NGA-NBS-NGHPH-2016-v1.0
General Household Survey-Panel Wave 3 (Post Harvest) 2015-2016
Third round
NGHPH 2015-2016
No Translation
Name | Country code |
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Nigeria | NGA |
Other Household Survey [hh/oth]
his General Household Survey-Panel is the second round of Panel surveys, previously conducted in in 2010/11 (Wave 1), 2012/13 (Wave 2), of a long-term project to collect panel data on households, their characteristics, welfare and their agricultural activities. The survey is the result of a partnership that NBS has established with the Federal Ministry of Agriculture and Rural Development (FMA&RD), the National Food Reserve Agency (NFRA), the Bill and Melinda Gates Foundation (BMGF) and the World Bank (WB). Under this partnership, a method to collect agricultural and household data in such a way as to allow the study of agriculture's role in household welfare over time was developed. Thus far, the third waves of the GHS-Panel was conducted in 2015/16 .
The Nigerian General Household Survey (GHS) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program and was revised in 2010 to include a panel component (GHS-Panel). The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, inter-institutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of 5,000 households, which are also representative of the geopolitical zones (at both the urban and rural level). The households included in the GHS-Panel are a sub-sample of the overall GHS sample households. This report presents findings from the third wave of the GHS-Panel, which was implemented in 2015-2016.
The survey finds that average household size is 5.9 and 4.9 persons in rural and urban areas, respectively. The numbers in this wave of the survey do not reflect any significant change in average household size at the national level since Wave 2 of the survey conducted 3 years before in 2012/13. Regionally, the greatest changes occurred in the North East and North West where the average number of household members increased by 0.6 and 0.5 persons respectively. The dependency ratio in rural areas (1.1%) is slightly higher than that in urban areas (0.9%) where it has remained unchanged since Wave 2.
The survey captures educational outcomes of household members through self-reported literacy, attendance, and attainment, as well as constraints to school enrollment such as proximity to school and school expenses. Similar to Wave 2, the present survey results show that the highest literacy rates for both males and females occurs among those between 15 to 19 years of age. Between the ages of 5 and 14, 68.7 percent of male children, and 65.4 percent of female children, are enrolled in a type of primary or secondary school; however, government school enrollment far exceeds private. The most cited reasons why children are not enrolled in school are no interest, too young to be in school, and school too far from households dwelling.
The questionnaire gathers information on recent illnesses, disability, healthcare utilization, and child anthropometrics. The data shows 13.7 and 15.2 percent of men and women, respectively, reported having an illness in the 4 weeks preceding the survey. For women over 65 years, this number jumps to 38.9 percent. Similar to Wave 2, individuals who reported being ill in the 4 weeks preceding the survey were most likely to seek care at a hospital (27.9% for men and 28.3% for women) or with a chemist (33.2% for men and 35.5% for women). On average, households allocate a larger proportion of health expenditure to drugs (74.7% for male and 71.3% for females) and consultation (14.5% for males and 15.6% for females). More than 50 percent of households live less than 30 minutes from the nearest hospital or health facility, though a small fraction live more than 2 hours from any sufficient healthcare services. Child anthropometric results indicate that 39.4 percent of boys and 35.4 percent of girls are stunted (low height-for-age). Generally, stunting and underweight prevalence estimates are found to be higher in rural than in urban areas.
The GHS-Panel also collected data on housing tenure and characteristics. Findings show that over 68.5 percent of households own their dwelling and 16.6 percent of households rent their homes. Although 63.6 percent of households live in homes with 3 or more rooms, the quality of the building material remains poor. Nationally, more than 59.3 percent of households have electricity (for an average of 35.8 hours per week), with no considerable change from Wave 2. However, there is a large disparity in access between urban and rural areas: 86 percent of urban households have electricity compared to only 41.1 percent of rural households.
Households were asked if they owned various assets including farm implements, home furniture, durables, entertainment equipment, and automobiles, among many others. About 94 percent of households own a mattress, 82 percent own a bed, and 76 percent own mats. The data suggest that rudimentary farm implements, such as hoes and cutlasses, are considerably more common than modern tools such as tractors and pickup trucks.
The survey collects information on households' access to information and communication technology (ICT) and patterns of usage. Findings reveal that nearly all persons 10 years or older (89%) have access to a mobile phone. Access the internet is more prevalent in urban areas than in rural areas (29.0% versus 9.8% of those 10 years or older); the most common uses are to send and receive emails (45.8%) and engage in educational activities (18.4%).
