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SOUTH SUDAN POVERTY ESTIMATES

at the County Level for 2008

Introduction South Sudan became the world’s newest country in July 2011 following a historic referendum on self-determination that took place in January 2011.

The South Sudan National Bureau of Statistics (SSNBS), formerly known as Southern Sudan Centre for Census, Statistics and Evaluation (SSCCSE) is the official statistical agency of the Government of the Republic of South Sudan. It is mandated by the Transitional Constitution to collect, analyse and disseminate all official economic, social and demographic statistics.

Under the provisions of the Comprehensive Peace Agreement and the Interim Constitution of Southern Sudan, SSCCSE was assigned responsibility for conducting the Southern Sudan section of the 5th Sudan Population and Housing Census in 2008. While the 2008 Census remains the largest undertaking by the SSCCSE, it has carried out other large nationally representative surveys, most notably the 2006 and 2010 rounds of the Sudan Household Health Survey (SHHS) and the National Baseline Household Survey (NBHS), 2009. It has also carried out other statistical activities as evidenced by the publication of the annual statistical yearbooks in 2009 and 2010.

Overview of County-Level Poverty Estimates This document presents the first county-level poverty estimates for South Sudan. It has been prepared by the South Sudan National Bureau of Statistics in collaboration with the World Bank. The key objective of this work is to inform policy planning by South Sudanese authorities.

County poverty estimates were generated using an established statistical procedure which involves combining information from the 2008 Census with the 2009 National Baseline Household Survey (NBHS). The county-level estimates found in this document should be taken as a complement to the national and state-level poverty estimates published by (SSCCSE) in Poverty in Southern Sudan: Estimates from NBHS 2009, which can be downloaded from the SSNBS website: ssnbs.org

The poverty estimates presented here are the estimated percentages of the population in each county with levels of per person consumption below the poverty line. This analysis uses the same poverty line of 72.9 SDG that was used by SSCCSE for its poverty

South Sudan Poverty Estimates at the County Level for 20082

South Sudan Poverty Estimates at the County Level for 2008 3

estimates based on the 2009 survey. The poverty estimates provided here correspond to the time of the census in 2008. Given the events that have transpired in South Sudan since 2008, it is likely that poverty rates for some counties have changed since 2008.

Poverty rate estimates at the state and county levels are illustrated graphically in Figures 1 to 11. At the county level, Malakal in Upper Nile state has the lowest poverty rate (12 percent) while Aweil South in Northern Bahr El Ghazal has the highest poverty rate (82 percent). The poverty estimates illustrate substantial differences within counties that could be used more effectively to target policy decisions.

It is important to emphasize that these estimates are subject to substantial uncertainty. For each county, there is a range of valid estimates that is expressed as what is known as the “95 percent confidence interval.” The detailed tabular estimates given in Table 1 emphasize the width of the 95 percent confidence intervals for each county, and the range of estimates within a 95 percent confidence interval for each county is illustrated in Figure 12. For any pair of counties where the confidence intervals overlap, it is not possible to say with certainty that one county is poorer than the other.

A brief description of the methodology is given at the end of this document.

Water Point and Clinic


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Figure 2: Central Equatoria Poverty Rates


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Figure 7: Unity Poverty Rates


Figure 8: Upper Nile Poverty Rates


Figure 9: Warrap Poverty Rates


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Ranking of county by estimated poverty rate

95% confidence interval Poverty rate estimate


Figure 12: 95 Percent Confidence Intervals for County Poverty Rates

Note: Standard errors are adjusted to account for census data sample, which includes all households in a 10 percent sample of enumeration areas. “average” corresponds to the average poverty headcount, and “lb/ub” corresponds to the 95 percent confidence interval.


Table 1: South Sudan County Poverty Estimates

TerekekaJubaLainyaYeiMoroboKajo KejiToritLopa / LafonKapoeta NorthKapoeta EastKapoeta SouthBudiIkotosMagwiOld FangakKhorflusAyodDukWurorNyirolAkoboPochallaPiborTwic EastBor SouthCueibitRumbek NorthRumbek CentreWuluRumbek EastYirol WestYirol EastAwerialAweil NorthAweil EastAweil SouthAweil WestAweil Centre

State69%36%44%34%41%45%39%49%58%46%47%57%46%36%55%44%62%54%40%43%52%56%44%50%50%49%57%44%40%46%51%53%48%77%75%82%67%76%

