.9 33.3 16.2 26.1 40.7 10.1 16.7 57.1 61.3 66.7 56.6 57.8 62.2 68.2 57.4 66.5 70.0 71.5 62.6 49.7 56.1 69.5 61.9 50.6 49.8 60.0 12.8 21.5 11.7 24.6 26.6 24.9 15.9 24.9 18.8 19.0 13.8 17.2 8.7 10.5 12.8 11.2 8.6 39.0 20.7 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Percent of households Increased Remained Declined Totally lost 21 Figure 4.4 contrasts mean incomes by occupation before and after the conflict began in rural Sudan, highlighting the income toll across employment types. During the conflict, a marked decline in mean income—from 223,600 to 180,400 in nominal Sudanese pounds on average—is evident across most sectors, particularly in wage labor within both agricultural and non-agricultural fields. This decline indicates the conflict’s disruptive impact on industries and employment, leading to reduced wages and potential job losses. Figure 4.4 Mean annual income by occupation before and during the conflict Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Individuals earning through production or profit shares have also witnessed income drops, pointing to compromised business profitability and production in both agricultural and non- agricultural sectors. Similarly, income from remittances, gifts, donations, pensions, or assistance has also decreased. An interesting finding is the rise in income from property rentals, likely driven by heightened demand and increased rental rates due to displaced people moving to rural areas amidst the conflict. Overall, the data illustrates significant adverse income impacts across a range of occupations following the conflict. This highlights the urgent need for economic recovery initiatives targeting the most affected sectors in both agriculture (farming and livestock) and other economic sectors. Figure 4.5 depicts per capita income variations across states before and during the conflict, offering insight into its economic repercussions at a regional level. Overall, a declining trend is seen in per capita income during the conflict, indicating widespread economic disruption. 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 Remittances Gifts, donations, pensions, assistance Renting land, properties, sharecropping Own household's non-farm enterprise Own household's fishing or aquaculture Own household's livestock business Work on own household's farm Share work, non-agriculture Salaried work, non-agriculture Sharecrop farming Salaried work, fishing / aquaculture Salaried work, livestock Salaried work, crop farming Wage work, non-agriculture Wage work, fishing / aquaculture Wage work, livestock Wage work, crop farming Mean annual income, Sudanese pounds During Before 22 Figure 4.5 Per capita annual income before and during the conflict, nominal Sudanese pounds, by current state of residence Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Notably, the extent of economic decline varies by state. Khartoum, previously the wealthiest state, experienced a significant drop in income, likely due to the severe impact of conflict on its economic structure. Siddig et al. (2023) note that higher-income groups and urban sectors, particularly housing and industry in Khartoum, suffered considerable losses. Moreover, reductions in income were not only limited to conflict-affected states. Red Sea and Gedaref states have also witnessed a significant decline in per-capita income due to the conflict. Conversely, other states, like Blue Nile, South Kordofan, and River Nile, saw marginal income increases. This may indicate that the rural economies fared better in these states, possibly due to less dependence on conflict-impacted activities. In essence, Figure 4.5 illustrates the conflict’s broad economic toll, but with impacts varying in intensity across states. This variation highlights the need for tailored economic recovery strategies, particularly in the hardest-hit regions, while also suggesting the value of understanding and leveraging the resilience observed in certain areas to inform broader recovery efforts. Figure 4.6 offers a detailed view of how household incomes have shifted as a result of the conflict by the educational attainment of the household head. Income changes are categorized into four patterns of increased, unchanged, decreased, or completely lost. A significant finding across all educational groups is a marked decrease in income, reflecting a broad economic downturn triggered by the conflict. This trend is particularly pronounced 0 5,000 10,000 15,000 20,000 25,000 30,000 Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Per-capita annual income, nominal Sudanese pounds During Before 23 among households headed by individuals with medium and low education, which includes household heads with primary, secondary, or vocational training. It underscores the negative impact of the conflict on income-earning opportunities for these household heads. Households with highly educated heads reported the highest rates of total income loss, indicating that higher education did not protect against the financial devastations of the conflict. Figure 4.6 Patterns in changes in income from before to during the conflict, by education level of the household head Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Meanwhile, households led by a head with a low level of education were relatively more able to maintain unchanged income levels, possibly due to engagement in subsistence or informal sectors less affected by the conflict or because their incomes were already at a minimum. The overall trend