Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. 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. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then 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 approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2018/19 is the fourth round of the survey with prior rounds conducted in 2010/11, 2012/13, and 2015/16. GHS-Panel households were visited twice: first after the planting season (post-planting) between July and September 2018 and second after the harvest season (post-harvest) between January and February 2019.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Agricultural plots
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS-Panel sample of 5,000 households across 500 enumeration areas (EAs) and was designed to be representative at the national level as well as at the zonal level. The complete sampling information for the GHS-Panel is described in the Basic Information Document for GHS-Panel 2010/2011. However, after a nearly a decade of visiting the same households, a partial refresh of the GHS-Panel sample was implemented in Wave 4.

For the partial refresh of the sample, a new set of 360 EAs were randomly selected which consisted of 60 EAs per zone. The refresh EAs were selected from the same sampling frame as the original GHS-Panel sample in 2010 (the “master frame”). A listing of all households was conducted in the 360 EAs and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximated 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS-Panel households from 2010 were selected to be included in the new sample. This “long panel” sample was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across the 6 geopolitical Zones. The systematic selection ensured that the distribution of EAs across the 6 Zones (and urban and rural areas within) is proportional to the original GHS-Panel sample. Interviewers attempted to interview all households that originally resided in the 159 EAs and were successfully interviewed in the previous visit in 2016. This includes households that had moved away from their original location in 2010. In all, interviewers attempted to interview 1,507 households from the original panel sample.

The combined sample of refresh and long panel EAs consisted of 519 EAs. The total number of households that were successfully interviewed in both visits was 4,976.

Sampling deviation

While the combined sample generally maintains both national and Zonal representativeness of the original GHS-Panel sample, the security situation in the North East of Nigeria prevented full coverage of the Zone. Due to security concerns, rural areas of Borno state were fully excluded from the refresh sample and some inaccessible urban areas were also excluded. Security concerns also prevented interviewers from visiting some communities in other parts of the country where conflict events were occurring. Refresh EAs that could not be accessed were replaced with another randomly selected EA in the Zone so as not to compromise the sample size. As a result, the combined sample is representative of areas of Nigeria that were accessible during 2018/19. The sample will not reflect conditions in areas that were undergoing conflict during that period. This compromise was necessary to ensure the safety of interviewers.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 4 consists of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provides information on demographics; education; health (including anthropometric measurement for children); labor; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income. Household location is geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets.

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicits information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; and household fishing activities. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicits information on access to infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

The Household Questionnaire is slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collects different information during each visit, but for the same plots and crops.

Cleaning operations

CAPI: For the first time in GHS-Panel, the Wave four exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires, household, agriculture and community questionnaires were implemented in both the post-planting and post-harvest visits of Wave 4 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Survey Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given tablets which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 4 was highly automated. Each field team was given a mobile modem allow for internet connectivity and daily synchronization of their tablet. This ensured that head office in Abuja has access to the data in real-time. Once the interview is completed and uploaded to the server, the data is first reviewed by the Data Editors. The data is also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file is generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files are communicated back to respective field interviewers for action by the interviewers. This action is done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. 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. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then 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 approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

The Basic Information Document (BID) provides a brief overview of the Nigerian General Household Survey (GHS) but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the GHS. As the BID does not describe in detail the background, development, or use of the GHS itself, the wave-specific GHS BIDs should supplement the information provided here.

The Nigeria Universal Panel Data (NUPD) consists of both survey instruments and datasets from the two survey visits of the GHS - Post-Planting (PP) and Post-Harvest (PH) - meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the GHS. The NUPD provides a consistent and straightforward means of conducting user-driven analyses using convenient, standardized tools.

The design of the NUPD combines the four completed Waves of the GHS Household Post-Planting and Post-Harvest Surveys – Wave 1 (2010/11), Wave 2 (2012/13), Wave 3 (2015/16), and Wave 4 (2018/19) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

Please see the GHS BIDs for each round for detailed descriptions of the sample design used in each round and their respective implementation efforts as this is a compilation of datasets from all previous waves.

Mode of data collection

Face-to-face [f2f]

Research instrument

The larger GHS-Panel project consists of three questionnaires (Household Questionnaire, Agriculture Questionnaire, Community Questionnaire) for each of the two visits (Post-Planting and Post-Harvest). The GHS-NUPD only consists of the Household Questionnaire.

GHS-Panel Household Questionnaire: The Household Questionnaire provides information on demographics; education; health (including anthropometric measurement for children); labor; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income.

The Household Questionnaire is slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

Cleaning operations

Please see the GHS BIDs for each round for detailed descriptions of data editing and additional data processing efforts as this is a compilation of datasets from all previous waves.

Abstract

The GHS is a cross-sectional survey of 22,000 households throughout the country. The panel component (GHS-Panel) is now being applied to 5,000 households of the GHS and covers multiple agricultural activities. The focus of this panel component is to improve data from the agriculture sector and link this to other facets of household behaviour and characteristics. The GHS-Panel drew heavily on the HNLSS and the NASS to create a new survey instrument and method to shed light on the role of agriculture in households' economic wellbeing. The NBS implemented the first stage (Post Planting) of the first wave of the GHS-Panel in 2010. This panel is a subset of the full GHS (or GHS-Cross Section) that will be finished in 2011.) It is envisaged that the GHS-Panel will be carried out every two years while the GHS-Cross Section will be carried out annually.

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.

Geographic coverage

National, the survey covered all the 36 states and Federal Capital Territory (FCT).

Analysis unit

Households, Individuals, Agricutural plots

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The GHS-Panel (Post Planting 2010), like all household surveys in the country, is based on the Master Sample Frame, This Frame is based on the 2006 Housing and Population Census conducted by the National Population Commission (NpopC). The census includes approximately 662,000 enumeration areas (EAs) throughout the country. From the census, the Master Frame was constructed at the local government area (LGA). In 668 LGAs, 30 EAs were scientifically selected. The remaining six LGAs are found in FCT, Abuja. Forty EAs were scientifically selected in each of these 6 LGAs. This gives a total of 23,280 EAs selected nationally. This is the Master Frame.

