Abstract

The primary mission of the 2006 Population and Housing Census (PHC) of Nigeria was to provide data for policy-making, evidence-based planning and good governance. The Government at all tiers, researchers, the academia, civil society organizations and the international agencies will find the sets of socio-demographic data useful in formulating developmental policies and planning. The 2006 data will certainly provide benchmarks for monitoring the Millennium Development Goals (MDGs). Enumeration in the 2006 PHC was conducted between March 21st and 27th 2006. It was designed to collect information on the quality of the population and housing, under the following broad categories: demographic and social, education, disability, household composition, economic activity, migration, housing and amenities, mortality and fertility. The results of the exercise are being released as per the Commission's Tabulation Plan which began with the release of the total enumerated persons by administrative areas in the country in the Official Gazette of the Federal Republic of Nigeria No.2, Vol 96 of February 2,2009 and followed with the release of Priority Tables that provide some detailed characteristics of the population of Nigeria by State and LGA.

Geographic coverage

National

Analysis unit

Individuals Households

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Census 2006 Processing: The Technology and Methodology:-

Unlike the data capture method used for the country’s previous censuses, where information from the census forms are typed into the computer system, data capture for census 2006 was carried out by OMR/OCR/ICR systems where questionnaires are scanned through high speed optical scanners. The choice of the scanning system was because it is faster and more accurate than the data keying method.

OMR/OCR/ICR Technology

Definition of terms

• OMR (Optical Mark Recognition) - This means the ability of the scanning machine to detect pencil marks made on the questionnaires by the Enumerators in accordance with the responses given by the respondents.
• OCR (Optical Character Recognition) - This means the ability of the scanning machine to recognize machine printed characters on the questionnaires.
• ICR (Intelligent Character Recognition) - This means the ability of the scanner to recognize characters hand written by the Enumerators in accordance with the responses given by the respondents.

Processing Procedures of Census 2006 at the DPCs:- Data processing took place in the Commission’s seven (7) Data Processing Centres located in different geographical zones in the country. There was absolute uniformity in the processing procedures in the seven DPCs.

(a) Questionnaire Retrieval/Archiving Questionnaires from the fields were taken directly from the Local Government Areas to designated DPCs. The forms on arrival at the DPCs were counted, archived and labeled. Retrieval of the questionnaires at the DPCs were carried out based on the EA frame received from the Cartography Department. Necessary Transmittal Forms are completed on receipt of the Forms at the DPCs. The Transmittal Forms are also used to keep track of questionnaires movement within the DPC.

(b) Forms Preparation The scanning machine has been designed to handle A4 size paper. And the Census form being twice that size has to be split into two through the dotted lines at the middle of the form. This forms preparation procedure is to get the questionnaires, for each Enumeration Areas (EAs), ready for scanning. There is a Batch Header to identify each batch.

(c) Scanning Each Batch on getting to the Scanning Room was placed on joggers (a vibrating machine)to properly align the forms, and get rid of dust or particles that might be on the forms.

The forms are thereafter fed into the scanner. There were security codes in form of bar codes on each questionnaire to identify its genuineness. There was electronic editing and coding for badly coded or poorly shaded questionnaires by the Data Editors. Torn, stained or mutilated forms are rejected by the scanner. These categories of forms were later manually keyed into the system.

Re-archiving of Scanned Forms:- Scanned forms were placed in their appropriate marked envelopes in batches, and thereafter returned to the Archiving Section for re-archiving.

Data Output from the Scanning Machine:- The OMR/OCR Software interprets the output from the scanner and translates it into an XML file from where it is further translated into the desired ASCII output that is compatible for use by the CSPro Package for further processing and tabulation.

Data back-up and transfer:- After being sure that the data are edited for each EA batch in an LGA, data then was exported to the SAN (Storage Area Network) of the Server. Two copies of images of the questionnaires for each EA copied to the LTO tapes as backup and then transferred to the Headquarters. The ASCII data files for each LGA are zipped and encrypted, and thereafter transfer to the Data Validation Unit (DVU) at the Headquarters in Abuja.

Data appraisal

Data collation and validation:- The Data Validation Unit at the Headquarters was responsible for collating these data into EAs, LGAs, States and National levels. The data are edited/validated for consistency errors and invalid entries. The Census and Survey Processing (CSPro) software is used for this process. The edited, and error free data are thereafter processed into desired tables.