The survey included questions on food and non-food expenditure, food shortages, shocks, and coping mechanisms. Overall oil and fat products along with grains and flours are the most commonly consumed food items with over 96 percent of households consuming food items in these groups. This is closely followed by vegetables (96.7%), and meat, fish and animal products (88.9%). Fruits and dairy products continue to be reported as the least prevalent food consumed. While grains and flour are the most commonly consumed food group, average household expenditure is highest for meat, fish, and animal products. Figures from the present survey show an increase in consumption of the most popular food groups compared with the values obtained for Wave 2 of the GHS-Panel. Soap and mobile recharge cards are the most common non-food items consumed by households, with close to 9 out of 10 households reporting soap purchases and 78.3 percent reporting expenditures on recharge cards. Mobile recharge cards also account for the highest national mean expenditure, with a monthly average household expenditure of N17,413.
Households were also asked about their experience with food security and their history of economic shocks. Similar to findings in Wave 2, reported food shortages from this wave are seasonal, with January and February posing the biggest risk of food insecurity. Twenty-six percent of households reported having to reduce the number of meals taken in the past 7 days, with urban households more likely to have reduced their meal intake than rural households (29.8% versus 24.1%). Major shocks that negatively affected households include: increase in the price of food items (12.4%), death or disability of a working household member (5.7%), increase in the price of inputs (3.6%), and nonfarm enterprise failure (3.1%). The most common coping mechanisms reported include receipt of assistance from family and friends (24%) and reduction in food consumption (23.6%).
According to survey results, agriculture is the most common income-generating activity, followed by working in a household nonfarm enterprise, and then wage employment. Among working individuals aged 5 to 14, agriculture is the most prevalent income-generating activity. The vast majority of persons with no work activity in the past 7 days are students or women performing household chores and child care. Sixty-seven percent of households operate at least one nonfarm enterprise. The most common types of nonfarm enterprises were retail trade (59.0%) and provision of personal services (10.2%). Households are most likely to acquire the start-up capital for these enterprises through household savings (46%) or friends and relatives (29.1%).
Household members were also asked about time spent collecting fuel wood and water and, as might be expected, more time is allocated to these activities in rural areas than in urban areas. The data show that, nationally, men and women who perform these tasks spend similar amounts of time doing so, though men were less likely to collect firewood than women. Regionally, the difference between male and female participation is generally greater. For example, in the North Central region, 71.3 percent of women collected firewood the previous day compared to only 42.5 percent of men.
The survey's agriculture modules cover crop farming and livestock rearing. Results show that each agricultural household holds an average of 2.6 plots at an average of 0.5 hectares in size. Nationally, only 7 percent of male-managed plots and 2.2 percent of female-managed plots are owned through outright purchase, though almost 31.6 percent of female-managed plots in the North West region were acquired through outright purchased. The most common means of acquiring land is through family inheritance - 71 percent of male-managed plots and 69 percent of female-managed plots are acquired through this method. Fertilizer, herbicides, and pesticides are applied in approximately 47.3 percent, 30.5 percent, and 20.7 percent of plots, respectively. Purchased seeds and animal traction are also common forms of agricultural input. The survey data indicates that goat is the most common animal owned among livestock owning households across all regions (67.3%). Overall, male-headed households own more animals than female-headed households. The majority of livestock owning households reported slaughtering (29%) or selling (28.5%) livestock.
Sample survey data [ssd]
Agricultural Households.
Version 1.0
2016-12-05
This is the first version to be released before review process.
The survey will cover a wide range of socio-economic topics which are highlighted in three different questionnaires to be used for data collection. These are Household Questionnaire, Agricultural Questionnaire and Community/Prices Questionnaire.
The post-harvest household questionnaire collected information on:
· Household Identification
· Household Member Roster, Demographic and Migration
· Education Status
· Labour (Adults and Children 5yrs+)
· Health and Child Development
· Remittances
· Behavior and Attitudes
· Non-Farm Enterprises and Income Generating Activities
· Consumption of Food (Recall)
· Non-Food Consumption Expenditure
· Food Security
· Other Household Income
· Safety Nets, Economic Shocks and Deaths
· Conflict
The post-harvest agriculture questionnaire collected information on:
Productivity of main crops, with emphasis on improved measures of:
· Land Holdings
· Family and Hired Labour
· Input Costs
· Fertilizer Acquisition
· Quantification of Crop Production and Disposition
· Agricultural Capital
· Agricultural Extension Services
· Other Agricultural Income Including Income from Agricultural By-Products
· Fishing Capital and Revenue
The community questionnaire collected information on:
· Assess to Community Characteristics Including Infrastructure
· Access to Public Services, Social Networks, Governance, Investment Projects and Necessary Community Empowerment etc.