From the Master Frame a master sample frame, called the National Integrated Survey of Households 2007/2012 Master Sample Frame (NISH-MSF) was developed. The NISHMSF was constructed by pooling the LGAs in the Master Frame by state. Thereafter, a systematic sample of 200 EAs was 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-MSF, selected as a combination of replicates from NISH-MSF frame. For the GHS-Panel, the sample is a subset of the EAs selected for the GHS.

Sample Framework 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.

Sample Selection The GHS 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, in which 10 HHs per EA are usually selected and give robust estimates.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

This survey used 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 and supervisors (the supervisors administered the community questionnaires and collected data on prices), 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: - Capture errors that might have been overlooked by a visual inspection only, - 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 specialized data entry program that was used for the data entry of the questionnaires.

Abstract

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 NBS covers a wide range of sectors. Except for the Harmonized National Living Standard Survey (HNLSS) which covers multiple topics, these different sectors are usually covered in separate surveys none of which is conducted as a panel. As part of the efforts to continue to improve data collection and usability, the NBS has revised the content of the annual General household survey (GHS) and added a panel component. The GHS-Panel is conducted every 2 years covering multiple sectors with a focus to improve data from the agriculture sector.

The Nigeria General Hosehold Survey-Panel, is the result of a partnership that NBS has established with the Federal Ministry of Agriculture and Rural Development (FMARD), 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. This GHS-Panel Survey responds to the needs of the country, given the dependence of a high percentage of households on agriculture activities in the country, for information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time, makes the GHS-Panel a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses to be made of how households add to their human and physical capital, how education affects earnings and the role of government policies and programs on poverty, inter alia.

The objectives of the survey are as follows 1. Allowing welfare levels to be produced at the state level using small area estimation techniques resulting in state-level poverty figures 2. With the integration of the longitudinal panel survey with GHS, it will be possible to conduct a more comprehensive analysis of poverty indicators and socio-economic characteristics 3. Support the development and implementation of a Computer Assisted Personal Interview (CAPI) application for the paperless collection of GHS 4. Developing an innovative model for collecting agricultural data 5. Capacity building and developing sustainable systems for the production of accurate and timely information on agricultural households in Nigeria. 6. Active dissemination of agriculture statistics

The second wave consists of two visits to the household: the post-planting visit occurred directly after the planting season to collect information on preparation of plots, inputs used, labour used for planting and other issues related to the planting season. The post-harvest visit occurred after the harvest season and collected information on crops harvested, labour used for cultivating and harvest activities, and other issues related to the harvest cycle.

Geographic coverage

National Coverage

Analysis unit

Households

Universe

Agricultural farming household members.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is designed to be representative at the national level as well as at the zonal (urban and rural) levels. The sample size of the GHS-Panel (unlike the full GHS) is not adequate for state-level estimates.

The sample is a two-stage probability sample:

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. A total of 500 EAs were selected using this method.

Second Stage: The second stage was the selection of households. Households were selected randomly using 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 was to generate a random start 'r' from the table of random numbers which stands as the 1st selection. Consecutive selection of households was obtained by adding the sampling interval to the random start.

Determination of the sample size at the household level was based on the experience gained from previous rounds of the GHS, in which 10 households per EA are usually selected and give robust estimates.

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 had different samples sizes depending on the total number of EAs in each state.

Households were not selected using replacement. Thus the final number of household interviewed was slightly less than the 5,000 eligible for interviewing. The final number of households interviewed was 4,986 for a non-response rate of 0.3 percent. A total of 27,533 household members were interviewed. In the second, or Post-Harvest Visit, some household had moved as had individuals, thus the final number of households with data in both points of time (post planting and post harvest) is 4,851, with 27,993 household members.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Data Entry 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: - Capture errors that might have been overlooked by a visual inspection only, - 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 specialized data entry program that was used for the data entry of the questionnaires.

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. 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 data sets 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.

Response rate

The response rate was very high. Response rate after field work was calculated to be 93.9% while attrition rate was 6.1% for households. During the tracking period, 52.4% of the attrition was tracked while at the end of the whole exercise, the response rate was: Post Harvest: 97.1%

Sampling error estimates

No sampling error

Abstract

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.

GHS-Panel households were 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 were visited twice regardless of whether they participated in agricultural activities. Some important factors such as labour, food consumption, and expenditures were collected during both visits.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage stratified sample design was used for the GHS and the Panel Survey. 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, a total of 2220 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.

For further details of the sample design, see Section 1.2 of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The GHS-Panel Wave 3 consists of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provides information on demographics; education; health (including anthropometric measurement for children and child immunization); labour and labour data collection options; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income. Household location is geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets. The labour module of the Household Questionnaire introduced four different variants to test the sensitivity of labour statistics to how labour modules are designed.

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicits information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; and household fishing activities.

GHS-Panel Community Questionnaire: The Community Questionnaire solicits information on access to infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

Data Entry The household and agricultural components of the survey were conducted using 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 and supervisors (the supervisors administered the community questionnaires and collected data on prices), 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: - Capture errors that might have been overlooked by a visual inspection only, - 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 specialized data entry program that was used for the data entry of the questionnaires.

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.

The General Household Survey (GHS), is a cross-sectional survey of 22,000 households is carried out annually throughout the country. Under the work of the partnership, a full revision of the questionnaire was undertaken and, at the same time, a sub-sample of the GHS now forms a panel survey. The panel component (GHS-Panel) applies to 5,000 households of the GHS collecting additional data on multiple agricultural activities and household consumption. As the focus of this panel component is to improve data from the agriculture sector and link this to other facets of household behavior and characteristics the GHS-Panel drew heavily on the Harmonized National Living Standards Survey (HNLSS - a multi-topic household survey) and the National Agricultural Sample Survey (NASS - the key agricultural survey) to create a new survey instrument to shed light on the role of agriculture in households' economic wellbeing that can be monitored over time.

