The Prevalence of Poverty and Inequality in South Sudan: The Case of Renk County

1 Introduction Prior to the cession of the Southern Sudan from the Sudan in July 2011, there were many challenges that trap the population of many areas of the country by poverty. The education, health, water and sanitation services are extremely poor as a result of the long civil conflict (1955–1972 and 1982–2005) and unfavorable climatic changes and natural disasters. Consequently, adult illiteracy rate reached 75% of total population with the primary school enrolment being only 20% (GOS, UNCT 2004). Only 27% of the population had access to safe drinking water and only 16% had access to sanitation facilities and (AEPRC, ARC, ICARDA, 2009). The Comprehensive Peace Agreement (CPA), which is signed between the Sudanese government and the Sudanese People’s Liberation Army (SPLM) in 2005, brought the more than 20 years of war to an end. According to the CPA, there should be a redistribution of the country’s wealth with particular focus on natural resources led by oil and that was to be implemented during the interim period of six years (2005–2011). In January 2011, the people in south have voted for secession from Sudan, and accordingly the new country of south Sudan was born. This study’s focus is on providing detailed assessment of the poverty situation in south Sudan after the signature of the CPA and before the secession, i.e. during the interim period (2005 – 2011). The data used in the analysis are collected from the Upper Nile state and the findings of the study are expected to form a base for further evaluation of the poverty situation in the pre/post secession of southern Sudan. The Upper Nile state is the fourth biggest state in the South Sudan by population with 964,353 inhabitants in 2010, which constitutes 12% of the total population in the country (NBS, 2010). The state has 12 counties of which the Renk County is the second biggest by population with 137750 inhabitants, which constitutes 14.3% of the state’s population. Accordingly, the Renk County is selected as a case study. Renk County has an area of 23 thousand square kilometers and is located in the northern part of the state. Its climate belongs to the semi-arid zone with annual average rainfall ranging between 400-800 mm. (De Zuviria 1992). The county depends on the White Nile River, a few seasonal streams, man-made dug pools (haffirs) and irrigation canals as the main sources of drinking water (Anyong 2007). The population of the Renk County was estimated at 137750 persons (CBS and NBS 2009). The income earned by most of the population in the study are is low and the majority of the people are involved in a subsistence economy and small scale farming on clay and heavy loamy soils (Onak 2005). Some of the population also relies on collecting Arabic gum and fishing (AEPRC, ARC, and ICARDA, 2009). Renk County has one hospital and few health centers and clinics, 38 primary schools, 8 secondary schools and 2 universities (Administration Unit of Renk County, 2008). 2 Research Methodology To collect the required data for pursuing this study, household field survey that differentiates urban from rural households in the Renk County is used. A simple random technique has been used, since the respondents belong to interrelated tribes and thus portray homogeneous characteristics. The designated sample comprises 245 households, about 1.01% of the County's population. After the data collection and refinement, the clean sample became 200 observations, of which 75 are urban and 125 were rural households. The considered households are considered representative to the county as it involves households from the major county’s residential towns and villages. The Renk County includes into five Payams (residential towns) and large number of villages, each termed as Buma (residential village) (Renk Information Unit, 2010). The vast area of the county and the security situation made total population coverage almost impossible. Our sample selects 15 households from each of the five Payams and 10 12 households from each of the 12 Bumas to equivalently cover the four geographical locations in the County totaling to the 75 and 125 respondents from the Payams and Bumas, respectively. For comprehensive assessment of the poverty situation in the study area, this study employs several methods of analysis. First, it employs three different measures to construct a food poverty line for the study. Second, it uses Engel Curve Equation to estimate the total poverty line. Third, it uses (DAD) software to calculate: (1) the Foster Greer Thorbecke (FGT) measures including the poverty incidence, poverty gap and poverty severity; (2) the inequality measures including Gini Coefficient, estimation and construction of the Lorenz curve, besides the Quintile Dispersion Ratio (QDR) and food share. A brief description of each of these methods is provided hereafter. 3 Preliminary Findings Results drown from the Food-poverty-line reveals similarities among rural and urban households in the study area. The food-poverty-lines for rural and urban households are SDG 1.85 and 1.83 per person/day, respectively. Nevertheless, the calories intake between rural and urban households exhibited a significant difference. On the contrary, results of the poverty line show completely different story as the lines are SDG 2.38 and SDG 3.5 for the rural and urban households, respectively. This are explained by the higher costs paid by urban households mainly on non-food items. Results of the FGT measures of poverty indicate that poverty incidence, gap and severity are more prevalent among urban household (87%) than rural households (73%). This could be due to the huge influx of Internally Displaced People (IDPs) and refugees during the civil war and the lack employment opportunities in the study area. Results show that 41%, 73% and 96% of the urban households are living below the poverty line if the standard poverty lines of US$ 1/day, US$ 1.25/day and US$ 2/day are considered, respectively. Corresponding shares among rural households are 63%, 82% and 97%, respectively. Keywords: Poverty, South Sudan, Inequality.