{"doc_desc":{"title":"General Household Survey-Panel (Post-Planting 2010)","idno":"DDI-NGA-NBS-GHS-PANEL-2010-v1.0","producers":[{"name":"National Bureau of Statistics","abbr":"NBS","affiliation":"Federal Government of Nigeria (FGN)","role":" Metadata Producer"}],"prod_date":"2011-06-28","version_statement":{"version":"Version 1.0"}},"study_desc":{"title_statement":{"idno":"NGA-NBS-GHS-PANEL-2010-v1.0","title":"General Household Survey-Panel (Post-Planting 2010)","sub_title":"First round","alternate_title":"GHS-PANEL 2010","translated_title":"No translation"},"authoring_entity":[{"name":"National Bureau of Statistics  (NBS)","affiliation":"Federal Government of Nigeria (FGN)"}],"oth_id":[{"name":"Federal Ministry of Agriculture and Rural Development","affiliation":"FMA&RD","email":"","role":"Technical advisory"},{"name":"Federal Ministry of Water Resources","affiliation":" FMWR ","email":"","role":"Technical advisory"},{"name":"National Food Reserve Agency","affiliation":"NFRA","email":"","role":"Technical advisory"}],"production_statement":{"producers":[{"name":"World Bank","abbr":"WB","affiliation":"","role":"funding and Technical advisory"}],"copyright":"\u00a9 NBS 2011","prod_date":"2006-05-10","funding_agencies":[{"name":"Federal Government of Nigeria","abbr":"FGN","role":"Funding"},{"name":"Bill and Melinda Gates Foundation","abbr":"BMGF ","role":"Funding"},{"name":"World Bank","abbr":"WB","role":"Funding"}]},"distribution_statement":{"distributors":[{"name":"NATIONAL BUREAU OF STATISTICS","abbr":"NBS","affiliation":"FEDERAL GOVT. OF NIGERIA","uri":""}],"contact":[{"name":"Alhaji R. A. Sanusi","affiliation":"AC SG National Bureau of Statistics","email":"rasanusi@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Mr E.O. Ekezie","affiliation":"HOD ICT","email":"eoekezie@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Mr C.O. Monike","affiliation":"Fedral Government of Nigeria (FGN)","email":"comonike@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Biyi Fafunmi","affiliation":"Data Access","email":"biyifafunmi@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Mrs A. A. Akinsanya","affiliation":"Data Archivist","email":"paakinsanya@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"Mr R.F. Busari","affiliation":"ICT","email":"rfbusari@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"},{"name":"National Bureau of Statistics (NBS)","affiliation":"Fedral Government of Nigeria (FGN)","email":"feedback@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"}]},"series_statement":{"series_name":"Living Standards Measurement Study [hh\/lsms]","series_info":"In the past decades, Nigeria has experienced substantial gaps in producing adequate and timely data to inform policy making. In particular, the country is lagging behind in producing sufficient and accurate agricultural production statistics. The current set of household and farm surveys conducted by the National Bureau of Statistics (NBS) cover a wide range of sectors, usually in separate surveys, except for the Harmonized National Living Standard Survey (HNLSS) which covers multiple topics. However, none of these surveys is conducted as a panel.\nPilot Test was done in Six selected States namely, Kaduna,  Nasarawa Taraba, Osun, Edo and Enugu.\n\n The NBS has implemented the  General Household Survey Panel (GHS-PANEL) which has been integrated into the current General Household Survey (GHS) . This survey will be conducted every 2 years. \n\nTowards the goal of improving agricultural statistics, the World Bank, through funding from the Bill and Melinda Gates Foundation (BMGF), is supporting seven countries in Sub-Saharan Africa in strengthening the production of household-level data on agriculture. The over-arching objective of the Living Standards Measurement Study \u2013\nIntegrated Surveys on Agriculture (LSMS-ISA) program is to improve our understanding of agriculture in Sub-Saharan Africa \u2013 specifically, its role in household\nwelfare and poverty reduction, and how innovation and efficiency can be fostered in the sector. This goal will be achieved by developing and implementing an innovative\nmodel for collecting agricultural data in the region.\n This is the first time the survey is coming up."},"version_statement":{"version":"version 1.0","version_date":"2011-06-28","version_notes":"v1.0  was original release in June 2011"},"study_info":{"topics":[{"topic":"economic conditions and indicators [1.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"income, property and investment\/saving [1.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"employment [3.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"unemployment [3.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"working conditions [3.6]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"compulsory and pre-school education [6.2]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"vocational education [6.7]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"housing [10.