The first wave of the revised GHS and GHS-Panel was carried out in two visits to the panel households (post-planting visit in August-October 2010 and post-harvest visit in February-April 2011) and one visit to the full cross-section (in parallel with the post-harvest visit to the panel). The GHS panel will be carried out every two years while the GHS-cross section is usually carried out annually.

Geographic coverage

National Coverage Local Government Sector (Urban/Rural)

Analysis unit

Households

Universe

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is designed to be representative at the national level as well as at the zonal (urban and rural) levels. The sample size of the panel, General Household Survey (GHS), is not adequate for state-level estimates, unlike the full GHS.

The sample was derived using a 2-stage sampling method. In the first stage, Enumeration Areas (EAs) were selected as Primary Sampling Units (PSUs). Selection was 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. A total of 500 EAs were selected using this method. In the second stage, households were selected randomly using 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 was to generate a random start 'r' from the table of random numbers which stands as the 1st selection. Consecutive selection of households was obtained by adding the sampling interval to the random start.

Determination of the sample size at the household level was based on the experience gained from previous rounds of the GHS, in which 10 households per EA are usually selected and give robust estimates.

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 had different samples sizes. The distribution of the samples are shown in Table 3.1 below which shows the size of the sample in each state, by geopolitical zone and urban/rural break-out.

Households were not selected using replacement. Thus the final number of household interviewed was slightly less than the 5,000 eligible for interviewing. The final number of households interviewed was 4,986 for a non-response rate of 0.3 percent. A total of 27,533 household members were interviewed. In the second, or Post Harvest Visit, some household had moved as had individuals, thus the final number of households with data in both points of time (post planting and post harvest) is 4,851, with 27,993 household members.

Mode of data collection

Face-to-face paper [f2f]

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 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 data sets 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.

Response rate

The response rate was 99.9% including replacement at household level. Replacement households represent 17.9% of the sample.

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. GHS-Panel households were 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 were visited twice regardless of whether they participated in agricultural activities. Some important factors such as labour, food consumption, and expenditures were collected during both visits.

Abstract

The General Household Survey Panel, Farm Area Measurement Validation Study 2013 was conducted on a subsample of the GHS-Panel survey, it focused on the land area measurement component. The survey was motivated by observed differences between farmer estimates of plot area and GPS measurement in Nigeria and other countries with LSMS-ISA surveys. The study set out to validate GPS measurement and farmer self-reported estimates against the compass and rope measurement, commonly accepted as the gold standard method. The LSMS-ISA, an agriculture-focused project of the LSMS program, and the institutional collaborations on which it is built, provides an ideal platform to support methodological research. The broader LSMS-ISA research agenda is composed of seven primary components:

1. Land area measurement
2. Soil fertility
3. Water resources
4. Labour inputs
5. Skill measurement
6. Production of continuous and extended-harvest crops
7. Computer-assisted personal interviewing for agricultural data.

Four states were purposefully selected based on safety and past performance in area measurement (Benue, Osun, Oyo, and Kogi). The total number of plots measured and included in the validation study were 495, coming from a total of 202 households. The GHSP-FAMVS was carried out in 2013 by the Nigeria National Bureau of Statistics (NBS) in collaboration with The World Bank Living Standards Measurement Study (LSMS) team. Fieldwork began in March 2013 and lasted for approximately 3 weeks.

Geographic coverage

Regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The plot size plays a significant role in the accuracy of plot area measurement using the various methods, the validation sample was stratified on four plot size strata to ensure we could test the various methods on larger plots, which are much rarer. Four states were purposefully selected based on safety and past performance in area measurement (Benue, Osun, Oyo, and Kogi). Using the second wave of the GHS panel as the sample frame and the GPS measurement of the plot taken in the post-planting visit, every plot was assigned to some plot-size strata: · strata 1: <=1000 sq. meters · strata 2: 1000-2500 sq. meters · strata 3: 2500-5000sq. Meters · strata 4: >5000 sq. meters).

One hundred plots were then randomly selected from each stratum. This process yielded the selection of 400 plots (211 households). However, in order to maximize the sample at minimal added cost, we included all plots from the selected households, not only the plots that were selected in the first step (totalling 518 plots). From the 518 selected plots, 23 plots were unable to be measured (5 due to land disputes, 4 due to respondent refusal, 14 for other reasons). Therefore, the total number of plots measured and included in the farm area measurement validation study is 495, coming from a total of 202 households. Stratification by plot size in the validation sample results in the unequal probability of plot selection within households from the GHS-Wave 2 sample. Household-level sampling weights were calculated for the validation sample to make them representative of the same household population sampled in Wave 2. Refer to Annex I of the Basic Information Document for details on the construction of the sampling weights.

Mode of data collection

Face-to-face [f2f]

Geographic coverage

National coverage

Analysis unit

households/individuals

Kind of data

survey

Frequency of data collection

Yearly

Sampling procedure

Sample size:

The General Household Survey Panel, Farm Area Measurement Validation Study (GHSP- FAMVS), 2013 was conducted on a subsample of the GHS-Panel survey, it focused on the land area measurement component. The survey was motivated by observed differences between farmer estimates of plot area and GPS measurement in Nigeria and other countries with LSMS-ISA surveys. The GHSP-FAMVS, set out to validate GPS measurement and farmer self-reported estimates against the compass and rope measurement, commonly accepted as the gold standard method. The LSMS-ISA, an agriculture-focused project of the LSMS program, and the institutional collaborations on which it is built, provides an ideal platform to support methodological research. The broader LSMS-ISA research agenda is composed of seven primary components: 1. Land area measurement 2. Soil fertility 3. Water resources 4. Labor inputs 5. Skill measurement 6. Production of continuous and extended-harvest crops 7. Computer-assisted personal interviewing for agricultural data Four states were purposefully selected based on safety and past performance in area measurement (Benue, Osun, Oyo, and Kogi). The total number of plots measured and included in the validation study were 495, coming from a total of 202 households. The GHSP-FAMVS was carried out in 2013 by the Nigeria National Bureau of Statistics (NBS) in collaboration with The World Bank Living Standards Measurement Study (LSMS) team. Fieldwork began in March 2013 and lasted for approximately 3 weeks.