points to greater income stability among those with the lowest educational attainment, while those with higher education faced more substantial disruptions to their incomes. However, the broader pattern is of a general economic decline across educational backgrounds, with significant income reductions being the most common outcome. Figure 4.7 and Figure 4.8 offer insight into the disruption of farming activities across various regions under the current period of conflict, highlighting both the extent and causes of these disruptions. Nationally, 51 percent of households undertaking farming reported disruptions in farming, a testament to the conflict’s widespread impact on agriculture beyond specific locations, with the highest reports of disruption coming from Khartoum. However, the states considered relatively safe also saw significant disruption, suggesting the conflict’s agricultural impact was systemic. A leading cause of disruption to farming, identified by 22 percent of households, was the rise in input prices, reflecting supply chain issues or resource scarcity driven by the conflict. Additionally, 10 percent attributed disruptions to the direct destruction of production facilities, while 9 percent faced restrictions on movement, hampering access to fields and markets. Challenges in acquiring seeds, other inputs, and fertilizer, along with constraints on selling outputs and hiring labor, further illustrate the myriad obstacles faced by farmers. The reported inability to hire sufficient labor for some households hints at a labor shortage due to displacement or conscription. Collectively, these factors reveal a sector under siege from increased costs, physical destruction, and logistical hurdles, emphasizing the conflict’s comprehensive effect on agricultural productivity and rural economic sustainability. 0% 20% 40% 60% 80% 100% Low education level Medium education level High education level Percent of households in category Increased Remained the same Declined Completely lost 24 Figure 4.7 Proportion of households who reported that their farming work was disrupted, by state Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Figure 4.8 Reason for the disruption to farming work, percent of all households Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Note: The reasons given are not mutually exclusive. 4.2 Challenges faced in income-generating activities Different sources of rural livelihoods present different challenges under conflict. Here, we examine the challenges specifically related to crop production, livestock raising, and earning a wage (Figure 4.9). 0 10 20 30 40 50 60 70 Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Percent of households 0 5 10 15 20 25 Could not hire enough labor Could not hire labor Could not sell output Could not obtain fertilizer Could not obtain other chemicals Could not acquire seeds on time Restrictions on movement Produce destroyed Higher input prices Percent of households 25 Figure 4.9 Challenges reported in generating incomes from crop production, livestock production, and wages Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. In terms of crop production, the most commonly reported challenge is related to irrigation water supply, affecting around 30 percent of households working in crop production. This could be due to damage to infrastructure, changes in control over water sources, or fuel shortages for pumps, which independently is the third most cited challenge for crop producers. The second most significant issue is the price of inputs. This is likely linked to supply chain disruptions and increased costs of importing goods during the current period of conflict due to the deterioration in the exchange rate. Other notable challenges include pests and diseases, weather conditions, the availability and price of labor, and the general scarcity of necessary inputs for farming. When it comes to raising livestock, the challenge reported by the greatest share of households working in the livestock sector is insufficient inputs. Livestock raising is input- intensive, requiring feed, medicine, and other supplies, which may be scarce or expensive due to the conflict. Grazing routes are the second most reported challenge, which can be altered or become inaccessible due to conflict-related changes or security issues. Water supply constraints and the sickness or death of animals were further reported to impede livestock-raising activities. These challenges could be linked to the broader environmental and health impact of the conflict, which can lead to water scarcity and increased disease prevalence. 0 10 20 30 40 50 60 Inability to reach work location Health problems Poor safety at work location Unsafe to travel to work location Reduced wages Reduced working hours / less work OBTAINING WAGE INCOME Other livestock challenges Sickness / death of animals Inadequate water supply Restrictions on grazing routes High price of inputs Not enough inputs LIVESTOCK PRODUCTION Other cropping challenges Difficulties in hiring workers Not enough inputs High price of inputs Adverse weather High fuel prices Pests and diseases Problems with irrigation supply CROP PRODUCTION Percent of households 26 For individuals reliant on wages or salaries, the predominant challenge is reduced working hours, which affects nearly half of those reporting on challenges related to wage labor. This reduction could stem from a decline in demand, business closures, and other restrictions impacting operational hours. The second major