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"children [12.1]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"gender and gender roles [12.6]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"religion and values [13.5]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"health policy [8.6]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"plant and animal distribution [9.4]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"TRANSPORT, TRAVEL AND MOBILITY [11]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"},{"topic":"time use [13.9]","vocab":"CESSDA","uri":"http:\/\/www.nesstar.org\/rdf\/common"}],"abstract":"Towards the goal of improving agricultural statistics, the World Bank, through funding from the Bill and Melinda Gates Foundation (BMGF), is supporting seven countries in Sub-Saharan Africa in strengthening the production of household-level data on agriculture. \n\nThe over-arching objective of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) program is to improve our understanding of agriculture in Sub-Saharan Africa - specifically, its role in household welfare and poverty reduction, and how innovation and efficiency can be fostered in the sector. This goal will be achieved by developing and implementing an innovative model for collecting agricultural data in the region.\n\nExpected Benefits:\n\nThe specific outputs and outcomes of the revised GHS with panel component are: \n\tDevelopment of an innovative model for collecting agricultural data in conjunction with household data;\n\tDevelopment of a model of inter-institutional collaboration between NBS and the FMA&RD and NFRA, inter alia, to ensure the relevance and use of the new \n               GHS;\n\tBuilding the capacity to generate a sustainable system for the production of accurate and timely information on agricultural households in Nigeria.\n\tComprehensive analysis of poverty indictors and socio-economic characteristics.\n\nInnovations\n\nThe revised GHS with panel component contains several innovative features.\n\tIntegration of agricultural data at the plot level with household welfare data;\n\tCreation of a panel data set that can be used to study poverty dynamics, the role of agriculture in development and the changes over time in health,\n               education and other labor activities, inter alia.  \n\tUse of small area estimation techniques (SAE) to generate state level poverty data by taking advantage of the integration of the panel households into the\n               full GHS.   \n \tCollection of information on the network of buyers and sellers of goods that household interact with;\n \tUse of GPS units for measuring agricultural land areas;\n\tInvolvement of multiple actors in government, academia and the donor community in the development of the survey and its contents as well as its\n               implementation and analysis; \n \tUse of concurrent data entry in Wave 1. In later Waves the project will develop and implement  a Computer  Assisted Personal Interview (CAPI) application\n               for the paperless collection of the GHS;\n \tUse of direct respondents for all sections of the questionnaires where individual level data or specific economic activity data are collected;\n \tCreation of a publicly available micro data sets for researchers and policy makers;\n \tActive dissemination of agriculture statistics.","time_periods":[{"start":"2010-08","end":"2011-03","cycle":"2 yrs"}],"coll_dates":[{"start":"2010-08-31","end":"2010-10-15","cycle":"six weeks"}],"nation":[{"name":"Nigeria","abbreviation":"NGA"}],"geog_coverage":"National Zone State Local Government Sector (Urban\/Rural)","geog_unit":"Zone","analysis_unit":"Household, individual, Farm, Plot and Crop","universe":"Household members","data_kind":"Sample survey data [ssd]","notes":"The survey covered a wide range of socio-economic topics which are highlighted  two different questionnaires administered to the household. These are the Household Questionnaire and the Agricultural Questionnaire.\n\nThe household questionnaire  was to be administered to all households in the sample.   \nThe survey covered a wide range of socio-economic topics which are highlighted. Household Questionnaire was used to collect information on\n\n-\tHousehold identification\n-\tHousehold member roster, demographics and migration\n-\tEducation Status\n-\tLabour  and Time use \n-\tCredit and Savings\n-\tHousehold Assets\n-\tNon-Farm Enterprises\n-\tConsumption of food (recall)\n-\tNon-food consumption expenditure\n-\tFood security\n-\tOther non-labour income sources\n\n  Agricultural Questionnaire collected information on:\n               Basic crop, livestock, poultry, fishery, and forestry production, storage and sales\n               Productivity of main crops, with emphasis on improved measures of:\n               Quantification of production\n               Plot size\n               Production stocks (pest, etc)\n               Land Holdings\n               Size and tenure\/ titling\n               Transaction\n               Access to and use of services, infrastructure and natural resources\n               Agricultural Extension Services\n               Infrastructure (including roads)\n               Credit ( both for agriculture and other purposes)\n               Market access\n               Access to information\n               Access to natural and common property resources\n               Input use and technology adoption\n               Family and hired labour\n               Use of technology and farming implements\n               Seed varieties\n                Fertilizer, pesticides etc.