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.

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.

Geographic coverage

National Zone State Local Government Sector (Urban/Rural)

Analysis unit

Household, individual, Farm, Plot and Crop

Universe

Household members

Kind of data

Sample survey data [ssd]

Sampling procedure

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.

Sampling deviation

No deviation from the sampling

Mode of data collection

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.
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

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.

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.

Response rate

The response rate 99.9% includeing replacements at household level. Replacement households represent 17.9% of the sample.

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.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales.
2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Nigeria General Household Survey, Panel (GHS-Panel) 2018-2019 and Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Nigeria - General Household Survey, Panel 2018-2019, Wave 4” and “Nigeria - COVID-19 National Longitudinal Phone Survey 2020” available in the Microdata Library for details.

Abstract

Nigeria was among the first few countries in Sub-Saharan Africa to identify cases of COVID-19. Reported cases and fatalities have been increasing since it was first identified. The government implemented strict measures to contain the spread of this virus (such as travel restrictions, school closures and home-based work). While the Government is implementing these containment measures, it is important to understand how households in the country are affected and responding to the evolving crises, so that policy responses can be designed well and targeted effectively to reduce the negative impacts on household welfare.

The objective of Nigeria COVID-19 NLPS is to monitor the socio-economic effects of this evolving COVID-19 pandemic in real time. These data will contribute to filling critical gaps in information that could be used by the Nigerian government and stakeholders to help design policies to mitigate the negative impacts on its population. The Nigeria COVID-19 NLPS is designed to accommodate the evolving nature of the crises, including revision of the questionnaire on a monthly basis.

The households were drawn from the sample of households interviewed in 2018/2019 for Wave 4 of the General Household Survey—Panel (GHS-Panel). The extensive information collected in the GHS-Panel just over a year prior to the pandemic provides a rich set of background information on the Nigeria COVID-19 NLPS households which can be leveraged to assess the differential impacts of the pandemic in the country.

Each month, the households will be asked a set of core questions on the key channels through which individuals and households are expected to be affected by the COVID-19-related restrictions. Food security, employment, access to basic services, coping strategies, and non-labour sources of income are channels likely to be impacted. The core questionnaire is complemented by questions on selected topics that rotate each month. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

Wave 4 of the GHS-Panel conducted in 2018/19 served as the frame for the Nigeria COVID-19 NLPS survey. The GHS-Panel sample includes 4,976 households that were interviewed in the post-harvest visit of the fourth wave in January/February 2019. This sample of households is representative nationally as well as across the 6 geopolitical Zones that divide up the country. In every visit of the GHS-Panel, phone numbers are collected from interviewed households for up to 4 household members and 2 reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NBS and the GHS-Panel households made this an ideal frame from which to conduct the COVID-19 monitoring survey in Nigeria.

Among the 4,976 households interviewed in the post-harvest visit of the GHS-Panel in 2019, 4,934 (99.2%) provided at least one phone number. Around 90 percent of these households provided a phone number for at least one household member while the remaining 10 percent only provided a phone number for a reference person. Households with only the phone number of a reference person were expected to be more difficult to reach but were nonetheless included in the frame and deemed eligible for selection for the Nigeria COVID-19 NLPS.

To obtain a nationally representative sample for the Nigeria COVID-19 NLPS, a sample size of approximately 1,800 successfully interviewed households was targeted. However, to reach that target, a larger pool of households needed to be selected from the frame due to non-contact and non-response common for telephone surveys. Drawing from prior telephone surveys in Nigeria, a final contact plus response rate of 60% was assumed, implying that the required sample households to contact in order to reach the target is 3,000.

3,000 households were selected from the frame of 4,934 households with contact details. Given the large amount of auxiliary information available in the GHS-Panel for these households, a balanced sampling approach (using the cube method) was adopted. The balanced sampling approach enables selection of a random sample that still retains the properties of the frame across selected covariates. Balancing on these variables results in a reduction of the variance of the resulting estimates, assuming that the chosen covariates are correlated with the target variable. Calibration to the balancing variables after the data collection further reduces this variance (Tille, 2006). The sample was balanced across several important dimensions: state, sector (urban/rural), household size, per capita consumption expenditure, household head sex and education, and household ownership of a mobile phone.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; knowledge regarding the spread of COVID-19; behaviour and social distancing; access to basic services; employment; income loss; food security; concerns; coping/shocks; and social safety nets.

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; employment (including non-farm enterprise and agricultural activity); other income; food security; and social safety nets.

ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; housing; employment (including non-farm enterprise and agricultural activity); other income; coping/shocks; and social safety nets.

ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; credit; employment (including non-farm enterprise, crop farming and livestock); food security; income changes; concerns; and social safety nets.

ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise and agricultural activity); and other income.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise); COVID testing and vaccination; and other income.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise); food security; concerns; and safety nets.

ROUND 8: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; employment (including non-farm enterprise and agriculture); and coping/shocks.

ROUND 9: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; early childhood development, access to basic services, employment (including non-farm enterprise and agriculture); and income changes.

ROUND 10: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise and agricultural activity); concerns and COVID testing and vaccination.

