{"doc_desc":{"title":"ZMB-CSO-LFS-2012-V01","idno":"DDI-ZMB-CSO-LFS-2012-v1.0","producers":[{"name":"Central Statistical Office","abbr":"CSO","affiliation":"Ministry of Finance","role":"Documentation of Survey"}],"prod_date":"2026-02-18","version_statement":{"version":"Version 1.0 (January 2026)"}},"study_desc":{"title_statement":{"idno":"ZMB-CSO-LFS-2012-v1.0","title":"Labour Force Survey 2012","alternate_title":"LFS 2012"},"authoring_entity":[{"name":"Central Statistical Office","affiliation":"Ministry of Finance"}],"production_statement":{"producers":[{"name":"Ministry Of Labour and  Social Security","abbr":"MLSS","role":"Participation in data collection and analysis"}],"copyright":"(c) 2013, Central Statistical Office, Zambia","funding_agencies":[{"name":"Government Republic of Zambia","abbr":"GRZ","role":"Funding and technical assistance"},{"name":"International Labour Organisation","abbr":"ILO","role":"Funding and technical assistance"}]},"distribution_statement":{"contact":[{"name":"Head of Dissemination","affiliation":"Zambia Statistics Agency","email":"info@zamstats.gov.zm","uri":"https:\/\/nada.zamstats.gov.zm"}]},"series_statement":{"series_name":"Labor Force Survey [hh\/lfs]","series_info":"The 2012  Labour Force Survey is the 4th in the series. The first LFS was conducted in 1986. Subsequent surveys were conducted in 2005 and 2008"},"version_statement":{"version":"Version 1.0 Edited Final Data, Anonymized dataset for public use","version_date":"2013-09-01"},"study_info":{"keywords":[{"keyword":"Labor Markets","vocab":"World Bank"},{"keyword":"Social Protection (includes Pensions, Safety Nets, Social Funds)","vocab":"World Bank"},{"keyword":"Social Development","vocab":"World Bank"},{"keyword":"Participation \/ Empowerment","vocab":"World Bank"},{"keyword":"Vocational Education","vocab":"World Bank"},{"keyword":"Gender","vocab":"World Bank"},{"keyword":"Economic Activity","vocab":"World Bank"},{"keyword":"Employment","vocab":"World Bank"},{"keyword":"income","vocab":"World Bank"},{"keyword":"Hours of work","vocab":"World Bank"},{"keyword":"unemployment","vocab":"World Bank"},{"keyword":"Education","vocab":"World Bank"},{"keyword":"Primary education","vocab":"World Bank"},{"keyword":"Secondary Education","vocab":"World Bank"}],"topics":[{"topic":"Labor Markets","vocab":"World Bank"},{"topic":"Social Protection (includes Pensions, Safety Nets, Social Funds)","vocab":"World Bank"},{"topic":"Social Development","vocab":"World Bank"},{"topic":"Participation \/ Empowerment","vocab":"World Bank"},{"topic":"Vocational Education","vocab":"World Bank"},{"topic":"Gender","vocab":"World Bank"},{"topic":"Education","vocab":"World Bank"},{"topic":"Primary Education","vocab":"World Bank"},{"topic":"Secondary Education","vocab":"World Bank"},{"topic":"Tertiary Education","vocab":"World Bank"},{"topic":"Employment","vocab":"World Bank"},{"topic":"Economic Activity","vocab":"World Bank"},{"topic":"Income","vocab":"World Bank"},{"topic":"Hours of work","vocab":"World Bank"}],"abstract":"This survey intends to: -\n\n\u00b7 Measure the labour force or economically active population size in relation to the general population in the country.\n\u00b7 Identify and analyse the factors leading to the emergence and growth of Labour Force in the country.\n\u00b7 Monitor the labour force participation.\n\u00b7 Identify and measure the informal sector from within the labour force.\n\u00b7 Monitor other Key Indicators of the Labour Market such as employment rates,unemployment rates, hours of work, average income and\/or wages etc.\n\nFurthermore, the survey seeks to examine the relationships of socio-economic factors such as education, health, social security, employment within the labour force, and more importantly to measure the causes and effects of children\u2019s involvements in economic activities with special focus on the conditions and environment under which affected children operate.\n\nThe main objective of the 2012 LFS was to collect data on the social and economic activities of the population, including detailed information on employment, unemployment, underemployment, wages, informal sector, general characteristics of the labour force and economically inactive population. The survey was designed to specifically measure and monitor Key Indicators of the Labour Market (KILM) such as employment levels, unemployment, income and child labour in Zambia. However, indicators on child labour are not part of this 2012 LFS report. There will be a separate report on child labour later. The measurement of the KILM was with a view to informing users and policy-makers for decision-making. The methodology used in carrying out the survey and the design of questionnaire conform to internationally acceptable standards.","coll_dates":[{"start":"2012-11-01","end":"2012-12-02"}],"nation":[{"name":"ZAMBIA","abbreviation":"ZMB"}],"geog_coverage":"National\nProvince\nRural\/Urban","analysis_unit":"The unit of analysis was Households and Individuals ( Men and Women of 5 years and older).\n\nAdditionally, the analysis focused on national level at both rural\/urban and provincial level. The micro-data has provisions to generate major indicators at district and constituency levels. As much as possible the micro-data have also been analyzed by sex and age.","universe":"The survey covered all de jure household members (usual residents) in non-institutionalised housing units, all women and men aged 5 years and older","data_kind":"Sample survey data [ssd]","notes":"The 2012 Labour Force Survey (LFS) was a nation-wide survey covering household population in all the ten provinces and, in both rural and urban areas. The survey excluded institutional populations such as those in Hospitals, Barracks, Prisons or Refugee camps. This is because the survey was intended only for usual members of the households - i.e. members who lived together as a household for at least six months or who intended to live together as a household for more than six months - who constituted a household.