\n            - GPS measure of plot size, etc"},"method":{"data_collection":{"time_method":"September 2003 to August 2004","data_collectors":[{"name":"National Bureau of Statistics","abbr":"NBS","role":"","affiliation":"Federal Government of Nigeria"}],"sampling_procedure":"National Integrated Survey of Households (NISH)-2007\/2012 Master Sample Frame (MSF) was adopted.  \n\nIn order to select the NISH sub-sample of EAs in each state, the thirty (30) master sample EAs in each LGA for that state were pooled together such that the total number of the EAs in the LGA master sample for each state is equal to 30 times the number of the LGAs in the state except in FCT, Abuja where it is 40 times.\n\nThereafter, a systematic sample of 200 sample EAs were selected with equal probability across all LGAs within the state.  Furthermore, the NISH EAs in each state were divided into 20 replicates of 10 EAs each, however, the sample EAs for most national household surveys such as the GHS are based on a sub-sample of the NISH master sample, selected as a combination of replicates from NISH frame in which the Household Panel was a subset of the GHS EAs 2010\n\nThe sample frame includes all thirty-six (36) states of the federation and Federal Capital Territory (FCT), Abuja. Both urban and rural areas were covered and in all, 500 clusters\/EAs were canvassed and 5,000 households were interviewed. These samples were proportionally selected in the states such that different states have different samples.  The distribution of the samples are shown in the table 3.1 below which shows the site of the sample in each state, allocation of EAs, households covered, field personnel used and the number of days for fieldwork by zone and state for the GHS Panel main survey 2010 (Post-Planting).\n\nThe Panel Survey used a two stage stratified sample selection process.\n\nFirst Stage:\nThe Primary Sampling Units (PSUs) were the Enumeration Areas (EAs).  These were selected based on probability proportional to size (PPS) of the total EAs in each state and FCT, Abuja and the total households listed in those EAs.\n\nSecond Stage:\n\nThe second stage involved the systematic selection of ten (10) households per EA. This involved obtaining the total number of households listed in a particular EA, and then calculating a Sampling Interval (S.I) by dividing the total households listed by ten (10).  The next step is to generate a random start 'r' from the table of random numbers which stands as the 1st selection. The second selection is obtained by adding the sampling interval to the random start.  For each of the next  selections, the sampling interval was added to the value of the previous selection until the 10th selection is obtained.\nDetermination of the sample size at the household level was based on the experience gained from previous rounds of the GHS cross section, in which 10 HHs per EA are usually selected and give robust estimates.","sampling_deviation":"No deviation from the sampling","coll_mode":["Face-to-face [f2f]"],"research_instrument":"The questionnaire is a structured questionnaire developed as a joint effort of the National Bureau of Statistics, the World Ban, Federal Ministry of Agriculture and Rural Development. Federal Ministry of Water Resources and National Food Reserve Agency during a series of meeting and two consultative workshops.  \nThese are the Household Questionnaire and the Agricultural Questionnaire.\n\nThe household questionnaire  consist of:   \n\nSECTION 1: HOUSEHOLD MEMBER ROSTER\nSECTION 2: EDUCATION\nSECTION 3: LABOUR\nSECTION 4: CREDIT AND SAVINGS\nSECTION 5: HOUSEHOLD ASSETS\nSECTION 6: NONFARM ENTERPRISES AND INCOME GENERATING ACTIVITIES\nSECTION 7A: MEALS AWAY FROM HOME EXPENDITURES\nSECTION 7B: FOOD EXPENDITURES\nSECTION 8: NON-FOOD EXPENDITURES\nSECTION 9: FOOD SECURITY\nSECTION 10: OTHER INCOME\n\nSections 7A, 7B and 8 are not included in the present data.