Activities of the Data Validation unit (DVU):-

Decryption of each LGA Data File Concatenation/merging of Data Files Check each EA batch file for EA completeness within an LGA and State Check for File/Data Structure Check for Range and Invalid Data items Check for Blank and empty questionnaire Check for inter and intra record consistency Check for Skip Patterns Perform Data Validation and Imputation Generate Statistics Report of each function/activity Generate Statistical Tables on LGA, State and National levels.

Abstract

The objectives of the Smallholder Household Survey in Nigeria were to:

• Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships • Segment smallholder households in Nigeria according to the most compelling variables that emerge • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING PROCEDURE

The smallholder household survey in Nigeria is a nationally-representative survey with a target sample size of 3,000 smallholder households. In order to take nonresponse into account, the target sample size was increased to 3,225 households assuming a response rate of 93%. The sample was designed to produce national level estimates as well as estimates for each of the six geo-political zones. Nigeria is comprised of the following states:

• North Central: Benue, Federal Capital Territory (FCT), Kogi, Kwara, Nasarawa, Niger, and Plateau
• North East: Adamawa, Bauci, Borno, Gombe, Taraba and Yobe
• North West: Jigawa, Kaduna, Kano, katsina, Kebbi, Sokoto and Zamfara
• South East: Abia, Anambra, Ebonyi, Enugu, and Imo
• South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and River
• South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

(b) SAMPLING FRAME

Nigeria is divided into 774 local governments (LGAs) and its last housing and population census took place in 2006. In preparation for this last census, the National Population Commission (NPopC) demarcated over 662,000 enumeration areas (EAs) for the country. From these EAs, two hierarchical master sample frames were developed by the Nigeria Bureau of Statistics (NBS): the LGA master frame and the National Integrated Survey of Households (NISH). The smallholder survey used the NISH as sampling frame but retained only the EAs containing agricultural households.

(c) SAMPLE ALLOCATION AND SELECTION

The total sample size was first allocated to the geo-political zones in proportion to their number of agricultural EAs in the sampling frame. Within each zone, the resulting sample was then further distributed to states proportionally to their number of agricultural EAs. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total number of 215 EAs were selected. The sample for the smallholder survey is a stratified multistage sample. A stratum corresponds to a state and the sample was selected independently in each stratum. In the first stage, EAs were selected as primary sampling units with equal probability. A household listing operation was carried out in all selected EAs to identify smallholder households and to provide a frame for the selection of smallholder households to be included in the sample. In the second stage, 15 smallholders were selected in each EA with equal probability. In each selected household, a household questionnaire was administered to the head of the household, the spouse or any knowledgeable adult household member to collect information about household characteristics. A multiple respondent questionnaire was administered to all adult members in each selected household to collect information on their agricultural activities, financial behaviours and mobile money usage. In addition, in each selected household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

The household listing operation identified fewer than 15 smallholder households in many sampled EAs. As a result, the sample take of 15 households per EA couldn't be implemented in those EAs. To avoid a situation where a sample falls short, the sample take was increased to 17 smallholder households where possible while retaining in the sample all smallholder households in EAs with fewer than 17 smallholder households. This yielded 3,457 sampled households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible. Following the finalization of questionnaires, a script was developed using Dooblo to support data collection on smart phones. The script was thoroughly tested and validated before its use in the field. The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection.

Response rate

• A total of 3,457 households was selected for the survey, of which 3,310 were found to be occupied during data collection. Of these occupied households, 3,026 were successfully interviewed, yielding a household response rate of 91 percent.

• In the interviewed households 6,643 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,128 eligible household members, yielding a response rate of 77 percent for the Multiple Respondent questionnaire.

• Among the 3,206 eligible household members selected for the Single Respondent questionnaire, 2,773 were successfully interviewed, yielding a response rate of 92 percent.

Sampling error estimates

For key survey estimates, sampling errors considering the design features were produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.

