High Frequency Survey: Wave 4 and Crisis Recovery Survey 2017
South Sudan, 2017
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WBG_SSD_2017_HFS-W4-CRS_v01_M
High Frequency Survey: Wave 4 and Crisis Recovery Survey 2017
| Name | Country code |
|---|---|
| South Sudan | SSD |
Other Household Survey
Between May and August 2017 The World Bank in collaboration with South Sudan’s National Bureau of Statistics, funded by DfID, conducted the fourth wave of the High Frequency Survey and the Crisis Recovery Survey to monitor welfare and perceptions of citizens in accessible urban areas and IDP camps across South Sudan. The HFS and CRS data contains information on security, economic conditions, education, employment, access to services, and perceptions. The data combines detailed household questionnaire information with displacement-specific information including drivers of displacement, access to resettlement mechanisms, and return intentions. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology as detailed in Pape and Mistiaen (2015).
Sample survey data [ssd]
Household
Coverage
The HFS covered urban areas of seven of South Sudan's ten former states: Western Equatoria, Central Equatoria, Eastern Equatoria, Northern Bahr-El-Ghazl, Western Bahr-El-Ghazal, Warrap and Lakes state. The CRS covered four of the largest Protection of Civilian (PoC) camps with defined boundaries: Bentiu, Bor, Juba, and Wau.
Producers and sponsors
| Name |
|---|
| South Sudan National Bureau of Statistics |
| Name | Affiliation |
|---|---|
| Utz J. Pape | IBRD |
| Name |
|---|
| Department for International Development |
| World Bank |
Sampling
Wave 4 of the High Frequency South Sudan Survey revisited urban households interviewed in Waves 1 and 2. Fifteen urban enumeration areas (EAs) visited in the first two waves were randomly selected from each state, and all of the households interviewed in the selected EAs were to be revisited. In Waves 1 and 2, the sampling strategy consisted of a stratified clustered design. Within each of the 7 strata (7 states, urban and rural) the primary sampling units are EAs that were drawn randomly proportional to size. Within EAs, a listing was conducted and 12 households were drawn randomly as unit of observation.
The CRS was conducted in 4 IDP camps in South Sudan between May to July 2017. The sample was restricted to Protection of Civilian (PoC) camps, and includes the 4 largest camps with clearly defined boundaries. The sample was designed as a multi-stage stratified random sample. Each camp was selected as a strata, with a target of 600 interviews per camp. Within each camp, 50 enumeration areas (EAs) were selected proportional to size, where the size was defined by the number of structures in the EA. The number of structures was counted using satellite imagery of the EAs and strata. Each EA was divided into 12 blocks, and a micro listing was done in the blocks to randomly select households. One structure per block was selected, and one household per structure was interviewed.
EAs were replaced if security rendered field work unfeasible. Replacements were approved by the project manager. Households were not replaced and were dropped from the sample after a total of three unsuccessful visits.
The selection probability for a household can be decomposed into the selection probability of the EA and the selection probability of the household within the EA.
In the HFS, the selection probability of an EA is calculated as the number of households within the EA divided by the number of households within the stratum multiplied by the number of selected EAs in the stratum estimated using the 2008 census. The selection probability for a household within an EA is constant across households and is calculated as the number of households selected in the EA over the number of listed households in the EA. Sampling weights were then scaled to equal the number of households per strata using the Census 2008 data.
In the CRS, the selection probability of an EA is calculated as the number of structures in the EA divided by the number of structures in the stratum multiplied by the number of EAs selected in the stratum. The number of structures was estimated using satellite imagery of the strata (camps). The selection probability of a household within the EA is decomposed into the selection probability of a block within the EA, the selection probability of a structure within a block, and the selection probability of a household within a structure. The sampling weights are then scaled to equal the number of structures per stratum as per the satellite imagery. In the Bor camp, the total number of households was similar to the target sample size, thus a census was conducted -- therefore, each household had a probability of selection of 1 and thus a sampling weight equal to 1.
Survey instrument
The questionnaire comprises the following modules.
-
The first three modules are different for the HFSSSW4 and CRS according to the sampling strategy.
Module 1: Introduction
Module 2: Administrative Information
Module A: Interview and Household Information -
The rest of the modules were identical for the HFSSSW4 and CRS.
Module B: Household Roster
Module C: Household Characterisitcs
Module D: Food consumption
Module E: Non-food consumption
Module F: Livestock
Module G: Durable goods
Module H: Wellbeing and Opinions
Module I: Conflict and Displacement
Module J: End of Interview
Module K : Enumerator Feedback
The questionnaire is available for download with the dataset.
Data collection
| Start |
|---|
| 2017 |
| Start date | End date |
|---|---|
| 2017-05 | 2017-08 |
Data processing
See accompanying Stata do-files, available under the related materials tab.
Data Access
| Is signing of a confidentiality declaration required? | Confidentiality declaration text |
|---|---|
| yes | Before being granted access to the dataset, all users have to formally agree: 1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor. 2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files. 3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor. |
The dataset has been anonymized and is available as a Public Use Dataset. It is accessible to all for statistical and research purposes only, under the following terms and conditions:
- The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the World Bank Microdata Library.
- The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.
- No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to the World Bank Microdata Library.
- No attempt will be made to produce links among datasets provided by the World Bank Microdata Library, or among data from the World Bank Microdata Library and other datasets that could identify individuals or organizations.
- Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from the World Bank Microdata Library will cite the source of data in accordance with the Citation Requirement provided with each dataset.
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Contacts
| Name | Affiliation | |
|---|---|---|
| Utz J. Pape | World Bank | upape@worldbank.org |
| Adwok Awur | South Sudan National Bureau of Statistics | adwokawur@gmail.com |
Metadata production
DDI_WBG_SSD_2017_HFS-W4-CRS_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Economics Data Group | The World Bank | Documentation of the DDI |
2018-12-13
Metadata version
Version 01 (2018)