GMB_2018_LFS_v01_M
Labour Force Survey 2018
Name | Country code |
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Gambia, The | GMB |
Labor Force Survey [hh/lfs]
The Gambia first Labour Force Survey was conducted in 1992. This was a localized study covering areas just around the capital, Banjul. The 2012 GLFS is the first nationally representative Labour Force Survey to be conducted in The Gambia. The survey was more comprehensive in coverage and also follows ILO guidelines. The 2018 GLFS study is the most recent study and is more comprehensive in terms of coverage and content than the 2012 survey.
The 2018 Gambia Labour Force Survey (GLFS) was implemented by the Gambia Bureau of Statistics (GBoS) from November 2017 to July 2018. Funding for the survey was provided by United Nations Development Programme (UNDP) and Ministry of Trade, Industry, Regional Integration and Employment.
Sample survey data [ssd]
Version 01
2019
The scope of the survey includes:
National coverage
Name |
---|
Gambia Bureau of Statistics |
Name |
---|
United Nations Development Programme |
Ministry of Trade, Industry, Regional Integration and Employment |
The Gambia LFS 2018 was intended to produce reliable estimates of the most important economic variables at the national level, for urban and rural areas, and for each LGA. Therefore, a stratified two-stage sample design was considered to provide estimates for the domains of interest. A Master Sample Frame designed for conducting household surveys was used for the sample selection. This frame is obtained from the 2013 Population and Housing Census data adjusted for the expected growth rate based on parameters estimated from the Integrated Household Survey 2015/16 data.
In the first stage, EAs were independently selected from the sample frame with Probability Proportional to Size (PPS) applied within each stratum the 8 Local Government Areas (LGAs). The EAs were selected as primary sampling units (PSUs) at the first stage of the sampling, since a new listing of households can be conducted in each sampled EA to update the frame for selecting the households at the second sampling stage.
It is important to note that Banjul and Kanifing LGAs are entirely urban settlements and hence do not have any rural EAs. The EAs in the rest of the LGAs are classified as either urban or rural. Therefore, by urban and rural, there was a total of 14 sampling strata in the 8 LGAs. In total, 313 EAs were selected (133 urban EAs and 180 rural EAs). Following the 12 selection of EAs at the first sampling stage, a new listing of households was conducted in all the sampled EAs in order to update the second stage sampling frame.
In the second stage, households were independently selected from the household listing for each sampled EA using systematic random sampling with equal probability in each EA. For the survey, the selected sample size was 6,260 households (2,660 in urban areas and 3,600 in rural areas).
The GLFS 2018 sample size requirements were derived based on the level of precision set for the main variable taking into account the size of the population, the sample design and method of estimation, the response rate and the fact that the true variability of the characteristic of interest in the population is unknown in advance. The sample was designed to provide labour market information with 95 per cent confidence interval in the 8 LGAs namely; Banjul, Kanifing, Brikama, Mansakonko, Kerewan, Kuntaur, Janjanbureh and Basse In each selected EA, 20 households were selected for the survey making it a total of 6,260 households.
In order for the sample estimates from the GLFS 2018 to be representative of the population, it is necessary to multiply the data by a sampling weight, or expansion factor. Principally the basic weight for each sample household is equal to the inverse of its probability of selection (calculated by multiplying the probabilities at each sampling stage). The sampling probabilities at each stage of selection were maintained in an Excel spread sheet with information from the sampling frame for each sampled EA so that the overall probability and corresponding weight was calculated.
The basic sampling weight, or expansion factor, is calculated as the inverse of the probability of selection.
Survey instruments for Gambia LFS 2018 were comprised of questionnaires, listing forms, instruction manuals to enumerators and supervisors. All these instruments were developed by the technical working group in various sessions prior to the main survey.
The LFS 2018 questionnaire was developed after extensive consultations with data users and other stakeholders in order to satisfy their respective data needs. The questionnaire consists of two modules, which are; Labour Force (LF) and Working Children (WC).
Start | End |
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2018 | 2018 |
The data collection exercise was subdivided into two phases. The first phase began on the 6th of July 2018 and all the ten teams were first deployed to Banjul and Kanifing LGAs. In Banjul, nineteen clusters were covered and for Kanifing, a total of forty-two clusters were covered. The rationale for starting the first phase of the exercise in these LGAs is to enable the coordinating team to respond quickly to any problem (s) in the instrument or the CAPI application at the early stage of the data collection exercise so that it can be addressed before the teams are deployed to their respective areas of work to cover the remaining 252 clusters.
Use of the dataset must be acknowledged using a citation which would include:
Example:
Gambia Bureau of Statistics. Gambia Labour Force Survey (LFS) 2018, Ref. GMB_2018_LFS_v01_M. Dataset downloaded from [URL] on [date].
Recommended citation: Gambia Bureau of Statistics (GBoS) [The Gambia] 2018. The Gambia Labour Force Survey 2018, Banjul, The Gambia: GBoS
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
DDI_GMB_2018_LFS_v01_M_WB
Name | Affiliation | Role |
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Poverty- GP | The World Bank | Metadata preparation |
2019-05-17
Version 01 (May 2019)
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