{"type":"survey","study_desc":{"title_statement":{"idno":"BGD_2015_LFS_v01_M","title":"Labour Force Survey 2015","alt_title":"LFS 2015"},"authoring_entity":[{"name":"Bangladesh Bureau of Statistics","affiliation":"Government of Bangladesh"}],"production_statement":{"funding_agencies":[{"name":"International Labour Organization","abbreviation":"ILO","role":""}]},"distribution_statement":{"contact":[{"name":"Bangladesh Bureau of Statistics","affiliation":"Government of Bangladesh","email":"mdkali0501@gmail.com","uri":"http:\/\/www.bbs.gov.bd\/"}]},"series_statement":{"series_name":"Labor Force Survey [hh\/lfs]","series_info":"Bangladesh Bureau of Statistics (BBS) has conducted the Quarterly Labour Force Survey from July 2015 to June 2016. This is the transition of BBS from periodic Labour Force Survey to quarterly survey for the first time. The survey on labour force collected information on various aspects of people\u2019s economic activity and provided labour market statistics relating to employment, unemployment and underemployment and many other aspects of people\u2019s working lives at the national and divisional level with urban and rural breakdown. These statistics provide input for labour market analysis, policy formulation as well as planning, implementing and monitoring programmes related to human resource development in the country. This report also includes statistics on employment and labour force by Urban-Rural-City corporation breakdown and geographical Division. It is hoped that this report meets users\u2019 need for comprehensive and up-to-date labour force statistics."},"version_statement":{"version":"- v1.0"},"study_info":{"abstract":"Bangladesh Bureau of Statistics (BBS), the National Statistical Organization of the country, has been conducting Labour Force Survey (LFS) since 1980 and repeated it every three\/four year until 2013. The surveys could not be held at uniform time intervals due to resource constraint and other reasons. Finally, from July 2015, BBS has undertaken a development project and started implementation of quarterly labour force survey to provide labour market indicators. Gender disaggregated data on labour force, employment, unemployment, underemployment, not in labour force, hours worked, earnings, informal employment. Non-economic activities, volunteer activities are available in this report. The survey found that around half (51.2 per cent) of the 30.5 million employed persons worked more than 48 hours per week. By sex, the proportion of male workers working more than 48 hours (60.9 per cent) was higher than that of female workers (28.4 per cent). By industry, the highest rates of persons in excessive hours were in the Accommodation and food service activities (78.4 per cent), wholesale and retail trade sector (72.9 per cent), manufacturing (69.3 per cent), and households (61.5 per cent). \n\nThe primary objective of the survey was to collect comprehensive data on the Labour Force, employment and unemployment of the population aged 15 or older for use by the Government, international organizations, NGOs, researchers and others to efficiently provide targeted interventions. Specific objectives of the survey:\n\n-  Provide relevant information regarding the characteristics of the population and household that relate to housing, household size, female-headed households;\n\n-  Provide detailed information on education and training, such as literacy, educational attainment and vocational training;\n\n-  Provide relevant information on economic activities and the labour force regarding the working-age population, economic activity status and Labour Force participation;\n\n-  Provide detailed information on employment and informal employment by occupation and industry, education level and status in employment;\n\n-  Provide relevant information on unemployment, the youth labour force participation, youth employment, and youth unemployment;\n\n-  Provide other information on decent work regarding earnings from employment, working hours and time-related underemployment, quality and stability of employment, social security coverage, and safety at work, equal opportunities;\n\n-  Provide relevant information on non-economic activities, volunteer activities etc.","time_periods":[{"start":"2015-07","end":"2015-09","cycle":""},{"start":"2015-10","end":"2015-12","cycle":""}],"coll_dates":[{"start":"2015","end":"2015","cycle":""}],"nation":[{"name":"Bangladesh","abbreviation":"BGD"}],"geog_coverage":"National coverage.","analysis_unit":"-  Individuals\n\n-  Household","universe":"Age is a strong determinant of labour market so a common age cut-off and categories are important. The labour related questions of the survey refer to the population of 15 years old and over. The following age ranges is used in