\n\nThe scope of the 2012 Labour Force Survey includes:\n. Demographic characteristics\n\u00b7 Education and Literacy\n\u00b7 Economic Activity \n\u00b7 Employment\n\u00b7 Hours of Work and underemployment\n\u00b7 Income\n\u00b7 Unemployment"},"method":{"data_collection":{"data_collectors":[{"name":"Central Statistical Office","abbr":"CSO","affiliation":"Ministry of Labour and Social Security"}],"sampling_procedure":"The sample was designed to allow separate estimates at national level for rural and urban areas. Further, it also allowed for provincial estimates. A cluster, which is equivalent to a Standard Enumeration Area\n(SEA), was the primary sampling unit in the first stage. In the second stage, a household was a sampling unit for enumeration purposes.\n\nZambia is administratively divided into ten provinces. Each province is in turn subdivided into districts. For statistical purposes each district is subdivided into Census Supervisory Areas (CSAs) and these are in turn demarcated into Standard Enumeration Areas (SEAs). The Census mapping exercise of 2006-2010 in preparation for the 2010 Census of Population and Housing, demarcated the CSAs within wards, wards within constituencies and constituencies within districts. As at the time of the survey, Zambia had 74 districts, 150 constituencies, 1,430 wards and about 25,000 SEAs. Information borne on the list of SEAs from the sampling frame also includes number of households and the population size as at the last update of the SEA. The number of households determined the selection of primary sampling units (PSU). The SEAs are stratifed as urban and rural.\n\nThe total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census results. The proportional allocation does not however allow for reliable estimates for lower domains like district, ward or constituency. Adjustments to the proportional allocation of the sample were made to allow for reasonable comparison to be achieved between strata or domains. Therefore, disproportionate allocation was adopted, for the purpose of maximizing the precision of survey estimates. The disproportionate allocation is based on the optimal square root allocation method designed by Leslie Kish. The sample was then selected using a stratifed two-stage cluster design.","coll_mode":["Face-to-face [f2f]"],"research_instrument":"Two types of questionnaires (Form A and Forma B) were used to collect data from the household members. Form A was used in the first stage for listing purposes while Form B was used in\nthe second stage for collecting detailed data from the selected households. It was a requirement for each household member to provide responses during the face-to-face interview to the\nquestions that were asked.\n\nThe main questionnaire has ten sections namely:\n\n Demographic Characteristics\n Education, Literacy and Skills Training\n Economic Activity\n Employment\n Hours of Work and Underemployment\nIncome\n Unemployment\/Job Search\nPrevious Work Experience\n Household Chores\nWorking Conditions (i.e. Forced labour)","coll_situation":"A pre-test for the LFS was conducted in March 2012. The objective of the Pre-test was to test the survey instruments. It was also aimed at orienting trainers to the survey instruments. The participants in the pre-test were drawn from the MLSS and CSO.The pilot for the LFS was conducted in June\/July 2012 whose prime objective was to finalise the review of the survey instruments and training of trainers. During the organisation of the pilot survey, training was characterised by role plays in which participants demonstrated how an interview could effectively be conducted both in local and english languages. After the\nfieldwork for the pilot survey, the participants reviewed and finalized all the survey instruments.\n\nTraining of supervisors, which lasted for 14 days, was conducted in August\/September 2012 in Lusaka while that of enumerators was conducted in October the same year in different provinces.\nThe CSO in consultation with the MLSS recruited 288 enumerators and 96 supervisors. The Master Trainers (MTs), who were part of the technical team led the training of supervisors while training of enumerators was led by supervisors. Both training of supervisors and enumerators was guided by the enumerators\u2019 manual which was developed during the pre-test and pilot survey undertakings.\n\nThe method of training was such that each trainer was assigned a topic to lecture on to the trainees based on the manual\u2019s prescriptions. Other sessions were for classroom role plays in which participants had to demonstrate how an interview was expected to be done.\n\n\nField work was conducted in the November-December period in 2012 in all the provinces. The primary data collectors were closely supervised by the Supervisors. Each supervisor had 3\nenumerators to work with in order to collect the data and edit the questionnaires, and code questionnaires for a minimum of six enumeration areas. Inaccessible enumeration areas were\nreplaced while the teams were in the field. In order to achieve smooth data collection, Regional Statisticians (RS) based in the provinces mobilised transport facilities such as bicycles, motorbikes, vehicles and boats from other government departments and local authorities. They also carried out sensitisation activities in order to communicate to the communities about the survey. One of the most effective\napproaches was the communication to the district commissioners, local chiefs and headmen.Te master trainers, together with RSs, were responsible for random spot-checks, supplying additional materials such as questionnaires, fuels and lubricants, and offer any other technical advice required to ensure completeness in terms of coverage and content of the data. In addition, some members of the secretariat from Head Office also complemented the efforts of the master trainers and RSs by visiting data collection sites to check on the quality of work.","act_min":"The primary data collectors were closely supervised by the Supervisors. Each supervisor had 3 enumerators to work with in order to collect the data and edit the questionnaires, and code questionnaires for a minimum of six enumeration areas. Each team used a 4 wheel drive vehicle to travel from cluster to cluster (and where necessary within cluster). Inaccessible enumeration areas were replaced while the teams were in the field.