\nThese data sets will be given when the Post Hrvest data set is avaliable. \n\nThe Agricultural Questionnaire:\n\nSECTIONS 11:  \n        a PLOT ROSTER\n        b LAND INVENTORY\n        c INPUT COSTS\n        d FERTILIZER ACQUISITION\n        e SEED ACQUISITION\n        f PLANTED FIELD CROPS\n        g PLANTED TREE CROPS \n        h MARKETING OF AGRICULTURAL SURPLUS\n        i ANIMAL HOLDINGS\n        j ANIMAL COSTS\n        k AGRICULTURE BY-PRODUCT\n        l EXTENSION\nSECTIONS 12: NETWORK ROSTER","sources":[{"name":"","origin":"","characteristics":""}],"coll_situation":"Fieldwork started on Augrst 31st, 2010 and was administered simultaneously throughout the country till mid October, 2010.  All three (3) questionnaires; Household, Agriculture and Community were used to collect information on Post-Planting activities.  Data were collected by teams comprised of a supervisor, 2-4 interviewer(s) and a data entry operator .,   The number of team(s) varied from state to state.  The teams moved in a roving manner and data collection lasted for between 25 - 35  days.  See table 3.1 in the report attached in external resources","act_min":"To ensure that good quality data are collected, a monitoring exercise was mounted.  One (1) monitor was assigned to 2 - 4 states and all the states and FCT, Abuja were covered. There were three levels of monitoring and evaluation, the first and the third levels were carried out by NBS state officers and zonal controllers while the second level was carried out by the technical team which was comprised of the National Bureau of Statistics (NBS), the Federal Ministry of Agriculture and Rural Development (FMA&RD), the National Food Reserve Agency (NFRA) headquarter staff, World Bank officials and consultants.\n\nThe monitors made sure that proper compliance with the laid down procedures as contained in the manual were followed, effected necessary corrections and tackled problems that arose.  The monitoring exercise was arranged such that the first level took place at the commencement of the fieldwork, and the third level not later than a week before the end of the data collection exercise.  In-between these two, the technical team visited all the states of the federation and FCT, Abuja.  While NBS state officers monitored in their state, the zonal controllers monitored in at least two (2) states (the zonal headquarters state and one other state of the same zone).  The  1st and 2nd rounds of the monitoring exercise lasted for nine (9) days while the 2nd round by the technical team lasted for eight (8) days.  Monitoring instruments were developed and discussed during both training of trainers and zonal training.","weight":"Population weight was calculated for the panel household.  This weight variable (WGHT) has been included in household dataset: Section A (SECTA).  When applied, this weight will raised the sample households and individuals to national values.\n\nFor any analysis, the SECTA data set will need to be merged with the file that is to be used.","cleaning_operations":"The data cleaning process was done in a number of stages.  The first step was to ensure proper quality control during the fieldwork.  This was achieved in part by using the concurrent data entry system which was, as explained above,  designed to highlight many of the errors that occurred during the fieldwork.  At this stage errors that are caught at the fieldwork stage are corrected based on the instruction of the supervisor.  The data that had gone through this first stage of cleaning was then sent from the state to the head office of NBS where a second stage of data cleaning was undertaken.  \n\nDuring the second stage the data were examined for out of range values and outliers.  The data were also examined for missing information for required variables, sections, questionnaires and EAs.  This problem was then reported back to the state where the correction was then made.  This was an ongoing process until all data were delivered to the head office.  \n\nAfter all the data were received by the head office, there was an overall review of the data to identify outliers and other errors on the complete set of data.  Where problems were identified, this was reported to the state.  There the questionnaires were checked and where necessary the relevant households were revisited and a report sent back to the head office with the corrections.\n\nThe final stage of the cleaning process was to ensure that the households and individuals were correctly merged across all sections of the household questionnaire.  Special care was taken to see that the households included in the data matched with the selected sample and where there were differences these were properly assessed and documented.  The agriculture data were also checked to ensure that the plot identified in the main sections merged with the plot information identified in the other sections.  This was also done for crop by plot information as well."},"method_notes":"This survey used  the concurrent data entry approach. In this method, the fieldwork and data entry was handled by one or two teams assigned to the state. Each team consisted of a field supervisor, 3-4 interviewers and a data entry operator.