presenting the statistics: 15\u201324; 25\u201334; 35\u201344; 45\u201354; 55\u201364; and 65 and over. Besides, LMI is provided separately for youths as the youths are more prone to unstable transition to labour market. However, in setting the minimum LFS coverage age is the fact that the Government of Bangladesh, being aware that many young people, who are unable to continue with higher schooling, enter the labour market instead, has set the legal age for admission to employment at 14 completed years. Given that, inclusion of persons aged 15 years and over may result in the undercount of persons employed or unemployed in the country.","data_kind":"Sample survey data [ssd]","notes":"The survey report has provided a complete picture of labour statistics as well as the following Key labour market Indicators:\n\n-  Labour force participation rate\n\n-  Employment-to-population ratio\n\n-  Status in employment\n\n-  Employment by sector\n\n-  Employment by occupation\n\n-  Part-time workers\n\n-  Hours of work\n\n-  Employment in the informal economy\n\n-  Unemployment\n\n-  Youth unemployment\n\n-  Long-term unemployment\n\n-  Time-related underemployment\n\n-  Not in labour force\n\n-  Educational attainment and illiteracy\n\n-  Average monthly wages","study_scope":"The survey report has provided a complete picture of labour statistics as well as the following Key labour market Indicators:\n\n-  Labour force participation rate\n\n-  Employment-to-population ratio\n\n-  Status in employment\n\n-  Employment by sector\n\n-  Employment by occupation\n\n-  Part-time workers\n\n-  Hours of work\n\n-  Employment in the informal economy\n\n-  Unemployment\n\n-  Youth unemployment\n\n-  Long-term unemployment\n\n-  Time-related underemployment\n\n-  Not in labour force\n\n-  Educational attainment and illiteracy\n\n-  Average monthly wages"},"method":{"data_collection":{"data_collectors":[{"name":"Bangladesh Bureau of Statistics","abbreviation":"BBS","affiliation":""}],"sampling_procedure":"The frame used for the selection of sample for the survey was based on the Population and Housing Census 2011. Sampling Frame which was made up of preparing of PSUs that is consists of collapsing one or more Enumeration Area (EAs) that was created for the Population and Housing Census 2011. EAs is geographical contiguous areas of land with identifiable boundaries. On average, each PSUs has 225 households. All the Enumeration areas of the country was identified into three segments viz. Strong, Semi-strong and not-strong based on the housing materials. The frame has 1284 PSUs\/EAs spread all over the country, and covers all socio-economic classes and hence able to get a suitable and representative sample of the population. The survey was distributed into twenty-one domains viz. Rural, Urban and City corporations of seven administrative divisions.\n\nFrom each selected PSUs\/EAs, an equal number of 24 households were selected systematically, with a random start. The systematic sampling method was adopted as it enables the distribution of the sample across the cluster evenly and yields good estimates for the population parameters. Selection of the households was done at the HQ and assigned to the Enumerators, with strictly no allowance for replacement of non-responding households. The Bangladesh Quarterly Labor Force Survey (QLFS) sample will be selected in two stages, with small area units called Primary Sampling Units (PSUs) in the first stage and a cluster of 24 households per PSU in the second stage. Both stages are random selections. The survey will implement a rotational panel strategy, in which some of the households in each cluster will be replaced by new households each quarter. The survey launched in July 2015, with a total sample size of about 30,800 households (1,284 PSUs) in each quarter and 123634 in the year 2015-16, intended to deliver reliable quarterly estimates of unemployment and other relevant labor force indicators for of the country's seven divisions and locality viz. national level estimates with disaggregation by City Corporations, Rural and Urban.