challenge reported is decreased wages. This may reflect economic downturns leading to pay cuts or shifts to lower-paying jobs. Safety concerns, both at work and in traveling to work, also emerge as significant issues, indicative of the persisting instability and potential threats in the current period of conflict. Health problems, likely exacerbated by the conflict, further contribute to the difficulties faced by wage earners. Each of these challenges to livelihoods impacts the ability of individuals and households to maintain or recover their income sources during the conflict. Reduced wages and working hours directly affect the livelihoods of salaried and wage workers, while disruptions in crop and livestock production arising from significantly higher input prices or restricted access to resources highlight the need for comprehensive recovery strategies addressing both market dynamics and infrastructural rehabilitation. 27 5) FOOD SECURITY AND COPING MECHANISMS 5.1 Food security situation The relationship between conflict and food security is notably profound in regions dependent on agriculture. Studies, such as those by Olanrewaju and Balana (2023), highlight how conflict-induced factors like migration and fatality exacerbate food insecurity and diminish dietary diversity in Nigeria. Diallo (2023) adds that such shocks, coupled with climate change, cripple agricultural activities, diminishing food supply. Kondylis (2008) explores displacement in Rwanda, showing that resettled households often move to more productive areas, indicating potential shifts in agricultural productivity due to conflict. Similarly, Eklund et al. (2017) observed land-use changes in Syria and Iraq, such as cropland expansion and abandonment, reflecting the conflict’s complex impact on agriculture. Brück et al. (2019) note the destruction of infrastructure and supply chains in the Gaza Strip, stressing the importance of aid in sustaining food access amid conflict. Displacement complicates food access, as displaced populations lose traditional agricultural lands and social support networks, crucial for food security (Shemyakina 2022). The agrifood system in Sudan, vital for livelihoods and food security, faces disruptions due to ongoing conflict, impacting smallholder farmers significantly. Abushama et al. (2023) and Kirui et al. (2023a) report on the adverse effects on farming preparations and outputs, with notable declines in cultivation and essential crop yields (FAO 2023). This chapter examines food security in Sudan utilizing the Food Insecurity Experience Scale (FIES) (Cafiero, Viviani and Nord 2018, FAO 2021) to assess the extent and severity of food insecurity. FIES also permits consideration of how violence and external shocks further aggravate food insecurity. Additionally, the Livelihood Coping Strategy Index (LCSI) is used to highlight the coping strategies households adopt in the face of economic and food security challenges (WFP 2021). By dissecting these aspects, the chapter aims to offer a detailed view of Sudan’s food security during the current period of conflict and the adaptive mechanisms employed by its people. This is done to guide effective interventions for supporting vulnerable communities. We estimated the severity of food insecurity following a Rasch Model within the context of FIES (Boone 2016). This model is a statistical technique that probabilistically classifies the food security status of households and is derived from the toolkit of Item Response Theory models commonly used in the educational and psychological fields (Reise and Revicki 2015). The model allows for the comparison of food insecurity prevalence rates from different countries by calibrating them against this global reference. Table 5.1 and Figure 5.1 provide parameters related to the FIES and its Rasch modeling. Approximately 59 percent of Sudanese households during the current conflict are experiencing moderate or more severe levels of food insecurity. Households in West Kordofan, South Kordofan, and Blue Nile states recorded the highest prevalence of food insecurity. 28 Table 5.1 Household food security status based on raw Food Insecurity Experience Score (FIES) and Rasch Model estimates, by state Food Insecurity Experience Score (FIES) Raw Scores Rasch Model Probability of Moderate or Severe Food Insecurity Probability of Severe Food Insecurity State Score Rank (states) Probability Rank (states) Probability Rank (states) Total 4.10 NA 0.589 NA 0.125 NA Khartoum 4.39 5 0.636 5 0.137 7 Central Darfur 3.83 13 0.556 13 0.047 18 East Darfur 4.17 7 0.593 9 0.117 9 North Darfur 4.41 4 0.637 4 0.153 5 South Darfur 4.03 10 0.596 8 0.069 16 West Darfur 3.77 14 0.536 14 0.054 17 North Kordofan 3.86 12 0.559 12 0.105 12 South Kordofan 4.74 3 0.690 2 0.177 3 West Kordofan 4.92 1 0.705 1 0.209 1 Sennar 4.16 8 0.597 7 0.131 8 Aj Jazirah 3.74 15 0.524 15 0.112 10 Blue Nile 4.78 2 0.687 3 0.190 2 White Nile 4.38 6 0.627 6 0.162 4 Northern 3.37 17 0.467 17 0.106 11 River Nile 3.50 16 0.503 16 0.082 15 Gedaref 3.95 11 0.575 10 0.103 13 Kassala 4.05 9 0.574 11 0.138 6 Red Sea 3.16 18 0.435 18 0.089 14 Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Note: NA = “not applicable”. Fifty-nine percent of households face moderate or severe food insecurity, while 12.5 percent face severe food insecurity. This indicates a critical impact of the conflict on agricultural production and livelihoods, particularly alarming in areas traditionally considered food baskets for urban centers. State-level analysis reveals severe food insecurity across all regions, with West Kordofan, Blue Nile, South Kordofan, White Nile, North Darfur, Kassala, Khartoum, and Sennar showing probability of severe food insecurity above the national average, highlighting an acute crisis. States like Red Sea and Northern, which are among the safest and have better access to markets and humanitarian aid, report the highest percentages of households that are not either moderately or severely food insecure in rural Sudan, suggesting that safety, market access, and humanitarian interventions play crucial roles in food security. 