Abstract

The 2018 Nigeria Demographic and Health Survey (2018 NDHS) was implemented by the National Population Commission (NPC). Data collection took place from 14 August to 29 December 2018. ICF provided technical assistance through The DHS Program, which is funded by the United States Agency for International Development (USAID) and offers financial support and technical assistance for population and health surveys in countries worldwide. Other agencies and organisations that facilitated the successful implementation of the survey through technical or financial support were the Global Fund, the Bill and Melinda Gates Foundation (BMGF), the United Nations Population Fund (UNFPA), and the World Health Organization (WHO).

SURVEY OBJECTIVES The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

national coverage

Analysis unit

Households Women Men children

Universe

the survey covered all household members (permanent residents and visitor), all Women aged 15-49 years, all children 0-59 months and all men aged 15-59 years in one-third of households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

In the first stage, 1,400 EAs were selected with probability proportional to EA size. EA size was the number of households in the EA. A household listing operation was carried out in all selected EAs, and the resulting lists of households served as a sampling frame for the selection of households in the second stage. In the second stage’s selection, a fixed number of 30 households was selected in every cluster through equal probability systematic sampling, resulting in a total sample size of approximately 42,000 households. The household listing was carried out using tablets, and random selection of households was carried out through computer programming. The interviewers conducted interviews only in the pre-selected households. To prevent bias, no replacements and no changes of the pre-selected households were allowed in the implementing stages.

Due to the non-proportional allocation of the sample to the different states and the possible differences in response rates, sampling weights were calculated, added to the data file, and applied so that the results would be representative at the national level as well as the domain level. Because the 2018 NDHS sample was a two-stage stratified cluster sample selected from the sampling frame, sampling weights were calculated based on sampling probabilities separately for each sampling stage and for each cluster.

The survey was successfully carried out in 1,389 clusters after 11 clusters with deteriorating law-and-order situations during fieldwork were dropped. These areas were in Zamfara (4 clusters), Lagos (1 cluster), Katsina (2 clusters), Sokoto (3 clusters), and Borno (1 cluster). In the case of Borno, 11 of the 27 LGAs were dropped due to high insecurity, and therefore the results might not represent the entire state. Please refer to Appendix A in the final report for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

The survey protocol was reviewed and approved by the National Health Research Ethics Committee of Nigeria (NHREC) and the ICF Institutional Review Board. After all questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo. The 2018 NDHS used computer-assisted personal interviewing (CAPI) for data collection.

The Household Questionnaire listed all members of and visitors to selected households. Basic demographic information was collected on each person listed, including age, sex, marital status, education, and relationship to the head of the household. For children under age 18, survival status of parents was determined. Data on age, sex, and marital status of household members were used to identify women and men who were eligible for individual interviews. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of drinking water; type of toilet facilities; materials used for flooring, external walls, and roofing; ownership of various durable goods; and ownership of mosquito nets. In addition, data were gathered on salt testing and disability.

The Woman’s Questionnaire was used to collect information from all eligible women age 15-49. These women were asked questions on the following topics: - Background characteristics (including age, education, and media exposure) - Birth history and child mortality - Knowledge, use, and source of family planning methods - Antenatal, delivery, and postnatal care - Vaccinations and childhood illnesses - Breastfeeding and infant feeding practices - Women’s minimum dietary diversity - Marriage and sexual activity - Fertility preferences (including desire for more children and ideal number of children) - Women’s work and husbands’ background characteristics - Knowledge, awareness, and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs) - Knowledge, attitudes, and behaviour related to other health issues (e.g., smoking) - Female genital cutting - Fistula - Adult and maternal mortality - Domestic violence

The Man’s Questionnaire was administered to all men age 15-59 in the subsample of households selected for the men’s survey. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health.

The Biomarker Questionnaire was used to record the results of anthropometry measurements and other biomarkers for women and children. This questionnaire was administered only to the subsample selected for the men’s survey. All children age 0-59 months and all women age 15-49 were eligible for height and weight measurements. Women age 15-49 were also eligible for haemoglobin testing. Children age 6-59 months were also eligible for haemoglobin testing, malaria testing, and genotype testing for sickle cell disease.

