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High Frequency Survey 2017

South Sudan, 2017
Central
Utz J. Pape
Last modified October 06, 2017 Page views 31466 Documentation in PDF Metadata DDI/XML JSON

Identification

IDNO
SSD_2017_HFS-W4_v01_M
Title
High Frequency Survey 2017
Subtitle
Wave 4
Countries
Name Code
South Sudan SSD
Study notes
Between May and August 2017, World Bank in collaboration with South Sudan’s National Bureau of Statistics, funded by DfID, conducted the fourth wave of the High Frequency South Sudan Survey to monitor welfare and perceptions of citizens, revisiting urban households visited in Waves 1 and 2. This dataset contains information on security, economic conditions, education, employment, access to services, and perceptions. 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 (2014).
Kind of data
Sample survey data [ssd]

Coverage

Geographic coverage
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.
Unit of analysis
Household

Producers and sponsors

Authoring entity
Name Affiliation
Utz J. Pape IBRD - World Bank
Producer(s)
Name
South Sudan's National Bureau of Statistics
Funding agencies
Name Abbreviation
Department for International Development DFID

Sampling

Sampling procedure
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.
Deviations from sample design
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.
Response rate
N/A
Weighting
The sampling weight is the inverse probability of selection. 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. 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.

Data Collection

Dates of collection
Start End
2017-05 2017-08
Mode of data collection
Computer Assisted Personal Interview [capi]
Questionnaires
The questionnaire comprises the following modules

Module 1: Introduction
Module 2: Administrative Information
Module A: Interview and Household Information
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 provided under the Related Materials tab.

Data Processing

metadata.study_desc.method.data_collection.method_notes
See accompanying Stata do-files, available under the related materials tab

Data access

Access authorities
Name Affiliation URI
Microdata Library World Bank microdata.worldbank.org
metadata.study_desc.data_access.dataset_use.conf_dec
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.
Access conditions
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:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
Citation requirements
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

Example:

Pape, Utz. World Bank. South Sudan High Frequency Survey 2017, Wave 4 (HFS-W4 2017). Ref. SSD_2017_HFS-W4_v01_M. Downloaded from [URI] on [Date]

Contacts

Contact(s)
Name Affiliation Email
Utz J. Pape IBRD - World Bank upape@worldbank.org

Metadata production

IDNo
DDI_SSD_2017_HFS-W4_v01_M_WB
Producers
Name Abbreviation Affiliation Role
Development Economics Data Group DECDG The World Bank Documentation of the DDI
Production date
2017-10-05
Version
Version 01 (October 2017)