\n\n\nThe general functions of the Supervisor included:\n1. Organizing the Enumerators to successfully complete their assignments.\n2. Ensuring that the work completed by the enumerators meets the required standards.\n3. Communicating with the Master Trainer and Provincial Head on a regular basis reporting on the progress of the listing exercise, relaying problems encountered in the field.\n4. Receiving directives on listing operations and resolutions to problems\n5. Allocating areas (SEAs) to Enumerators, showing Enumerators their SEA boundaries on the ground, issuing of listing books and other materials to the enumerators.\n6. To draw the sample of all households to which the questionnaire is to be administered. These should include replacements when need arises. And record all the information regarding the sampling in the listing book. Follow the instructions as they are laid down. Never allow an enumerator to do the sampling\n7. Providing routine supervision with regard to administrative and personnel matters.\n8. To supervise the Enumerators under him\/her on a daily basis and rotating between Enumerators. Supervisors will lead and superviseon average 5 Enumerators each.\n9. Editing completed listing books and questionnaire for, legibility, completeness, consistency etc.","weight":"Due to the non-proportional allocation of the sample to the different strata, sampling weights were required to ensure actual representative ness of the sample at national level. The sampling  probabilities at First-stage selection of SEAs and probabilities of selecting the households were used to calculate the weights. The weights of the sample are equal to the inverse of the probability of selection.","cleaning_operations":"Data editing took place at a number of stages throughout the processing. These included:\n\n1. Field editing\n2. Office editing and coding\n3. Data entry\n4. Structure checking and completeness\n5. Secondary editing\n6. Strucural checking of SAS data files"},"method_notes":"A team of Information Technology specialists and statisticians at CSO was tasked to develop the data entry screen in CSpro. The screen was developed after the final review of the questionnaire. It was developed to capture data on a case-bycase basis. The raw data file was made to be transferable to other software programs like SPSS, SAS, or STATA.\n\nA one-week training for data entry clerks was conducted during which data entry clerks were oriented to the questionnaire and the data entry screen. The training was meant to ensure that data entry clerks made correct entries into the program. In terms of data cleaning, CSO Information Technology specialists worked together with labour statisticians from the MLSS and CSO.\n\nThe primary basis for data cleaning was the development of syntaxes in the Syntax Editor of the SPSS. The syntaxes were then applied on the raw data to identify possible inconsistencies.\nInconsistencies arising from the raw data were removed through imputational procedures. The 2008 LFS is the basis for the formulation of the statistical tables for the 2012 LFS report. A set of descriptive tables was proposed to senior management in the MLSS and CSO.","analysis_info":{"response_rate":"At the end of the field work and editing in the provinces, a total of at least 11,000 of completed questionnaires, representing a 99.8 percent response rate were sent to Head Office for data processing."}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"The Agency shall,where statistics are designated as official statistics, protect the confidentiality and identity of the source of data.Under the provision of the Statistics ACT no.13 of 2018, ZamStats is obliged to preserve the confidentiality of respondent information in all it's survey data \n\nBefore being granted access to the dataset, all users have to formally agree: \n   1. To make no copies of any files or portions of files to which s\/he is granted access except those authorized by the Agency. \n   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. \n   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 Agency","required":"yes"}],"contact":[{"name":"Zambia Statistics Office","affiliation":"Ministry of Finance and National Planning","email":"info@zamstats.gov.zm","uri":"https:\/\/nada.zamstats.gov.zm"}],"cit_req":"ZamStats(2012). 2012 Labour Force Survey, Lusaka, Zambia","conditions":"Micro data records are  anonymised as per procedures  before these are made available to users.Census micro data will only be provided as a micro sample (10 percent) and subjected to the anonymisation techniques and documented accordingly.\nMicro data files are all free but under access policy Conditions:\n\nEach dataset has an access policy :Public use file- Accessible to all and - Licensed datasets, accessible under 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:\n\n 1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the Zambia Statistics Agency\n 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. \n 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.","disclaimer":"ZamStats will not bear any responsibility for the erroneous use of its data by researchers. Users should report inconsistencies in the data (both micro and aggregated) to ZamStats as soon as possible.\n \nThe 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 use"}}},"schematype":"survey"}