The purpose of the Fieldworker Questionnaire was to collect basic background information on the people who were collecting data in the field, including the team supervisor, field editor, interviewers, and the biomarker team

This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) with national coverage for Nigeria, along with estimates of the number of people belonging to various age-sex groups. Version 2.0 is an update of the previous version 1.2 gridded population estimates and is based on more recent and detailed settlement information and a different regional boundary definition. These model-based population estimates most likely represent the time period around 2019, corresponding to the period when the satellite imagery was processed to generate building footprints. Populations are mapped only in areas where residential settlements are predicted.

Abstract

The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-5 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

The processing of the 2018 NDHS data began almost immediately after the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NPC data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of April 2019.

Response rate

A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, yielding a response rate of 99%. In the households interviewed, 42,121 women age 15-49 were identified for individual interviews; interviews were completed with 41,821 women, yielding a response rate of 99%. In the subsample of households selected for the male survey, 13,422 men age 15-59 were identified and 13,311 were successfully interviewed, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2018 Nigeria Demographic and Health Survey (NDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Standardisation exercise results from anthropometry training - Height and weight data completeness and quality for children - Height measurements from random subsample of measured children - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends - Data collection period - Malaria prevalence according to rapid diagnostic test (RDT)

Note: See detailed data quality tables in APPENDIX C of the report.

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

Abstract

NASC is an exercise designed to fill the existing data gap in the agricultural landscape in Nigeria. It is a comprehensive enumeration of all agricultural activities in the country, including crop production, fisheries, forestry, and livestock activities. The implementation of NASC was done in two phases, the first being the Listing Phase, and the second is the Sample Survey Phase. Under the first phase, enumerators visited all the selected Enumeration Areas (EAs) across the Local Government Areas (LGAs) and listed all the farming households in the selected enumeration areas and collected the required information. The scope of information collected under this phase includes demographic details of the holders, type of agricultural activity (crop production, fishery, poultry, or livestock), the type of produce or product (for example: rice, maize, sorghum, chicken, or cow), and the details of the contact persons. The listing exercise was conducted concurrently with the administration of a Community Questionnaire, to gather information about the general views of the communities on the agricultural and non-agricultural activities through focus group discussions.

The main objective of the listing exercise is to collect information on agricultural activities at household level in order to provide a comprehensive frame for agricultural surveys. The main objective of the community questionnaire is to obtain information about the perceptions of the community members on the agricultural and non-agricultural activities in the community.

Additional objectives of the overall NASC program include the following: · To provide data to help the government at different levels in formulating policies on agriculture aimed at attaining food security and poverty alleviation · To provide data for the proposed Gross Domestic Product (GDP) rebasing

Geographic coverage

Estimation domains are administrative areas from which reliable estimates are expected. The sample size planned for the extended listing operation allowed reporting key structural agricultural statistics at Local Government Area (LGA) level.

Analysis unit

Agricultural Households.

Universe

Population units of this operation are households with members practicing agricultural activities on their own account (farming households). However, all households in selected EAs were observed as much as possible to ensure a complete coverage of farming households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

An advanced methodology was adopted in the conduct of the listing exercise. For the first time in Nigeria, the entire listing was conducted digitally. NBS secured newly demarcated digitized enumeration area (EA) maps from the National Population Commission (NPC) and utilized them for the listing exercise. This newly carved out maps served as a basis for the segmentation of the areas visited for listing exercise. With these maps, the process for identifying the boundaries of the enumeration areas by the enumerators was seamless.

The census was carried out in all the 36 States of the Federation and FCT. Forty (40) enumeration Areas (EAs) were selected to be canvassed in each LGA, the number of EAs covered varied by state, which is a function of the number of LGAs in the state. Both urban and rural EAs were canvassed. Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno States) were not covered due to insecurity (99% coverage). In all, thirty thousand, nine hundred and sixty (30,960) EAs were expected to be covered nationwide but 30,546 EAs were canvassed.

The Sampling method adopted involved three levels of stratification. The objective of this was to provide representative data on every Local Government Area (LGA) in Nigeria. Thus, the LGA became the primary reporting domain for the NASC and the first level of stratification. Within each LGA, eighty (80) EAs were systematically selected and stratified into urban and rural EAs, which then formed the second level of stratification, with the 80 EAs proportionally allocated to urban and rural according to the total share of urban/rural EAs within the LGA. These 80 EAs formed the master sample from which the main NASC sample was selected. From the 80 EAs selected across all the LGAs, 40 EAs were systematically selected per LGA to be canvassed. This additional level selection of EAs was again stratified across urban and rural areas with a target allocation of 30 rural and 10 urban EAs in each LGA. The remaining 40 EAs in each LGA from the master sample were set aside for replacement purposes in case there would be need for any inaccessible EA to be replaced.