· Communal Resource Management
· Changes in the Community and Key Events Leading to Changes
· Community Needs, Actions and Achievements over the Past Years
· Prices of Food Items at the Community Level
· Conflict at the Community Level
National Zonal State Sector
Agricultural Farming Household Members.
Name | Affiliation |
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National Bureau of Statistics (NBS) | Federal Government of Nigeria (FGN) |
Name | Affiliation | Role |
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World Bank | Funding | |
Federal Ministry of Agriculture and Rural Development | Federal Government of Nigeria (FGN) | Technical Assitance |
National Food Reserved Agency | Federal Government of Nigeria (FGN) | Technical Assitance |
Name | Abbreviation | Role |
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Federal Government of Nigeria | FGN | Funding |
World Bank | WB | Funding |
Bill and Melinda Gates Foundation | Funding |
Name | Affiliation | Role |
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Federal Ministry of Water Resources | FMWR | Technical Assistance |
Federal Department of Agricultural Extension | FDAE | Technical Assistance |
The GHS-Panel sample is fully integrated with the 2010 GHS Sample. The GHS sample is comprised of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs) chosen from each of the 37 states in Nigeria. This results in a total of 2,220 EAs nationally. Each EA contributes 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,000 households, 5,000 households from 500 EAs were selected for the panel component and 4,916 households completed their interviews in the first wave. Given the panel nature of the survey, some households had moved from their location and were not able to be located by the time of the Wave 3 visit, resulting in a slightly smaller sample of 4,581 households for Wave 3.
In order to collect detailed and accurate information on agricultural activities, GHS-Panel households are visited twice: first after the planting season (post-planting) between August and October and second after the harvest season (post-harvest) between February and April. All households are visited twice regardless of whether they participated in agricultural activities. Some important factors such as labour, food consumption, and expenditures are collected during both visits. Unless otherwise specified, the majority of the report will focus on the most recent information, collected during the post-harvest visit.
No deviation
The response rate was very high
opulation 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.
The questionnaire is a structured questionnaire developed as a joint effort of the National Bureau of Statistics, the World Bank and National Planning Commission. After series of meeting and two consultative workshops.
The information collected are stated below in each module of the questionaire:
Household Questionaire
Section 1: Roster
Section 2: Education for members in the household
Section 3A: Labour
Section 4: Health
Section 4B: Child development
Section 6: Remittances
Section 6A: Behaviour
Section 6B: Attitude
Section 9: Non-farm enterprises
Section 10A: Meals outside the household
Section 10B: Food consumption and expenditures
Section 10C: Aggregate food consumption
Section 12: Food security
Section 13: Other income
Section 15A: Economic shocks
Section 15B: Deaths
Section 15C: Conflict
Contact Information
Agricultural Questionaire
Section A1:Land
Section A2: Labour
Section 11C2: Input costs
Section 11D: Fertilizer acquisition
Section A3I: Crop harvest
Section A3II: Crop disposition
Section A4: Agricultural capital
Section A5A: Extension services (topics)
Section A5B: Extension services (sources)
Section A9A: Fishing
Section A9B: Fishing capital & revenues
Section A10: Network roster
The Community Questionnaire
Section 1: Assess to Community Characteristics Including Infrastructure
Section 2: Access to Public Services, Social Networks, Governance, Investment Projects and Necessary Community Empowerment etc.
Section 3:Communal Resource Management
Section 4: Changes in the Community and Key Events Leading to Changes
Section 5:Community Needs, Actions and Achievements over the Past Years
Section 6: Prices of Food Items at the Community Level
Section 7: Conflict at the Community Level
The CSPro software was used to design the specialised data entry program that was used for the data entry of the questionnaires.
The 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.
Data cleaning and processing was an ongoing operation while the data were being collected in the field and after. Field staff and data entry operators were required to respond to data quality enquires from the Headquarters (HQ). There was a joint review of the data by HQ and field staff to ensure that the data collected is of the highest quality. The work of field staff (including data entry operators) were completed only when the data was signed-off as being satisfactory by the HQ and the World Bank.
