General Household Survey-Panel (Post-Planting 2010)
First round
Nigeria, 2010
Get MicrodataIdentification
NGA-NBS-GHS-PANEL-2010-v1.0
General Household Survey-Panel (Post-Planting 2010)
First round
GHS-PANEL 2010
No translation
| Name | Country code |
|---|---|
| Nigeria | NGA |
Living Standards Measurement Study [hh/lsms]
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.
Pilot Test was done in Six selected States namely, Kaduna, Nasarawa Taraba, Osun, Edo and Enugu.
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.
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. The 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.
This is the first time the survey is coming up.
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.
The 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.
Expected Benefits:
The specific outputs and outcomes of the revised GHS with panel component are:
Development of an innovative model for collecting agricultural data in conjunction with household data;
Development 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
GHS;
Building the capacity to generate a sustainable system for the production of accurate and timely information on agricultural households in Nigeria.
Comprehensive analysis of poverty indictors and socio-economic characteristics.
Innovations
The revised GHS with panel component contains several innovative features.
Integration of agricultural data at the plot level with household welfare data;
Creation 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,
education and other labor activities, inter alia.
Use of small area estimation techniques (SAE) to generate state level poverty data by taking advantage of the integration of the panel households into the
full GHS.
Collection of information on the network of buyers and sellers of goods that household interact with;
Use of GPS units for measuring agricultural land areas;
Involvement of multiple actors in government, academia and the donor community in the development of the survey and its contents as well as its
implementation and analysis;
Use of concurrent data entry in Wave 1. In later Waves the project will develop and implement a Computer Assisted Personal Interview (CAPI) application
for the paperless collection of the GHS;
Use of direct respondents for all sections of the questionnaires where individual level data or specific economic activity data are collected;
Creation of a publicly available micro data sets for researchers and policy makers;
Active dissemination of agriculture statistics.
Sample survey data [ssd]
Household, individual, Farm, Plot and Crop
Version
version 1.0
2011-06-28
v1.0 was original release in June 2011
Scope
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.
The household questionnaire was to be administered to all households in the sample.
The survey covered a wide range of socio-economic topics which are highlighted. Household Questionnaire was used to collect information on
-
Household identification
-
Household member roster, demographics and migration
-
Education Status
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Labour and Time use
-
Credit and Savings
-
Household Assets
-
Non-Farm Enterprises
-
Consumption of food (recall)
-
Non-food consumption expenditure
-
Food security
-
Other non-labour income sources
Agricultural Questionnaire collected information on:
Basic crop, livestock, poultry, fishery, and forestry production, storage and sales
Productivity of main crops, with emphasis on improved measures of:
Quantification of production
Plot size
Production stocks (pest, etc)
Land Holdings
Size and tenure/ titling
Transaction
Access to and use of services, infrastructure and natural resources
Agricultural Extension Services
Infrastructure (including roads)
Credit ( both for agriculture and other purposes)
Market access
Access to information
Access to natural and common property resources
Input use and technology adoption
Family and hired labour
Use of technology and farming implements
Seed varieties
Fertilizer, pesticides etc.- GPS measure of plot size, etc
| Topic | Vocabulary | URI |
|---|---|---|
| economic conditions and indicators [1.2] | CESSDA | http://www.nesstar.org/rdf/common |
| income, property and investment/saving [1.5] | CESSDA | http://www.nesstar.org/rdf/common |
| employment [3.1] | CESSDA | http://www.nesstar.org/rdf/common |
| unemployment [3.5] | CESSDA | http://www.nesstar.org/rdf/common |
| working conditions [3.6] | CESSDA | http://www.nesstar.org/rdf/common |
| compulsory and pre-school education [6.2] | CESSDA | http://www.nesstar.org/rdf/common |
| vocational education [6.7] | CESSDA | http://www.nesstar.org/rdf/common |
| housing [10.1] | CESSDA | http://www.nesstar.org/rdf/common |
| children [12.1] | CESSDA | http://www.nesstar.org/rdf/common |
| gender and gender roles [12.6] | CESSDA | http://www.nesstar.org/rdf/common |
| religion and values [13.5] | CESSDA | http://www.nesstar.org/rdf/common |
| health policy [8.6] | CESSDA | http://www.nesstar.org/rdf/common |
| plant and animal distribution [9.4] | CESSDA | http://www.nesstar.org/rdf/common |
| TRANSPORT, TRAVEL AND MOBILITY [11] | CESSDA | http://www.nesstar.org/rdf/common |
| time use [13.9] | CESSDA | http://www.nesstar.org/rdf/common |
Coverage
National Zone State Local Government Sector (Urban/Rural)
Zone
Household members
Producers and sponsors
| Name | Affiliation |
|---|---|
| National Bureau of Statistics (NBS) | Federal Government of Nigeria (FGN) |
| Name | Role |
|---|---|
| World Bank | funding and Technical advisory |
| Name | Abbreviation | Role |
|---|---|---|
| Federal Government of Nigeria | FGN | Funding |
| Bill and Melinda Gates Foundation | BMGF | Funding |
| World Bank | WB | Funding |
| Name | Affiliation | Role |
|---|---|---|
| Federal Ministry of Agriculture and Rural Development | FMA&RD | Technical advisory |
| Federal Ministry of Water Resources | FMWR | Technical advisory |
| National Food Reserve Agency | NFRA | Technical advisory |
Sampling
National Integrated Survey of Households (NISH)-2007/2012 Master Sample Frame (MSF) was adopted.