ROUND 11: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; credit; access to basic services; education; employment (including non-farm enterprise); safety nets; youth contact details; and phone signal.

ROUND 12: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on youth aspirations and employment; and COVID vaccination.

Cleaning operations

COMUPTER ASSISTED TELEPHONE INTERVIEW (CATI): The Nigeria COVID-19 NLPS exercise was conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Data Analytics and Tools Unit within the Development Economics Data Group (DECDG) at the World Bank. Each interviewer was given two tablets, which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CATI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication

Abstract

In this report, we present data from the emergency response survey conducted via telephone among households in three conflict affected regions of Nigeria, North East, North Central and South South between August-September 2017. This round is the second round of telephone data collected from a subsample of households in the Nigeria General Household Survey (GHS). The first round collected data on conflict exposure.

The purpose of this second round of data collection was to understand food insecurity in conflict affected regions. Armed conflict can have a detrimental effect on food security. This might be due to for example reduced agricultural production, or price increases due to malfunctioning markets. Food insecurity might be permanent, such that a household living below the poverty line has a constant struggle to acquire food from the market or produce food for their own use. In situations such as armed conflict, also better endowed households might be temporarily food insecure.

In this report, we find that food insecurity is a major concern in all the three regions studied:

· The mean household in all the three regions is “highly food insecure” · North East of Nigeria is the most food insecure of the three regions · Reducing meals or portion size is the most important coping strategy in all three regions · Food prices are the most important source of food insecurity in all three regions · A large majority of households rely on the market as the main source of food in all regions. Price concerns should therefore be taken very seriously by policy makers. · Households in all three regions do not report there being an inadequate supply of food in the market.

Geographic coverage

National Coverage Households

Analysis unit

Households

Universe

The Survey covered all household members. The questionnaire was administered to only one respondent per household - most often a male household head.

Kind of data

Sample survey data [ssd]

Sampling procedure

The food security survey was a telephone based survey conducted between August 15th and September 8th 2017. The interview was the second round of a telephone survey using a sub-set of the sample of GHS (General Household Survey) households. The first round of the telephone interview was administered during spring 2017 with 717 completed interviews with the following geographical distribution: 175 interviews in the North East, 276 in North Central and 266 in South South. The first round was focused on conflict exposure, while the second round discussed in this report focused on food insecurity in conflict affected regions.

In the three conflict affected geographical zones comprising of 16 states of Nigeria, households from LGS's that had high conflict exposure were oversampled chosen for a pilot sample, conducted before the telephone surveys. These LGS's were chosen based on the following criteria: The oversampled LGS's needed to have over 10 conflict events during 2012-14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database.

The first round of the telephone survey (which took place after the pilot) first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 percent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews.

Conflict affected areas were oversampled in order to have a large enough sample of households that in fact experienced conflict events in order to shed light on the type of events that have happened. A random sample of the zones might have given too small sample of conflict affected households and therefore restricted the analysis of the various types of conflict events. Due to the oversampling however, the sample drawn was not representative at the level of the geographical zone, as is the case in the GHS. Therefore in the analysis we use sampling weights that adjust for the propensity of being in a conflict affected LGA in order to ensure that the sample is representative at the level of the geographical zone.

During the second round of the survey 582 of the 717 households were re-interviewed on food security related issues (only the 717 were attempted to be reached). Of the 582 households, 147 in the North East, 219 in North Central, and 216 in South South were interviewed. The attrition rates in our sample from round one to round two are hence 16 percent, 21 percent, and 19 percent for North East, North Central and South South, respectively. The attrition from the conflict survey round was mostly due to not being able to reach the respondents possibly due to non-functioning phone numbers. Only 3 percent of respondents refused to answer.

Similar telephone-based surveys are being conducted in six countries in Sub-Saharan Africa under the World Bank project "Listening to Africa". As a comparison, a mobile phone survey in Tanzania (see Croke et al. 2012 for details), had a high drop-out rate between the very first rounds from 550 to 458 respondents, but very low attrition for the subsequent rounds for the 458 respondents, who could reliably be reached by a mobile phone. In light of this reference point and also considering the fact that the households interviewed live in conflict affected regions, our attrition rates seem to be within reasonable limits.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Response rate

The first round of the telephone survey (which took place after the pilot), first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 per cent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews. The response rate is 96%

Data appraisal

Limitations Recall Bias In the pilot data collection, respondents were asked to report on conflict events that had taken place in their family and their community over the last six years. This extremely long recall period must be considered when drawing inferences from the data. People are likely to under-report less severe (and therefore less memorable) events, particularly those that happened to community members in larger communities. Respondents are also more likely to recall events that happened to family members than those that happened to community members. Other biases may also be at play - for example, those who have been most highly affected by conflict over the last six years may have moved to another community. These factors demonstrate the importance of implementing a regular data collection schedule, which would allow far more accurate data to be collected.

Sampling Bias The GHS is a panel survey taking place over multiple rounds through a period of time. Therefore, households that are more mobile or households that are nomadic are less likely to be represented in this sample. This may be particularly relevant in circumstances where nomadic groups are named as perpetrators of conflict events.

Power Dynamics There are some disadvantages to the phone system, and for this reason it should be supplemented by additional types of data collection wherever possible. In a mobile phone survey, the respondent is the person who owns a mobile phone. In many areas, particularly those highly affected by poverty and those located in rural areas, only one family member owns a mobile phone. This is generally the household head, who is most likely male. Furthermore, in many of these communities, women are not allowed to have access to mobile phones and are forbidden from speaking to outsiders, which can prohibit mobile phone-based data collection.