29 Figure 5.1 Household food security status based on Rasch Model estimates of Food Insecurity Experience Score (FIES), by state Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. The variations across states suggest that infrastructure quality, conflict intensity, and access to aid significantly influence food security levels. The pervasive severe food insecurity necessitates urgent and extensive interventions to enhance food aid, revitalize agricultural systems, and restore supply chains, aiming to mitigate the food crisis and prevent further escalation. The situation has continued to deteriorate since the survey was carried out. The Integrated Food Security Phase Classification (IPC) report of March 2024 highlights a sharp decline in food security and nutrition due to escalating conflict, threatening millions with acute food insecurity and malnutrition (IPC 2024). With the severe restriction of humanitarian responses and assessments and an anticipated challenging lean season upcoming, a famine in the country cannot be ruled out in 2024, particularly in Khartoum and Aj Jazirah states and the states in the Darfur and Kordofan regions. Severe food insecurity presents a critical challenge across both male and female-headed households, with female-headed households slightly more affected (Figure 5.2). This difference may result from socioeconomic inequalities, such as lower income levels and restricted access to resources, that women particularly face, alongside their often greater responsibility for family nourishment. Despite the general struggle with food security, male- headed households report slightly better food security levels than their female counterparts. This scenario underscores the need for gender-sensitive food security interventions during 43.5 57.4 57.5 50.3 46.7 62.7 68.7 52.4 59.7 70.5 69.0 55.9 53.6 59.6 63.7 59.3 55.6 63.6 58.9 8.9 13.8 10.3 8.2 10.6 16.2 19.0 11.2 13.1 20.9 17.7 10.5 5.4 6.9 15.3 11.7 4.7 13.7 12.5 0 10 20 30 40 50 60 70 80 Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Al Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Percent of households Severe Food Insecurity Moderate or Severe Food Insecurity 30 the current period of conflict, emphasizing support for female-headed households to ensure fair access to food resources and address the unique obstacles they encounter. Figure 5.2 Food Insecurity Experience Scale (FIES) categories, by sex of household head Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Analyzing moderate or severe food insecurity alongside income changes reveals a distinct pattern: households experiencing income increases are mostly food secure, highlighting a direct correlation between improved income and access to food (Figure 5.3). In contrast, households with stagnant or reduced incomes face moderate to severe food insecurity, with those experiencing a decrease in income particularly vulnerable to high food insecurity. The situation is most critical for households that have completely lost their income, the majority of whom suffer from severe food insecurity. This pattern demonstrates the profound effect of income loss on food procurement capabilities, resulting in significant food insecurity. These findings underscore the essential role of income stability in ensuring food access. As income diminishes or disappears, the risk and intensity of food insecurity grow, pointing to the necessity of interventions that bolster both food availability and economic opportunities to mitigate food insecurity effectively. Figure 5.3 Food Insecurity Experience Scale (FIES) categories, by patterns in changes in income from before to during the conflict Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Households encountering shocks, such as illness, death, or climatic events, typically face heightened food insecurity due to diminished savings or income, impeding their food purchasing capacity (Figure 5.4). This effect is more pronounced in areas where such shocks are common, pushing a significant portion of households into moderate or severe food insecurity. Violence exacerbates this scenario by causing displacement and asset loss, further restricting food production and acquisition, leading to higher rates of severe food insecurity among affected households (Figure 5.5). 