\n\nThe survey involved a sample of 30816 households from 1284 PSUs\/sample enumeration areas distributed across all the 64 Districts for each quarter and the ultimate sample households for the year 2015-16 was 126000 in total. The survey covered both urban and rural areas and dwelling households, including one person households. The institutional households, that is, those living in hostels, hotels, hospitals, old homes, military and police barracks, prisons, welfare homes and other institutions were excluded from the coverage of the survey.","sampling_deviation":"Most BBS household surveys use a two-stage sampling strategy similar to that of the QLFS, and most of them share a common set of PSUs \u2013 the Integrated Multi-Purpose Sample (IMPS) \u2013 as a basis for their first sampling stage. However, the QLFS, given the specificities of its rotational strategy, has opted for choosing an independent set of PSUs for this purpose. The first stage sample frame of the QLFS was developed on the basis of the list of Enumeration Areas (EAs) generated by the 2011 Census. Some of the original 293,093 EAs were deemed too small to support the adopted rotational panel strategy, and were joined to neighboring EAs in order to create 146,576 PSUs of more adequate size: most of the resulting PSUs have between 150 and 300 households, with an average of 217. Whenever possible, the EAs with less than 150 households were appended to EAs from the same village, although in the most sparsely populated areas it was sometimes necessary to append entire villages to neighboring villages within the same mauza or mahalla (the lower level administrative division of the country.)2 Entire mauzas or mahallas were never appended to neighboring areas, even if they were too small \u2013 they remained as individual PSUs in the sample frame. The second stage sample frame will be a full listing of all households in the selected PSUs. The listings were completed between February and March 2015. If the survey indeed becomes a regular exercise, they should be permanently updated so that they are never older than two years.","coll_mode":"Face-to-face [f2f]","research_instrument":"The Quarterly Labour Force Survey 2015-16 questionnaire comprised 14 sections, as follows:\n\n-  Section 1. Household basic information\n\n-  Section 2. Household roster (members\u2019 basic information)\n\n-  Section 3. General education (for persons aged 5 years or older) & vocational training (for persons aged 15 years or older)\n\n-  Section 4. Working status (for persons aged 15 years or older)\n\n-  Section 5. Main activities (for persons aged 15 years or older)\n\n-  Section 6. Secondary activities (for employed persons aged 15 years or older)\n\n-  Section 7. Occupational safety and health within the previous 12 months (for persons aged 15 years or older)\n\n-  Section 8. Underemployment (for employed persons aged 15 years or older)\n\n-  Section 9. Unemployment (for not employed persons aged 15 years or older)\n\n-  Section 10. Own use production of goods (for persons aged 15 years or older)\n\n-  Section 11. Own use provision of services (for persons aged 15 years or older)\n\n-  Section 12. Unpaid trainee\/apprentice work (for persons aged 15 years or older)\n\n-  Section 13. Volunteer work (for persons aged 15 years or older)\n\n-  Section 14. Migration (for persons aged 15 years or older)","coll_situation":"To aid in identification and access to the household, letters of introduction highlighting survey objectives and identification badges were provided to the Enumerators. Enumerators were advised to visit the households to introduce themselves prior to administer the Questionnaire. Supervising officer also sometimes went for courtesy calls to the Households. It took the Enumerators approximately 30-40 minutes to administer the questionnaire depending on the size of the household. Most of the teams managed to collect the data within the stipulated timeframe. Data collection was carried out using personal interview approach. Officers from BBS and outsourcing Enumerators who were involved in this survey were given special training. They visited selected households (HH) to collect information on demography, labour force, non-economic activities using a set of questionnaires. Field checks were undertaken by experienced officers from the BBS & SID to detect and rectify any invalid information occurred during interview session. In addition, follow-up\/reinterviews of certain selected households was done to ensure the quality of data collected.","act_min":"To properly conduct the nationwide survey, intensive training programmes were arranged for the survey supervisors and enumerators as well as the survey coordinators. In total, 107 enumerators, among them 94 females and 84 supervising officers, were involved and received seven days of training on data collection. The training consisted of five days for training, one day for field testing and one day for reviewing. The training covered instructions in general interviewing techniques, field procedures (including sample selection), a detailed discussion of items on the questionnaire and practice interviews in the field.","weight":"Weights were developped to account for the selection probabilities and also using the design weights of the PSUs. The non-response adjustment and urban-rural calibration were also used. the captured data were exported to STATA format for cleaning and analysis. The cleaned data was weighted before final analysis.