Gender Dynamics The questionnaire was administered to only one respondent per household - most often a male household head. This means that crimes that carry stigma, especially sexual violence, are less likely to be reported. In this dataset, no sexual assault was reported despite data collected elsewhere that indicate that rape was used as a weapon by Boko Haram and elsewhere. This also means that violence that affects members of the household with less power (such as women, children, and employees), is less likely to be reported. This may be particularly important when considering violence not related to ongoing external conflict, such as domestic violence.

Abstract

The objective of the Nigeria NLPS Phase 2 is to monitor in real-time how the Nigerian households are coping with national and global crises and their effects on the welfare and livelihoods of the households. The households in the Phase 2 are drawn from the sample of households interviewed in GHS-Panel 2018/19 including those interviewed during the Phase 1. This survey has become a flexible tool that contributes to filling critical gaps in information that could be used by the Nigerian government and stakeholders to help design policies to mitigate the negative impacts of the COVID-19 pandemic, the oil prices crises, inflation and global value chain crises, among others. The Nigeria NLPS Phase 2 is designed to accommodate the evolving nature of the crises, including revision of the questionnaire on a bi-monthly basis.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

BASELINE (ROUND 1): Wave 4 of the GHS-Panel conducted in 2018/19 served as the frame for the Nigeria NLPS surveys. The GHS-Panel sample includes 4,976 households that were interviewed in the post-harvest visit of the fourth wave in January/February 2019. This sample of households is representative nationally as well as across the 6 geopolitical Zones that divide up the country. In every visit of the GHS-Panel, phone numbers are collected from interviewed households for up to 4 household members and 2 reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NBS and the GHS-Panel households made this an ideal frame from which to conduct the NLPS in Nigeria.

Among the 4,976 households interviewed in the post-harvest visit of the GHS-Panel in 2019, 4,934 (99.2%) provided at least one phone number. Around 90 percent of these households (4,440) provided a phone number for at least one household member while the remaining 10 percent only provided a phone number for a reference person. For the second phase of the NLPS, all 4,440 GHS-Panel households with household member contact details were included in the sample to be contacted. This included the sample of households from the first phase of the NLPS who had household member contact details (2,701 of 3,000). Based on the response rate in the first phase of the NLPS of 65 percent, this was expected to yield an interviewed sample of nearly 2,900 households that is both nationally representative as well as representative of urban and rural areas of the country.

ROUND 2: Interviewers attempted to contact and interview all 2,922 households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2. The second round of the NLPS Phase 2 also included individual-level data collection on the migration history of household members. For the migration module, information on adult (15 years or older) members of the household was targeted, including respondents that fall into this age range. However, information was not captured for all adult members. In order to limit the burden for respondents and interviewers in cases where the number of adult members is large, a maximum of 6 household members were selected (in addition to the main respondent) to capture information on migration. Therefore, for households with less than 6 adult members, all eligible members were included. However, 93 percent of interviewed households had 6 or less adult members and only 7 percent had more than six. For the 7 percent with more than 6 adult members, 6 members were randomly selected from among the pool of eligible members. The selection was stratified by sex with an equal split of 3 male and 3 females was targeted, depending on the pool of eligible males and females. However, the application of selection as relatively rare.

ROUND 3: Interviewers attempted to contact and interview all 2,811 households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2, excluding 41 households that refused in Round 2. The third round of the NLPS Phase 2 also included individual-level data collection on employment and job history of household members. For the employment and job history modules, information on adult (15 years or older) members of the household was targeted, including respondents that fall into this age range. However, information was not captured for all adult members. In order to limit the burden for respondents and interviewers in cases where the number of adult members is large, a maximum of 4 household members were selected (in addition to the main respondent) to capture information on employment and job history. Therefore, for households with less than 4 adult members, all eligible members were included.

However, 90 percent of interviewed households had 4 or less adult members and only 10 percent had more than four. For the 10 percent with more than 4 adult members, 4 members were randomly selected from among the pool of eligible members. The selection was stratified by sex with an equal split of 2 male and 2 females was targeted, depending on the pool of eligible males and females. The selection of eligible household members in Round 3 was conditional to the selection conducted in Round 2 for the migration module. In that round, up to 6 household members were selected (15 years or older) to answer the migration module. However, the application of selection as relatively rare.

ROUND 4: Interviewers attempted to contact and interview all 2,852 households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2, excluding 70 households that refused in previous rounds of the survey.

ROUND 5: Interviewers attempted to contact and interview 2,824 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 98 households that refused in previous rounds of the survey.

ROUND 6: Interviewers attempted to contact and interview 2,799 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 123 households that refused in previous rounds of the survey.

ROUND 7: Interviewers attempted to contact and interview 2,784 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 138 households that refused in previous rounds of the survey.

ROUND 8: Interviewers attempted to contact and interview 2,771 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 151 households that refused in previous rounds of the survey.

ROUND 9: Interviewers attempted to contact and interview 2,753 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 169 households that refused in previous rounds of the survey.

ROUND 10: Interviewers attempted to contact and interview 2,743 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 179 households that refused in previous rounds of the survey.

ROUND 11: Interviewers attempted to contact and interview 2,732 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 190 households that refused in previous rounds of the survey.

ROUND 12: Interviewers attempted to contact and interview 2,724 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 198 households that refused in previous rounds of the survey.

ROUND 13: Interviewers attempted to contact and interview 2,714 households consisting of households that were successfully interviewed in the baseline (round 1) of the NLPS Phase 2 excluding 208 households that refused in previous rounds of the survey.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to health services; employment and non-farm enterprise; and COVID-19 vaccine.

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; migration; employment; and household migrants.

ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to health services; employment; job history; and COVID-19 vaccine.

ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to health services; petrol; employment; credit; and economic sentiments. While the Household Questionnaire was administered to all the sample households, economic sentiments questions were asked to only half of the sample households (randomly selected).

ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to health services; employment; COVID-19 vaccine; economic sentiments; and farmer screening. While the Household Questionnaire was administered to all the sample households,

Abstract

The 2017 Conflict and Violence in Nigeria study reports on the prevalence of conflict and violence, and how these affect Nigerian households, between 2010 and 2017. The report takes into account conflict- and violence-related events of all types, independent of the cause or perpetrator of the event. This approach seeks to provide a better understanding of the extent to which households are affected by violence and conflict, as well as their perceived risk of exposure to conflict. It assumes that the economic and social impacts of violence are meaningful no matter what the cause. The report also provides context on the perceived causes and perpetrators of the conflict and violence. This data can be useful in informing response to and prevention of these events. This report seeks to explain the prevalence of conflict and violence, and how these affect Nigerian households, between 2010 and 2017. The report takes into account conflict- and violence-related events of all types, independent of the cause or perpetrator of the event. This approach seeks to provide a better understanding of the extent to which households are affected by violence and conflict.

Conflict in Nigeria: - Conflict was higher in 2016 than in 2010 in each of the three zones - Households in North East Nigeria are the most exposed to all types of conflict events - Each of the three geopolitical zones surveyed has a distinct principal cause of conflict - A small minority of conflict-affected households in any of Nigeria's geopolitical zones receive any form of assistance

Key Lessons: - Overall levels of conflict have risen between 2010 and 2016 - Sustained conflict is known to be both caused by and contribute to poverty; however, according to our findings wealth does not protect households from exposure to conflict and violence in Nigeria - Many conflict events are never reported to authorities; engaging community and religious leaders in surveillance may improve rates of reporting events and improve overall understanding of the changing context of conflict and violence across Nigeria - Only a small minority of conflict-affected households receive any type of assistance in support of their recovery - increased reporting and a stronger response system may aid in post-conflict rehabilitation - Phone-based data collection can improve understanding of conflict and violence, especially in areas where insecurity prevents face-to-face access to community members

Geographic coverage

Zones States Local Government Areas (LGAs) Households

Analysis unit

Individuals Households Communities

Universe

The survey covered all household members. The questionnaire was administered to only one respondent per household - most often a male household head.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was a telephone based survey conducted between March 22 and May 10th, 2017. The interview was the first round of a telephone survey using a sub-set of the sample of GHS (General Household Survey) households. The first round was focused on conflict exposure, while the second round not discussed in this report focused on food insecurity in conflict affected regions. This first round of the telephone interview had 717 completed interviews with the following geographical distribution: 175 interviews in the North East, 276 in North Central and 266 in South South.

In the three conflict affected geographical zones comprising of 16 states of Nigeria, households from LGS's that had high conflict exposure were oversampled chosen for a pilot sample, conducted before the telephone surveys. These LGS's were chosen based on the following criteria: The oversampled LGS's needed to have over 10 conflict events during 2012-14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database.

The first round of the telephone survey (which took place after the pilot), first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 per cent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews.

Conflict affected areas were oversampled in order to have a large enough sample of individuals that in fact experienced conflict events in order to shed light on the type of events that have happened. A random sample of the zones might have given too small sample of conflict affected households and therefore restricted the analysis of the various types of conflict events. Due to the oversampling however, the sample drawn was not representative at the level of the geographical zone, as is the case in the GHS. Therefore in the analysis we use probability weights that adjust for the propensity of being in a conflict affected LGA in order to ensure that the sample is representative at the level of the geographical zone.

During the second round of the survey 582 of the 717 households were re-interviewed on food security related issues (only the 717 were attempted to be reached). The data on the second telephone interview on food security as well as issues related to attrition in reaching the households are discussed in a separate report.

Sampling deviation

No deviation

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is divided into sections with a household roster.

Cleaning operations

Data was analyzed using descriptive statistics in Stata 15. All data analysis was tracked using comprehensive do files to ensure reproducibility. All statistics presented in this report have been adjusted with probability weights, when possible, to be representative at the level of the geopolitical zone. Demographics for each geopolitical zone were analyzed based on the complete GHS 2016 dataset.

Response rate

1030 households were attempted to be reached, with a final sample size of 717 completed interviews. The response rate is 96%

Sampling error estimates

No sampling error

Data appraisal

• Recall Bias - In the pilot data collection, respondents were asked to report on conflict events that had taken place in their family and their community over the last six years. This extremely long recall period must be considered when drawing inferences from the data. People are likely to under-report less severe (and therefore less memorable) events, particularly those that happened to community members in larger communities. Respondents are also more likely to recall events that happened to family members than those that happened to community members. Other biases may also be at play - for example, those who have been most highly affected by conflict over the last six years may have moved to another community. These factors demonstrate the importance of implementing a regular data collection schedule, which would allow far more accurate data to be collected.

• Sampling Bias - The GHS is a panel survey taking place over multiple rounds through a period of time. Therefore, households that are more mobile or households that are nomadic are less likely to be represented in this sample. This may be particularly relevant in circumstances where nomadic groups are named as perpetrators of conflict events.

• Power Dynamics - There are some disadvantages to the phone system, and for this reason it should be supplemented by additional types of data collection wherever possible. In a mobile phone survey, the respondent is the person who owns a mobile phone. In many areas, particularly those highly affected by poverty and those located in rural areas, only one family member owns a mobile phone. This is generally the household head, who is most likely male. Furthermore, in many of these communities, women are not allowed to have access to mobile phones and are forbidden from speaking to outsiders, which can prohibit mobile phone-based data collection.

• Gender Dynamics - The questionnaire was administered to only one respondent per household - most often a male household head. This means that crimes that carry stigma, especially sexual violence, are less likely to be reported. In this dataset, no sexual assault was reported despite data collected elsewhere that indicate that rape was used as a weapon by Boko Haram and elsewhere. This also means that violence that affects members of the household with less power (such as women, children, and employees), is less likely to be reported. This may be particularly important when considering violence not related to ongoing external conflict, such as domestic violence.