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Male headed Female headed Percent of households in category Food secure Moderately or severely food insecure 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Increased Remained the same Declined Completely lost Percent of households in category Food secure Moderately or severely food insecure 31 Figure 5.4 Food Insecurity Experience Scale (FIES) categories, by whether household reported experiencing a shock Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Figure 5.5 Food Insecurity Experience Scale (FIES) categories, by whether household reported being victimized by violence Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. An analysis of the demographic aspects of food insecurity shows that meal skipping is slightly more prevalent among male household members (15 percent) than females (9 percent). However, the issue’s severity escalates at the household level, affecting 40 percent of all members. The incidence of going to bed hungry is equal at 9 percent for adult male and female members but extends to 35 percent across all household members, signifying a widespread challenge. Similarly, going to bed hungry impacts children, highlighting the nutritional risks they face during crucial developmental phases. The most extreme cases of food deprivation—going a whole day and night without eating— disproportionately affect children and are more common among females than males. Recurrent food insecurity experiences over 30 days reveal a deep-rooted issue. Twelve percent of households reported frequently facing entire days without food, indicating severe deprivation. Similarly, half of households reported occasionally going to bed hungry and 20 percent reported often going to bed hungry. These findings highlight the persistent struggle for daily sustenance for many rural Sudanese households. The lack of any food at home affects more than half of households sometimes and almost one-quarter quite often, pointing to an ongoing crisis. These findings illuminate the pervasive and recurring nature of food insecurity, especially among women and children, necessitating targeted interventions to cater to their specific needs within broader food security efforts. The data underscores the urgency of addressing both immediate and structural challenges to break the cycle of hunger and deprivation. 5.2 Coping mechanisms to maintain livelihoods The Livelihood Coping Strategy Index (Phukan, et al. 2023) across various states highlights the diverse coping strategies households employ to navigate economic difficulties (Figure 5.6). These strategies include minimizing agricultural input expenses, selling household items or jewelry, and disposing of productive assets or vehicles. Nationally, half of the rural 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% No Shock Shock Percent of households in category Food secure Moderately or severely food insecure 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% No Violence Violence Percent of households in category Food secure Moderately or severely food insecure 32 Sudanese households have adopted one or two coping strategies. However, a notable 35 percent have not employed any, possibly indicating either resilience or an initial absence of resources to liquidate. Fifteen percent report having resorted to three or more coping strategies, signaling deep economic distress. There is a significant variance in coping responses across states. Darfur region, for instance, shows a high percentage of households without any coping strategies, particularly in West Darfur, where 64 percent of households have not adopted any measures, and only 3 percent have utilized three or more. Central Darfur follows closely, with 63 percent of households not engaging in any coping mechanisms and a mere 2 percent implementing three or more strategies. Conversely, Kordofan’s regions, such as West and South Kordofan, demonstrate a greater dependency on all three strategies, at 29 percent and 25 percent of households, respectively, underscoring the region’s severe economic challenges but the greater resources households can employ in coping with them. Overall, the data reveals that although many households across various states have navigated economic challenges without resorting to extreme coping strategies, a significant portion has adopted more drastic measures, such as asset liquidation. This trend underscores a deeper vulnerability, potentially hindering the long-term economic resilience of these households. The observed variations in coping strategies across states highlight the context-dependent nature of economic hardship and the effectiveness of these strategies, influenced by factors like conflict intensity, local economic strength, and the presence of external support like remittances. 33 Figure 5.6 Number of livelihood coping strategies reported employed by household, by state Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. These data reveal a disparity in coping strategy use between male and female-headed households, with a higher percentage of female-headed households not employing any strategies (Figure 5.7). This suggests that female-headed households may have fewer assets to utilize during economic hardships, possibly due to inequalities in property ownership, financial access, or social and institutional support, which often impact women more severely. Conversely, male-headed households more commonly adopt one to two or all three coping strategies, indicating they may have more resources or options for mitigating economic strain. This pattern highlights the need for gender-specific interventions to address the unique challenges and limited coping capacity of female-headed households, underlining the importance of creating support mechanisms that cater specifically to their needs. Figure 5.7 Number of livelihood coping strategies reported employed by households, by sex of household head Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. 