\n\nTo obtain unbiased estimators from the sample, the data reported for the household should be affected by a sampling weight (or raising factor) whij, equal to the inverse of its selection probability (whij=1\/phij). If nhi and n\u2019hi were equal in all PSUs, the formula would simplify to a constant and the sample would be self-weighted within each stratum. In practice, nhi and n\u2019hi will rarely by equal but often similar, so the sample will not be exactly self-weighted, but quite approximately so. As the quarterly survey started from July 2015, survey base weights were post-adjusted to estimate total population of July 2015 for the first quarter and and kept same for the successive three quarters of the QLFS 2015-16.","cleaning_operations":"With regard to editing and processing errors, several consistency checks were done, both manually and computerized programme using CSPro; batch editing was done using Stata, to ensure the quality and acceptability of the data produced. The Non-sampling error is to ensure high quality data, several steps were taken to minimize non-sampling errors. Unlike sampling errors, these errors cannot be measured and can only be overcome through several administrative procedures. These errors can arise as a result of incomplete survey coverage, frame defect, response error, non-response and processing errors such as during editing, coding and data capture.","method_notes":"Initial manual editing and coding of industry and occupation classification was done in the BBS headquarters by the selected editors and coders. The supervising officers further checked the questionnaires and validated the data randomly sampled edited questionnaires. Data was captured using Census and Survey Processing System (CSPro) through a data entry screen specially created and incorporated with checks to ensure accuracy during data entry. Erroneous entries and potential outliers were then verified and corrected appropriately. A total of 12 data entry personnel were engaged during the exercise. Weights were developed to account for the selection probabilities. The weights were developed using the design weights of the PSUs. The non-response adjustment and urban-rural calibration was also used. The captured data were exported to STATA format for cleaning and analysis. The cleaned data was weighted before final analysis."},"analysis_info":{"sampling_error_estimates":"Sampling error is a result of estimating data based on a probability sampling, not on census. Such error in statistics is termed as relative standard error and often denoted as RSE which is given in percentage. This error is an indication to the precision of the parameter under study. In other words, it reflects the extent of variation with other sample-based estimates. Sampling errors of estimates on a few important variables at national levels are calculated separately as shown in the annex. For example, the labour force participation rate at the national level was 67.0 per cent with an RSE of 0.23 per cent and standard error (SE) of 0.16 per cent. At 95 per cent confidence interval (a = 0.05), the labour force participation rate was in the range of 66.69\u201367.31 per cent."}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"Personal information will not be disclosed.","required":"yes","form_no":"","uri":""}],"contact":[{"name":"Bangladesh Bureau of Statistics","affiliation":"Government of Bangladesh","email":"dg@bbs.gov.bd","uri":"http:\/\/www.bbs.gov.bd\/home.aspx"}],"cit_req":"Use of the dataset must be acknowledged using a citation which would include:\n\n- The Identification of the Primary Investigator\n\n- The title of the survey (including country, acronym and year of implementation)\n\n- The survey reference number\n\n- The source and date of download\n\nExample:\n \nBangladesh Bureau of Statistics. Bangladesh Labour Force Survey (LFS) 2015. Ref. BGD_2015_LFS_v01_M. Dataset downloaded from [URL] on [date].","conditions":"Public use files, accessible to all.","disclaimer":"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."}}},"data_files":[{"file_id":"F2","file_name":"BGD_2015_LFS_v01_M_SPSS","description":null,"case_count":"0","var_count":"275","producer":null,"data_checks":null,"missing_data":null,"version":null,"notes":null}],"variables":[{"vid":"V276","name":"psu","file_id":"F2","var_dcml":"0","var_intrvl":"discrete","var_start_pos":"1","var_end_pos":"16","var_width":"16","var_rec_seg_no":"1","labl":"PSU","var_sumstat":[{"value":"0","type":"vald"},{"value":"0","type":"invd"}],"var_format":"numeric","var_format_schema":"other","fid":"F2"},{"vid":"V277","name":"hh","file_id":"F2","var_dcml":"0","var_intrvl":"discrete","var_start_pos":"17","var_end_pos":"32","var_width":"16","var_rec_seg_no":"1","labl":"Household","var_sumstat":[{"value":"0","type":"vald"},{"value":"0","type":"invd"}],"var_format":"numeric","var_format_schema":"other","fid":"F2"},{"vid":"V278","name":"ln","file_id":"F2","var_dcml":"0","var_intrvl":"discrete","var_start_pos":"33","var_end_pos":"48","var_width":"16","var_rec_seg_no":"1","labl":"Line","var_sumstat":[{"value":"0","type":"vald"},{"value":"0","type":"invd"}],"var_format":"numeric","var_format_schema":"other","fid":"F2"},{"vid":"V279","name":"q1","file_id":"F2","var_dcml":"0","var_intrvl":"discrete","var_start_pos":"49","var_end_pos":"64","var_width":"16","var_rec_seg_no":"1","labl":"Q1_Ownership