Abstract

This document reports on the prevalence of conflict and violence, and how these affect Nigerian households, between 2010 and 2017. The report takes into account conflict- and violence-related events of all types, independent of the cause or perpetrator of the event. This approach seeks to provide a better understanding of the extent to which households are affected by violence and conflict, as well as their perceived risk of exposure to conflict. It assumes that the economic and social impacts of violence are meaningful no matter what the cause. The report also provides context on the perceived causes and perpetrators of the conflict and violence. This data can be useful in informing response to and prevention of these events.This report seeks to explain the prevalence of conflict and violence, and how these affect Nigerian households, between 2010 and 2017. The report takes into account conflict- and violence-related events of all types, independent of the cause or perpetrator of the event. This approach seeks to provide a better understanding of the extent to which households are affected by violence and conflict. Conflict in Nigeria · Conflict was higher in 2016 than in 2010 in each of the three zones · Households in North East Nigeria are the most exposed to all types of conflict events · Each of the three geopolitical zones surveyed has a distinct principal cause of conflict · A small minority of conflict-affected households in any of Nigeria's geopolitical zones receive any form of assistance

Key Lessons · Overall levels of conflict have risen between 2010 and 2016 · Sustained conflict is known to be both caused by and contribute to poverty; however, according to our findings wealth does not protect households from exposure to conflict and violence in Nigeria · Many conflict events are never reported to authorities; engaging community and religious leaders in surveillance may improve rates of reporting events and improve overall understanding of the changing context of conflict and violence across Nigeria · Only a small minority of conflict-affected households receive any type of assistance in support of their recovery - increased reporting and a stronger response system may aid in post-conflict rehabilitation · Phone-based data collection can improve understanding of conflict and violence, especially in areas where insecurity prevents face-to-face access to community members

Geographic coverage

Zones States Local Government Areas (LGAs) Households

Analysis unit

Individuals, Households and Communities

Universe

The Survey covered all household members. The questionnaire was administered to only one respondent per household - most often a male household head.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was a telephone based survey conducted between March 22 and May 10th, 2017. The interview was the first round of a telephone survey using a sub-set of the sample of GHS (General Household Survey) households. The first round was focused on conflict exposure, while the second round not discussed in this report focused on food insecurity in conflict affected regions. This first round of the telephone interview had 717 completed interviews with the following geographical distribution: 175 interviews in the North East, 276 in North Central and 266 in South South. In the three conflict affected geographical zones comprising of 16 states of Nigeria, households from LGS's that had high conflict exposure were oversampled chosen for a pilot sample, conducted before the telephone surveys. These LGS's were chosen based on the following criteria: The oversampled LGS's needed to have over 10 conflict events during 2012-14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database. The first round of the telephone survey (which took place after the pilot), first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 per cent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews. Conflict affected areas were oversampled in order to have a large enough sample of individuals that in fact experienced conflict events in order to shed light on the type of events that have happened. A random sample of the zones might have given too small sample of conflict affected households and therefore restricted the analysis of the various types of conflict events. Due to the oversampling however, the sample drawn was not representative at the level of the geographical zone, as is the case in the GHS. Therefore in the analysis we use probability weights that adjust for the propensity of being in a conflict affected LGA in order to ensure that the sample is representative at the level of the geographical zone.
During the second round of the survey 582 of the 717 households were re-interviewed on food security related issues (only the 717 were attempted to be reached). The data on the second telephone interview on food security as well as issues related to attrition in reaching the households are discussed in a separate report.

Sampling deviation

No deviation

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is divided into sections with a household roster. Information on Conflict and Violence from the year 2010 to 2017, Causes and perpetrators.

Cleaning operations

Data was analyzed using descriptive statistics in Stata 15. All data analysis was tracked using comprehensive do files to ensure reproducibility. All statistics presented in this report have been adjusted with probability weights, when possible, to be representative at the level of the geopolitical zone. Demographics for each geopolitical zone were analyzed based on the complete GHS 2016 dataset.

Response rate

The first round of the telephone survey (which took place after the pilot), first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 per cent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews. The response rate is 96%

Sampling error estimates

No Sampling Error

Data appraisal

Limitations Recall Bias In the pilot data collection, respondents were asked to report on conflict events that had taken place in their family and their community over the last six years. This extremely long recall period must be considered when drawing inferences from the data. People are likely to under-report less severe (and therefore less memorable) events, particularly those that happened to community members in larger communities. Respondents are also more likely to recall events that happened to family members than those that happened to community members. Other biases may also be at play - for example, those who have been most highly affected by conflict over the last six years may have moved to another community. These factors demonstrate the importance of implementing a regular data collection schedule, which would allow far more accurate data to be collected. Sampling Bias The GHS is a panel survey taking place over multiple rounds through a period of time. Therefore, households that are more mobile or households that are nomadic are less likely to be represented in this sample. This may be particularly relevant in circumstances where nomadic groups are named as perpetrators of conflict events. Power Dynamics There are some disadvantages to the phone system, and for this reason it should be supplemented by additional types of data collection wherever possible. In a mobile phone survey, the respondent is the person who owns a mobile phone. In many areas, particularly those highly affected by poverty and those located in rural areas, only one family member owns a mobile phone. This is generally the household head, who is most likely male. Furthermore, in many of these communities, women are not allowed to have access to mobile phones and are forbidden from speaking to outsiders, which can prohibit mobile phone-based data collection. Gender Dynamics The questionnaire was administered to only one respondent per household - most often a male household head. This means that crimes that carry stigma, especially sexual violence, are less likely to be reported. In this dataset, no sexual assault was reported despite data collected elsewhere that indicate that rape was used as a weapon by Boko Haram and elsewhere. This also means that violence that affects members of the household with less power (such as women, children, and employees), is less likely to be reported. This may be particularly important when considering violence not related to ongoing external conflict, such as domestic violence.