47.9 24.7 17.4 35.0 41.6 23.1 30.7 33.8 22.2 15.9 30.2 38.1 63.8 53.2 27.9 45.2 63.0 40.3 35.0 42.9 55.7 62.6 50.6 48.7 56.8 47.2 53.7 54.5 54.8 45.2 48.3 33.6 39.2 53.2 43.7 34.6 52.2 50.4 9.3 19.6 20.0 14.4 9.7 20.1 22.2 12.5 23.3 29.3 24.6 13.6 2.7 7.6 18.9 11.1 2.5 7.6 14.6 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Red Sea Kassala Gedaref River Nile Northern White Nile Blue Nile Aj Jazirah Sennar West Kordofan South Kordofan North Kordofan West Darfur South Darfur North Darfur East Darfur Central Darfur Khartoum TOTAL Percent of households No coping strategies One or two strategies Three or more 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Male headed Female headed Percent of households in category No coping strategies One or two strategies Three or more 34 Figure 5.8 illustrates that as households experience worsening food insecurity, their reliance on coping strategies intensifies. Figure 5.8 Number of livelihood coping strategies reported employed by households, by Food Insecurity Experience Scale (FIES) category of household Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Among food-secure households, just over half reported to not be using any coping strategies, reflecting either their stable access to food or lack of resources to use in coping with food insecurity. Forty-three percent implement one or two strategies, likely as a precaution to safeguard their food security. Only a minimal 6 percent of food-secure households employ three or more strategies, indicating either minimal economic distress or good access to one or two resources that are effective for use in coping. For moderately or severely food-insecure households, less than a quarter do not use any strategies, showing emerging economic challenges. Most, 55 percent, have adopted one or two strategies. Over 20 percent report using three or more, highlighting increasing reliance on coping mechanisms as economic strain grows. 0% 20% 40% 60% 80% 100% Moderately or severely food insecure Food secure Percent of households in category No coping strategies One or two strategies Three or more 35 6) HOUSEHOLD AND AGRICULTURAL ASSETS 6.1 Housing type and tenure This section focuses on three housing-related variables, namely the type of dwelling, its ownership, and the number of persons per room. We will analyze those variables in relation to household head characteristics2 and area of residence. Figure 6.1 illustrates that the largest proportion of respondents reside in mud houses or huts, which are classified as inadequate housing. A smaller percentage of respondents live in adequate housing types, which include brick bungalows or similar houses made of modern materials, semi-pucca houses constructed with a mix of modern and traditional materials, or apartments.3 On average, 73 percent of rural households in Sudan live in inadequate housing. Figure 6.1 Type of current dwelling of households Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Figure 6.2 depicts the housing of rural households disaggregated by characteristics of household heads. Female-headed households are more likely to reside in inadequate housing than male-headed households. Households headed by younger or middle-aged individuals are slightly more likely to live in inadequate housing than are households headed by older individuals. Widowed-headed household are notably more inclined to live in inadequate housing compared to households whose heads are in other marital status categories. The data clearly illustrate a significant decrease in the likelihood of residing in inadequate housing with higher levels of education. 2 To achieve this, we have selectively retained only those respondents who identified themselves as household heads within the sample. This refinement resulted in a 50 percent reduction in the sample size. By implementing this measure, we aim to ensure that explanations regarding household housing situations are based solely on characteristics provided by the household head, thus avoiding potential distortions from information provided by other household members. 3 At this first stage, we exclude the category “Other [specify]” from the analysis. 0 5 10 15 20 25 30 35 Other Apartment Bungalow brick house Semi-pucca (mix traditional & modern materials) Mud brick (jaloos) house Wooden house Sturdy hut Bamboo house Simple hut IDP (internally displaced persons) camp Percent of households 36 Figure 6.2 Proportion of households who live in inadequate houses, by household head characteristics and local conflict intensity Source: Authors’ weighted analysis of data from IFPRI-UNDP Sudan Rural Household Survey 2023. Note: The high conflict intensity states are Khartoum, Central Darfur, North Darfur, South Darfur, West Darfur, North Kordofan, South Kordofan, West Kordofan, and Aj Jazirah. Comparing residence in inadequate housing by employment status, daily wage workers are more prone to reside in inadequate housing, while landowners are the least likely. Lastly, the results demonstrate that inadequate housing is more prevalent in areas with low-intensity conflict compared to those with high-intensity conflict.4 The data show that 79 percent of households own their dwelling. However, house ownership varies across the characteristics of household heads and areas of residence. Figure 6.3 illustrates that women and young individuals are less likely to own the dwelling they reside in. Ownership rates by the age of the household head vary from 71 percent for households with heads under 29 years of age to 82 percent for those aged 45 and above. Regarding marital status, married heads are more likely to report owning thei