Start | End | Cycle |
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2016-02-22 | 2016-03-24 | 4 weeks |
Start date | End date | Cycle |
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2016 | 2016 | 1 month |
Name | Affiliation | Abbreviation |
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National Bureau of Statistics | Federal Government of Nigeria | NBS |
Senior staff from NBS, FMA&RD and NFRA conducted the monitoring and supervision exercises. The monitoring officers ensured proper compliance with the laid down procedures as contained in the manual.The activities were as follows:
-All states and FCT Abuja were monitored
-There were 3 levels of monitoring
-The first and third levels was carried out by the technical staff from NBS headquarters and staff from FMA&RD and NFRA headquarters.
Data were collected by teams consisting of a supervisor, between 2 and 4 interviewers and a data entry operator. The number of teams varied from state to state depending on the sample size or number of EAs selected. The teams moved in a roving manner and data collection lasted for between 20 - 30 days for each of the post-planting and post-harvest visits.
This survey used a concurrent data entry approach. In this method, the fieldwork and data entry were handled by each team assigned to the state. Each team consisted of a field supervisor, 2-4 interviewers and a data entry operator. Immediately after the data were collected in the field by the interviewers, the questionnaires were handed over to the supervisor to be checked and documented.
At the end of each day of fieldwork, the questionnaires were then passed to the data entry operator 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. The 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.
The benefits of this method are that it allows one to:
i Capture errors that might have been overlooked by a visual inspection only,
ii 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.
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 designed to highlight many of the errors that occurred during the fieldwork. Errors that are caught at the fieldwork stage are corrected based on re-visits to the household 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.
During 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. Any problems found were 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.
After 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.
The final stage of the cleaning process was to ensure that the household- and individual-level datasets 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 plots 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.
All completed and edited questionnaires by each team will remain in the custody of the field supervisor even after the data has been captured by data entry operators.
The supervisor will submit the completed and edited questionnaires to the NBS state officer. He /she will coordinate the forwarding of all completed questionnaires, softcopy records, laptops and printers to the NBS Headquarters in Abuja.
No sampling error
Variable Naming Scheme
Generally, the variables are named to correspond with each of the questions. For example in the case of the cover dataset (Section A) the variables names start with 'SA' which means section A of the household
questionnaire. This is followed by 'Q' and a number e.g. 'Q1' 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 'S11A - 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.
Name | Affiliation | URL | |
---|---|---|---|
National Bureau of Statistics (NBS) | Federal Government of Nigeria (FGN) | http://www.nigerianstat.gov.ng | feedback@nigerianstat.gov.ng feedback@nigerianstat.gov.ng |
Is signing of a confidentiality declaration required? | Confidentiality declaration text |
---|---|
yes | The confidentiality of the individual respondent is protected by law (Statistical Act 2007). This 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. |
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.
National Bureau of Statistics,General Hosehold Survey-Panel Wave 3 (Post Harvest) 2016 v1.0 of the public use (December, 2016) provided by National Data Archive, http:/www.nigerianstat.gov.ng''
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.
(c) NBS 2016
Name | Affiliation | URL | |
---|---|---|---|
Dr. Yemi Kale (Statistician General) | National Bureau of Statistics | yemikale@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
Mr Biyi Fafunmi (Head of ICT) | National Bureau of Statistics | biyifafunmi@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
Mr Isiaka Olarenwaju (Deputy-Director:Household Statistics) | National Bureau of Statistics | ialarenwaju@yahoo.com | http://www.nigerianstat.gov.ng |
Mr Uwayemi Etchie (Head:System programming) | National Bureau of Statistics | http://www.nigerianstat.gov.ng | |
Funmilayo Ajao (Data Archivist) | National Bureau of Statistics | funmicoco@yahoo.com | http://www.nigerianstat.gov.ng |
Mr Akufo (Consultant) | World Bank | http://www.nigerianstat.gov.ng | |
Mr Kelvin | World Bank | http://www.nigerianstat.gov.ng | |
National Bureau of Statistics | National Bureau of Statistics | http://www.nigerianstat.gov.ng |
DDI-NGA-NBS-NGHPH-2016-v1.0
Name | Abbreviation | Affiliation | Role |
---|---|---|---|
National Bureau of Statistics | NBS | Federal Government of Nigeria(FGN) | Meta Producer |
2016-02-14
Version 1.0 (December, 2016)