Nigeria Living Standards Survey 2018-2019
Nigeria, 2018 - 2019
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NGA-NBS-NLSS-2018-v01
Nigeria Living Standards Survey 2018-2019
NLSS 2018-19
| Name | Country code |
|---|---|
| Nigeria | NGA |
Income/Expenditure/Household Survey [hh/ies]
The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.
The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.
Sample survey data [ssd]
- Households
- Individuals
- Communities
Version
Version 01: Edited, anonymized dataset for public distribution
Scope
The Nigeria Living Standards Survey 2018/19 covered the following topics:
Household:
- Demographic
- Education
- Health
- Labour
- Food and non-food expenditure
- Household nonfarm income-generating activities
- Food security and shocks
- Safety nets
- Housing conditions
- Assets
- Information and communication technology
- Agriculture and land tenure
- Other sources of household income
Community:
- Access to transported and infrastructure
- Community organizations
- Resource management
- Changes in the community
- Key events
- Community needs, actions and achievements
- Local retail price information
Coverage
National coverage
The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.
Producers and sponsors
| Name | Affiliation |
|---|---|
| National Bureau of Statistics (NBS) | Federal Government of Nigeria |
| Name | Role |
|---|---|
| World Bank | Collaborated in the implementation of the survey |
| Name | Abbreviation | Role |
|---|---|---|
| Federal Government of Nigeria | FGN | Funded the study |
| The World Bank | WB | Funded the study |
| Department for International Development | DFID | Funded the study |
| National Social Safety-Net Coordinating Office | NASSCO | Funded the study |
Sampling
The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.
Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.
EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.
Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.
A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.
HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.
Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.
Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.
The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.
In order for the sample estimates from the 2018/19 NLSS data to be representative of the population, it is necessary to multiply the data by a sampling weight, or expansion factor. The basic weight for each sample household is equal to the inverse of its probability of selection (calculated by multiplying the probabilities at each sampling stage). See the detail in the Basic Information Document.
CALIBRATION: The base weight obtained above reflects the sample selection process in its entirety. However, in order to better ensure the weighted estimates obtained from the survey truly reflect the underlying population distribution across the strata, the weights can be calibrated to known population totals. Following the base weight calculation, the 2018/19 NLSS weights were calibrated to projected total state population in 2019 using a generalized regression approach. The calibration adjustment was performed at the EA level such that all households within the EA maintain the same weight.
The household weights can be found in the cover page data file “secta_cover.dta”. The variable name in the data file is wt_final.
Survey instrument
Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. 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.
Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.
Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.
Data collection
| Start | End |
|---|---|
| 2018-09-28 | 2019-09-28 |
- Computer Assisted Personal Interview [capi]
| Name | Affiliation | Abbreviation |
|---|---|---|
| National Bureau of Statistics | Federal Government of Nigeria | NBS |
IN-PERSON MONITORING OF FIELDWORK: To ensure that good quality data is collected, an extensive field monitoring exercise was mounted during the 12-months of data collection. The first monitoring was implemented immediately after the second level training and at the start of the fieldwork. In the first round of monitoring, one senior technical staff (trainer) that was part of the first and second training was assigned to monitor field teams in 2-3 states. These monitors visited their respective teams 4 times during the 12-month period, with the visits scheduled such that the monitors visit the field teams every quarter. On each visit to the team, the monitors spent up to 6 days with the team, including field visits to oversee interviews and conduct spot-checks.
The monitors were tasked with ensuring the interviewers were fully conforming with the procedures laid out in the manual and explained during training as well as effecting necessary corrections and tackling any problems that the field teams might face. During each visit to the teams, monitors were given a monitoring questionnaire to complete and upload to the project server. This was to ensure that the monitors visited the teams, spent the required number of days with the teams, and reported vividly and accurately, their observations from their monitoring visit. The monitors were also charged to continue to follow-up with the teams in their respective states throughout the course of the fieldwork, remotely address any issues or challenges that they might have, and filter unsurmountable challenges to the management and senior technical persons to address.
During the periods when the monitors are not with the teams, the state officers and zonal controllers took in-person field monitoring responsibilities, reporting directly to headquarters any issues/challenges that could mar the quality of the data collected. While the state officers monitored in their own state, the zonal controllers conducted monitoring in at least 2 states (the zonal headquarters state and one other state of the same zone).