Details of sampling procedure implemented in the NASC (LISTING COMPONENT). A stratified two-phase cluster sampling method was used. The sampling frame was stratified by urban/rural criteria in each LGA (estimation domain/analytical stratum).

First phase: in each LGA, a total sample of 80 EAs were allocated in each strata (urban/rural) proportionally to their number of EAs with reallocations as need be. In each stratum, the sample was selected with a Pareto probability proportional to size considering the number of households as measure of size.

Second phase: systematic subsampling of 40 EAs was done (10 in Urban and 30 in Rural with reallocations as needed, if there were fewer than 10 Urban or 30 Rural EAs in an LGA). This phase was implicitly stratified through sorting the first phase sample by geography.

With a total of 773 LGAs covered in the frame, the total planned sample size was 30920 EAs. However, during fieldwork 2 LGAs were unable to be covered due to insecurity and additional 4 LGAs were suspended early due to insecurity. For the same reason, replacements of some sampled EAs were needed in many LGAs. The teams were advised to select replacement units where possible considering appurtenance to the same stratum and similarity including in terms of population size. However about 609 EAs replacement units were selected from a different stratum and were discarded from data processing and reporting.

Sampling deviation

Out of 774 LGAs in the country, 767 LGAs were covered and the remaining 7 LGAs (4 in Imo and 3 in Borno states) were not covered due to insecurity (99% coverage).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASC household listing questionnaire served as a meticulously designed instrument administered within every household to gather comprehensive data. It encompassed various aspects such as household demographics, agricultural activities including crops, livestock (including poultry), fisheries, and ownership of agricultural/non-agricultural enterprises.

The questionnaire was structured into the following sections:

Section 0: ADMINISTRATIVE IDENTIFICATION Section 1: BUILDING LISTING Section 2: HOUSEHOLD LISTING (Administered to the Head of Household or any knowledgeable adult member aged 15 years and above).

Cleaning operations

Data processing of the NASC household listing survey included checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning was carried out electronically using the Stata software package. In some cases where data inconsistencies were found a call back to the household was carried out. A pre-analysis tabulation plan was developed and the final tables for publication were created using the Stata software package.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through re-sampling approaches (Bootstrap/Jackknife)

Nigeria (Kaduna & Lagos) Round 1 Household and Female (HQFQ) survey used a three-stage cluster design, with urban-rural as strata in Kaduna. A sample of 66 clusters of enumeration areas (EAs) in Kaduna and 37 clusters of enumeration areas (EAs) in Lagos were drawn from the National Population Commission’s master sampling frame along with a list of EAs contiguous to the index EA. The EAs in Nigeria are usually small, with approximately 48 households on average. To create clusters with a minimum of 200 households, each EA was listed and mapped. If there were fewer than 200 households identified, the next EA in the list of contiguous EAs was listed and mapped in its entirety. If the total number of households was still less than 200, the entirety of the third EA was listed and so on. Each cluster of EAs serves as the primary sampling unit from which 35 households (40 in Lagos) and up to 3 private health facilities in both Kaduna and Lagos were randomly selected. Households were surveyed and occupants enumerated. All eligible females age 15 to 49 were contacted and consented for interviews. The final sample included 2,194 households and 2,569 females in Kaduna; and 974 households and 764 females in Lagos. Data collection was conducted between September and October 2014. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/b88s-zx32

Abstract

The Republic of Sierra Leone is a small coastal West African country bordered by Guinea and Liberia. Sierra Leone has an area of 71,620 square kilometers (about 28,000 square miles). The country is divided into four major Administrative Areas namely, The Western Area, Northern Province, Southern Province and Eastern Province.

The Provinces are divided into twelve districts and the districts are divided into one hundred and forty nine chiefdoms. Western Area is divided into (Western Urban) Freetown and Western Rural Areas. Freetown is divided into wards.