In 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.
Thereafter, 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
The 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).
The Panel Survey used a two stage stratified sample selection process.
First Stage:
The 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.
Second Stage:
The 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.
Determination 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.
No deviation from the sampling
The response rate 99.9% includeing replacements at household level.
Replacement households represent 17.9% of the sample.
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.
For any analysis, the SECTA data set will need to be merged with the file that is to be used.
Survey 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.
These are the Household Questionnaire and the Agricultural Questionnaire.
The household questionnaire consist of:
SECTION 1: HOUSEHOLD MEMBER ROSTER
SECTION 2: EDUCATION
SECTION 3: LABOUR
SECTION 4: CREDIT AND SAVINGS
SECTION 5: HOUSEHOLD ASSETS
SECTION 6: NONFARM ENTERPRISES AND INCOME GENERATING ACTIVITIES
SECTION 7A: MEALS AWAY FROM HOME EXPENDITURES
SECTION 7B: FOOD EXPENDITURES
SECTION 8: NON-FOOD EXPENDITURES
SECTION 9: FOOD SECURITY
SECTION 10: OTHER INCOME
Sections 7A, 7B and 8 are not included in the present data.
These data sets will be given when the Post Hrvest data set is avaliable.
The Agricultural Questionnaire:
SECTIONS 11:
a PLOT ROSTER
b LAND INVENTORY
c INPUT COSTS
d FERTILIZER ACQUISITION
e SEED ACQUISITION
f PLANTED FIELD CROPS
g PLANTED TREE CROPS
h MARKETING OF AGRICULTURAL SURPLUS
i ANIMAL HOLDINGS
j ANIMAL COSTS
k AGRICULTURE BY-PRODUCT
l EXTENSION
SECTIONS 12: NETWORK ROSTER
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.
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.
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 were:
- To capture errors that might have been overlooked by a visual inspection only
- To 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 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.
A 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.
Data collection
| Start | End | Cycle |
|---|---|---|
| 2010-08-31 | 2010-10-15 | six weeks |
September 2003 to August 2004
| Start date | End date | Cycle |
|---|---|---|
| 2010-08 | 2011-03 | 2 yrs |
- Face-to-face [f2f]
| Name | Affiliation | Abbreviation |
|---|---|---|
| National Bureau of Statistics | Federal Government of Nigeria | NBS |
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.
The 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.
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
Data processing
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.
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. 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.
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 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.
Data appraisal
VARIABLE NAMING SCHEME
Generally, 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 ‘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.
There 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
? Provision of field vehicles with charging facilities for the data entry equipment was an added advantage
? Challenges on how to send data via internet to NBS headquarters
? Problems in effective managing of data problems while the teams were in the field like; printing and correct reading of error messages
? Problems of EA and HH replacement:
o Suggested a re-listing exercise
o Improve method of replacement
? Problems with geographical codes
o Using different codes in the states and headquarters for LGAs, EAs and replicate identification codes (RIC)
o Suggested harmonization of codes
Distributor information
| Organization name | Abbreviation | Affiliation |
|---|---|---|
| NATIONAL BUREAU OF STATISTICS | NBS | FEDERAL GOVT. OF NIGERIA |
Data Access
| Name | Affiliation | URL | |
|---|---|---|---|
| National Bureau of Statistics (NBS) | Federal Government of Nigeria (FGN) | http://www.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 Household Survey-Panel (Post-Planting 2010) v1.0
Disclaimer and copyrights
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.
© NBS 2011
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Alhaji R. A. Sanusi | AC SG National Bureau of Statistics | rasanusi@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
| Mr E.O. Ekezie | HOD ICT | eoekezie@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
| Mr C.O. Monike | Fedral Government of Nigeria (FGN) | comonike@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
| Biyi Fafunmi | Data Access | biyifafunmi@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
| Mrs A. A. Akinsanya | Data Archivist | paakinsanya@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
| Mr R.F. Busari | ICT | rfbusari@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
| National Bureau of Statistics (NBS) | Fedral Government of Nigeria (FGN) | feedback@nigerianstat.gov.ng | http://www.nigerianstat.gov.ng |
Metadata production
DDI-NGA-NBS-GHS-PANEL-2010-v1.0
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| National Bureau of Statistics | NBS | Federal Government of Nigeria (FGN) | Metadata Producer |
2011-06-28
Metadata version
Version 1.0