Central Equatoria

Eastern Equatoria

Jonglei

Lakes

Northern Bahr El Ghazal

(54% , 83%)(22% , 50%)(27% , 61%)(23% , 45%)(26% , 55%)(29% , 61%)(27% , 51%)(37% , 61%)(46% , 69%)(37% , 55%)(32% , 62%)(46% , 67%)(32% , 60%)(24% , 48%)(41% , 68%)(18% , 70%)(50% , 74%)(40% , 67%)(29% , 51%)(31% , 56%)(40% , 65%)(35% , 78%)(30% , 58%)(36% , 65%)(39% , 61%)(32% , 65%)(36% , 78%)(23% , 65%)(18% , 61%)(29% , 63%)(36% , 66%)(37% , 69%)(29% , 66%)(69% , 85%)(69% , 82%)(76% , 87%)(56% , 79%)(67% , 85%)

Poverty HeadcountCountyRange of estimates within a

95% confidence interval


PariangAbiemnhomMayomRubkonaGuitKochLeerMayenditPayinijarRenkManyoFashodaMelutMabanMaiwutLuakpiny/NasirLongochukUlangBalietMalakalPanyikangAbyeiTwicGogrial WestGogrial EastTonj NorthTonj EastTonj SouthRagaJur RiverWauTamburaNageroNzaraEzoYambioIbbaMaridiMvoloMundri WestMundri East

State74%53%73%50%75%67%72%77%70%22%19%21%21%26%27%32%31%38%32%12%21%69%63%63%64%67%67%56%40%60%41%36%49%37%45%38%44%35%53%54%45%

Unity

Upper Nile

Warrap

Western Bahr El Ghazal

Western Equatoria

(61% , 86%)(31% , 75%)(65% , 82%)(28% , 72%)(63% , 88%)(49% , 84%)(61% , 83%)(61% , 93%)(58% , 81%)(14% , 30%)

(4% , 35%)(12% , 31%)(10% , 32%)(16% , 36%)(13% , 41%)(24% , 40%)(18% , 43%)(26% , 51%)(21% , 44%)

(5% , 19%)(11% , 30%)(52% , 86%)(52% , 74%)(53% , 72%)(52% , 77%)(57% , 77%)(56% , 78%)(42% , 71%)(31% , 48%)(54% , 67%)(35% , 47%)(27% , 46%)(22% , 76%)(25% , 49%)(36% , 55%)(28% , 47%)(32% , 56%)(24% , 46%)(40% , 65%)(34% , 73%)(31% , 58%)

Poverty HeadcountCountyRange of estimates within a

95% confidence interval


Poverty Estimate MethodologyThe methodology used to produce the poverty estimates is described in detail in Elbers, Lanjouw and Lanjouw (2003).1 The method involves using a household survey to impute estimates of consumption for households in a census. The poverty mapping methodology was originally developed by the World Bank and has been used to create detailed poverty estimates in a number of other countries including Ecuador, Madagascar and South Africa.

For South Sudan, the variables used for imputation of county poverty estimates are demographic characteristics of the household (size, gender and age of household head), education of the household head, housing characteristics and household asset ownership. A total of thirteen variables were selected for analysis. Importantly, the wording of the questions was exactly the same across both the census and the survey, ensuring comparability across the datasets. The chosen variables had similar distributions of responses at the state level. For consistency, the same set of variables was used across all ten states. A list of variables is given in Table 2.

1 Elbers, Chris, Jean O. Lanjouw and Peter Lanjouw (2002). “Micro-Level Estimation of Poverty and Inequality.” Econometrica 71:1, pp. 355-364.


Table 2: Description of variables used in poverty rate model

Owns bicycleDoes not own bicycleOwns radioDoes not own radioFirewood or grassOther (charcoal, gas, electricity, paraffin, cow dungand biogas)Basic (tukul or gottiya built of mud or sticks, tents and incomplete houses)Non-basic (flats, apartments, villas, wood and brick houses)Toilet (pit latrines, flush toilets or bucket toilets)No toiletFirewood or grassOther (public and private electricity, gas, paraffin lanterns and lamps, candle wax, solar power or biogas)Farming (crop farming or animal husbandry)Other (wages, salaries, owned businesses, property income, remittances, pension or humanitarian aid)

Owned dwellingNot owned (rented, provided as part of work, or free)Male or female

Age in number of completed years

Attended schoolNever attended school

Bicycle

Radio

Cooking energy source

Dwelling type

Toilet

Light source

Livelihood type

Household sizeTenure status

Gender of household headAge of household headEducation of household headRural/urban

1

2

3

4

5

6

7

89

10

11

12

13

Design: Robert Waiharo • Photos: © NBS

South Sudan National Bureau of Statistics (SSNBS)Juba, Sudan, P. O. Box 137

Email: [email protected] or [email protected]: www.ssnbs.org

March 2012

south sudan poverty estimates - reliefweb sudan poverty estimates at the county level for 2008 17...

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