\n\n Immediately after the data was collected in the field by the interviewers, the questionnaires were handed over to the supervisor to be checked and documented.  The questionnaires were then passed to the data entry operator at the end of each day of fieldwork for entry. After the questionnaires were entered the data entry operator generated an error report which reported issues including out of range values and  inconsistencies in the data. \n\nThe supervisor then checked the report, determined what should be corrected, and decided if the field team needed to revisit the household to obtain additional information.\n  \nThe benefits of this method were:\n\n1.\tTo capture errors that might have been overlooked by a visual inspection only\n2.\tTo identify errors early during the field work so that if any correction required a revisit to the household, it could be done while the team was still in the EA.\n\nThe CSPro software was used to design the specialised data entry program that was used for the data entry of the questionnaires.\n\nThe cleaning process at the head office was impeded by the fact that the questionnaires were not immediately available for inspection when problems were identified in the data . The questionnaires were retained by the state in case there was the need for household revisits. So whenever problems were identified at the head office, the state office had to be contacted in order to determine if the suspect data were the same as the information on the questionnaire, and to ensure that changes were captured in both places. This was a very cumbersome and time consuming process since communication was difficult and in many instances the response was not timely. \n\nA second challenge in data management and cleaning was the difficulty faced by state offices in sending the data from the state to the head office.  There were difficulties in accessing internet facilities in many of the EAs and surrounding areas where the field teams were active. The consequence of this was that the data were not sent to the head office until the teams returned to state capitals where, due to the distance, it was difficult to return to the EAs for household revisits when requested by the head office.","analysis_info":{"response_rate":"The response rate 99.9% includeing replacements at household level.\nReplacement households represent 17.9% of the sample.","data_appraisal":"VARIABLE NAMING SCHEME\nGenerally, the variables are named to correspond with each of the questions. For example in the case of the cover dataset (SECTA) the variables names start with \u2018SA\u2019 which means section A of the household questionnaire. This is followed by \u2018Q\u2019 and a number e.g. \u2018Q1\u2019 which indicates the question number, so the first question in Section A is captured in the variable SAQ1. Section 1 to 10, was represented using S1 to S10 with the question (Q) and number post-fixed as in the example above. The approach is similar in the case of the agriculture datasets. Here the variables are labeled \u2018S11A \u2013 S11L and S12 corresponding to the section number. These variables all end with the question and number just as is done in the household datasets.\n\nThere were some few data entry problems encountered by some of the data entry operators due to the introduction of this method of concurrent data entry\n? Provision of field vehicles with charging facilities for the data entry equipment was an added advantage\n? Challenges on how to send data via internet to NBS headquarters\n? Problems in effective managing of data problems while the teams were in the field like; printing and correct reading of error messages\n? Problems of EA and HH replacement:\no Suggested a re-listing exercise\no Improve method of replacement\n? Problems with geographical codes\no Using different codes in the states and headquarters for LGAs, EAs and replicate identification codes (RIC)\no Suggested harmonization of codes"}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"The confidentiality of the individual respondent is protected by law (Statistical Act 2007)\nThis is published in the Official Gazette of the Federal republic of Nigeria No. 60 vol. 94 of 11th June 2007. See section 26 para.2. Punitive measures for breeches of confidentiality are outlined in section 28 of the same Act.","required":"yes","form_no":"","form_uri":""}],"contact":[{"name":"National Bureau of Statistics (NBS)","affiliation":"Federal Government of Nigeria (FGN)","email":"feedback@nigerianstat.gov.ng","uri":"http:\/\/www.nigerianstat.gov.ng"}],"cit_req":"National Bureau of Statistics, General Household Survey-Panel (Post-Planting 2010) v1.0","conditions":"A comprehensive data access policy is been developed by NBS, however section 27 of the Statistical Act 2007outlines the data access obligation of data producers which includes the realease of properly anonymized micro data.","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."}}},"schematype":"survey"}