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High Frequency Survey: Market Linkages Survey 2017

South Sudan, 2017
Central
Utz J. Pape, World Bank
Created on June 24, 2021 Last modified June 24, 2021 Page views 581 Download 27 Documentation in PDF Metadata DDI/XML JSON

Identification

Survey ID Number
SSD_2017_HFS-MLS_v01_M
Title
High Frequency Survey: Market Linkages Survey 2017
Country
Name Country code
South Sudan SSD
Study type
Other Household Survey [hh/oth]
Abstract
The Market Linkages Survey (MLS), along with price data from the High Frequency South Sudan Survey is used to assess the level of integration between food markets; barriers limiting integration; and subsequently inform recommendations to enable integration. The MLS conducted trader interviews on trade barriers to better understand the level of integration. The MLS was administered between March-May 2017 to over 500 traders across 14 cereal surplus and deficit towns. Information on 331 variables was collected on transport barriers, storage barriers, funding constraints, and information.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Individual

Version

Version Description
Final Version

Scope

Notes
The scope of the High Frequency Survey - Market Linkages Survey 2017 includes:
- Basic Trader Information
- Transport barriers
- Storage
- Financial Access
- Information Access

Coverage

Geographic Coverage
14 Locations in South Sudan: Malakal, Bor, Kuajok, Tonj South, Aweil, Aweil North, Wau, Rumbek, Rumbek East, Yambio, Maridi, Juba, Torit, and Magwi.

Producers and sponsors

Primary investigators
Name Affiliation
Utz J. Pape, World Bank International Bank for Reconstruction and Development (IBRD)
Producers
Name
South Sudan’s National Bureau of Statistics
Funding Agency/Sponsor
Name Abbreviation
TF for Sustainable Logistics SULOG

Sampling

Sampling Procedure
The sample design employs a design stratified by locations and type. To obtain a sampling frame, enumerators were requested to list at least 100 grain traders from each of the 14 locations using snowball listing, although in some locations this number was less due to issues of insecurity or lack of traders. Traders were approached and listed through referrals from the previous grain trader. The listed grain traders were then divided into two additional strata for each location strata into those who trade only locally and those who don't. For each location, 50 traders were then randomly selected to be interviewed.
Deviations from the Sample Design
Traders were replaced if security rendered field work unfeasible. Replacements were approved by the project manager. Replacement of traders were approved by the supervisor after a total of three unsuccessful visits to the trader's shop. Additionally, in cases where less than 50 traders were listed (2 locations), enumerators used snowball sampling to interview 50 traders per location.
Weighting
The sampling weight is the inverse probability of selection. The probability of selection of a trader in this case is one over the total number of traders listed in the particular stratum. Inversing this weight therefore results in the actual number of traders listed per stratum.

Data Collection

Dates of Data Collection
Start End
2017-03 2017-05
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Data collection was split into two phases - listing and the MLS. Enumerators listed 100 grain traders in each of the 14 locations out of which 50 were randomly selected by stratified sampling.

Questionnaires

Questionnaires
The questionnaire: Agrologistics Questionnaire is provided under the Related Materials tab. The questionnaire is made of the following modules:
- Module 1: Introduction
- Module 2: Basic trader information
- Module A: Transport barriers
- Module B: Storage
- Module C: Financial access
- Module D: Information access
- Module E: End of interview
- Module F: Enumerator feedback

Access policy

Contacts
Name Affiliation Email
Utz J. Pape World Bank upape@worldbank.org
Confidentiality
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:

Utz J. Pape, The World Bank. South Sudan High Frequency Survey: Market Linkages Survey 2017 (MLS 2017). Ref. SSD_2017_HFS-MLS_v01_M. Dataset downloaded from [url] on [date].

Metadata production

DDI Document ID
DDI_SSD_2017_HFS-MLS_v01_M_WB
Producers
Name Abbreviation Affiliation Role
Development Economics Data Group DECDG The World Bank Documentation of the DDI
Date of Metadata Production
2021-06-11
DDI Document version
Version 01 (June 2021).

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