of house","var_val_range":[{"units":null,"min":"1","max":"3"}],"var_sumstat":[{"value":"0","type":"vald"},{"value":"0","type":"invd"}],"var_catgry":[{"value":"1","labl":"1. 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Manufacture of machinery for mining, quarrying and con","stats":null,"type":null},{"value":"2825","labl":"2825. Manufacture of machinery for food, beverage and tobacc","stats":null,"type":null},{"value":"2826","labl":"2826. Manufacture of machinery for textile, apparel and leat","stats":null,"type":null},{"value":"2829","labl":"2829. Manufacture of other special-purpose machinery","stats":null,"type":null},{"value":"2910","labl":"2910. Manufacture of motor vehicles","stats":null,"type":null},{"value":"2920","labl":"2920. Manufacture of bodies (coachwork) for motor vehicles;","stats":null,"type":null},{"value":"2930","labl":"2930. Manufacture of parts and accessories for motor vehicle","stats":null,"type":null},{"value":"3011","labl":"3011. Building of ships and floating structures","stats":null,"type":null},{"value":"3012","labl":"3012. Building of pleasure and sporting boats","stats":null,"type":null},{"value":"3013","labl":"3013. Ship breaking and dismantling","stats":null,"type":null},{"value":"3020","labl":"3020. Manufacture of railway locomotives and rolling stock","stats":null,"type":null},{"value":"3030","labl":"3030. Manufacture of air and spacecraft and related machiner","stats":null,"type":null},{"value":"3040","labl":"3040. Manufacture of military fighting vehicles","stats":null,"type":null},{"value":"3091","labl":"3091. Manufacture of motorcycles","stats":null,"type":null},{"value":"3092","labl":"3092. Manufacture of bicycles and invalid carriages","stats":null,"type":null},{"value":"3093","labl":"3093. Manufacture of auto-rickshaw \/ three wheeler","stats":null,"type":null},{"value":"3094","labl":"3094. Manufacture of rickshaw \/ van","stats":null,"type":null},{"value":"3099","labl":"3099. Manufacture of other transport equipment n.e.c.","stats":null,"type":null},{"value":"3100","labl":"3100. Manufacture of metal furniture fixture","stats":null,"type":null},{"value":"3101","labl":"3101. 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Manufacture of gas; distribution of gaseous fuels thro","stats":null,"type":null},{"value":"3530","labl":"3530. Steam and air conditioning supply","stats":null,"type":null},{"value":"3600","labl":"3600. Water collection, treatment and supply","stats":null,"type":null},{"value":"3700","labl":"3700. Sewerage","stats":null,"type":null},{"value":"3811","labl":"3811. Collection of non-hazardous waste","stats":null,"type":null},{"value":"3812","labl":"3812. Collection of hazardous waste","stats":null,"type":null},{"value":"3821","labl":"3821. Treatment and disposal of non-hazardous waste","stats":null,"type":null},{"value":"3822","labl":"3822. Treatment and disposal of hazardous waste","stats":null,"type":null},{"value":"3830","labl":"3830. Materials recovery","stats":null,"type":null},{"value":"3900","labl":"3900. Remediation activities and other waste management serv","stats":null,"type":null},{"value":"4100","labl":"4100. 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Sale of motorcycles","stats":null,"type":null},{"value":"4541","labl":"4541. Maintenance and repairs of motorcycles","stats":null,"type":null},{"value":"4542","labl":"4542. Sale of motorcycle parts and accessories","stats":null,"type":null},{"value":"4543","labl":"4543. Sale of auto-rickshaw and related parts and accessorie","stats":null,"type":null},{"value":"4544","labl":"4544. Maintenance and repairs of auto-rickshaw","stats":null,"type":null},{"value":"4545","labl":"4545. Sale of auto-rickshaw parts and accessories","stats":null,"type":null},{"value":"4546","labl":"4546. Sale, maintenance and repairs of rickshaws and related","stats":null,"type":null},{"value":"4547","labl":"4547. Wholesale and retail sale of transport vehicles and re","stats":null,"type":null},{"value":"4610","labl":"4610. Wholesale on a fee or contract basis","stats":null,"type":null},{"value":"4611","labl":"4611. Importers and exporters","stats":null,"type":null},{"value":"4620","labl":"4620. 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