REMOTE MONITORING OF FIELDWORK: In addition to the in-person monitoring of quality of the data collection by the monitors, there was also an extensive remote monitoring effort conducted by NBS ICT team and the World Bank technical team. The first level of remote monitoring was performed by the NBS ICT (data editors and data managers). The data editors would review every incoming interview from the field for any potential errors or omissions. Their review was also complimented by the second level of monitoring performed by the data managers and the World Bank technical team. Each day, the live data was downloaded from the server and a comprehensive set of error, outlier, and consistency checks were performed on newly submitted interviews. A report was generated for each case and provided to the data editors. The data editors reviewed the issues identified and included them in their own review of the interviews. If any issues were identified by the data editor’s review or from the global data checks, then the data editor would make comments in the interview and reject it back to the interviewer for them to address the identified issues. Data editors would also contact interviewers or supervisors for persistent or complicated problems that need to be more thoroughly addressed. After the issues were addressed either through a re-interview of the household or explanation by the interviewer, the interviewer would send the interview back to the data editor who would either approve the case or reject back for further clarification.
In addition to the daily interview review and global data checks, a dashboard was developed which tracked fieldwork progress and interviewer performance. This dashboard allowed NBS coordinators and the World Bank technical team to more broadly monitor data quality and spot consistent issues or particular teams who needed additional attention.
ORGANIZATION OF FIELDWORK: Each state had one field team comprising one supervisor and three interviewers, who worked in a roving manner. The team traveled to an EA, interviewed all selected households and conducted the community interview, including collecting market prices, and then moved to the next EA. Teams spent on average 3 days in an EA, with each interviewer interviewing one household per day. The supervisor administered the community questionnaire, collected the market prices, and then interviewed one remaining household after the 3 interviewers had each interviewed 3 households over the 3-day period. Thus, in addition to the community questionnaire, the supervisor conducted one household interview per EA. Besides interviewing all selected households fully and accurately, spending 3 days per EA allowed the team to also address all error checks, comments, and feedback that were sent to them by the data editor, NBS headquarters team, and the World Bank technical team.
Given that the survey was conducted over a 12-month period, the teams were in the field throughout the duration of the fieldwork except for scheduled breaks. Longer breaks in the fieldwork occurred in the holiday period of late December/early January as well as during the federal elections held in February 2019. Shorter breaks occurred for other holidays such as Easter and Eid.
PRE-LOADED INFORMATION: Basic identification information (location, household head name, phone number, etc.) on every household was pre-loaded in the CAPI assignments for each interviewer. The information was pre-loaded to assist interviewers in locating and identifying the household. The pre-loaded basic household information was derived from the household listing exercise.
Data processing
CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet which they used to conduct the interviews. Overall, implementation of the survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews and real-time quality checks.
DATA COMMUNICATION SYSTEM: The data communication system used in 2018/19 NLSS was highly automated. Each field team was given a mobile modem to allow for internet connectivity and daily synchronization of their assignments and completed interviews. This ensured that headquarters in Abuja had access to the data in real-time. Once each 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 dofiles run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application during data collection and entry. An excel error file is generated following the running of the Stata dofiles 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 was done on a daily basis for the duration of the survey.
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. 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. 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.
Data Access
Before being granted access to the dataset, all users have to formally agree:
- To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor.
- Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files.
- To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download.
E.g. National Bureau of Statistics. Nigeria Living Standards Survey (NLSS) 2018/19. Dataset downloaded from [source] on [date].
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Dr. Yemi Kale | Statistician General, National Bureau of Statistics (NBS) | ykale@nigerianstat.gov.ng | www.nigerianstat.gov.ng/nada |
| Biyi Fafunmi | National Bureau of Statistics (NBS) | biyifafunmi@nigerianstat.gov.ng | www.nigerianstat.gov.ng/nada |
| Tunde Adebisi | National Bureau of Statistics (NBS) | tundeadebisi@nigerianstat.gov.ng | www.nigerianstat.gov.ng/nada |
Metadata production
DDI-NGA-NBS-NLSS-2018-v01
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| ICT Department | National Bureau of Statistics (NBS) | Documentation of the DDI | |
| Development Economics Data Group | DECDG | The World Bank | Documentation of the DDI |
2020-06-29
Metadata version
Version 01 (June 2020)