There are five Physical Regions in Sierra Leone: (i) The Central Plains, (ii) The Northern Woodlands Savannah, (iii) The South Western Upland, (iv) The Western Coastal Swamps and (v) the Western Peninsula Upland Region.

The country is mountainous; about 50% of the terrain is covered by mountains including the Capital, Freetown. Agriculture is the main occupation for the people of Sierra Leone; especially rice farming in which about 60% of the people are engaged through the practice of shifting cultivation.

Sierra Leone has a tropical climate with two very different seasons - the Dry Season, traditionally from November to April and the Rainy Season from May to October with July and August being the wettest months of the year. In 2004, the census was taken in December.

BACKGROUND OF AND JUSTIFICATION FOR THE 2004 POPULATION AND HOUSING CENSUS

The first population count in Sierra Leone was undertaken in 1802 in what is now the Western Area. Subsequently, a number of population counts in various parts of the country were conducted. However, it was not until 1963 that the whole country was covered for the first time and, since then, censuses have been carried out at intervals ranging from 10 to 17 years.

The first full-scale modern Population Census, however, was that of April 1963. It was also the first post independence census and it was conducted with the expectation that a decennial census programme would be maintained. Due to various constraints, however, the next two censuses were conducted at eleven-year intervals in 1974 and 1985. Due to the war situation, a census was not conducted in 1995. The next Population Census was conducted in December 2004.

The 1985 census showed a total population of about 3.5 million. It was a de facto count with December 1, 2004 as the reference date. The topics covered in the census were: Relationship, sex, age, maternal orphan hood, birth place, nationality, place of residence, level of education, marital status, type of economic activity, occupation, industry, employment status, children ever born to women aged 10 years and above, particulars of most recent birth and housing conditions.

The census data was processed on a Wang Vs 80 mini-computer and data entry was done on 19 workstations.

The publication plan relating to the 1985 Census results proposed the following:

· The Preliminary Reports · National Statistical Tables · Summary Statistics on Settlements of 1000 or more people · Analytical Report · Report of Seminar on the use of census data

The provisional census results indicating a total population of 3,515,812 was announced in January 1986 and The Preliminary Report published in August 1986. Due to delay in the installation of the data processing equipments, however, there were delays in the production of the final statistical tables. Further, considerable time was spent investigating a significant difference (about 9 percent) between the provisional results and the total population figure of 3,222,901 obtained after the computer processing of the census returns. On the basis of the investigation, the Sierra Leone Government endorsed the 3, 515,812 as the total population of the 1985 Census.

Following the acceptance of the census results, a team of local consultants carried out the analysis of the census data on the following themes:

· The Land and The People · Population size, growth, age and sex structure · Education and Literacy · Employment and Labour Force · Nuptiality and Fertility · Mortality Levels and Differentials · Household and Dwelling Characteristics · The Census Operations

The following reports were published as a result of these consultant reports:

· Volume 1: Summary Results; May 1992 · Volume 2: National Dissemination Seminar Report; July1992 · Volume 3: Analytical Report, 1996

In addition to the published reports, census data including computer printout of tabulations were sent out to some Ministries, Departments and Agencies and various other data users such as the University of Sierra Leone.

Since the 1985 Population and Housing Census, a number of nation-wide surveys in the area of education, health, HIV/AIDS etc. have been conducted by the National Statistical Agency (formerly Central Statistics Office and now Statistics Sierra Leone) and other stakeholders: These include the following:

· Labour Force Survey (1988/89) · Survey on Household Expenditure and Household Economic Activities (1989/90) · Demographic and Social Monitoring Survey (1992) · National Nutrition Survey by the Ministry of Health and Sanitation with support from UNICEF (1989) · Multi Indicator Cluster Survey II with support from UNICEF (2000)

The National Population Commission (NPC) which was established in 1982 also used the 1985 Census data to adopt a “National Population Policy for Development Progress and Welfare” in August 1989. Even before the development of the National Population Policy, the first five-year Development Plan (1974 - 1979) had drawn attention to the “interactive role of population and development planning generally”.

Population data is therefore needed on a continuous basis for the formulation, implementation, monitoring and evaluation of the country's population programme and development planning. Thus, Population Censuses and Household Sample Surveys constituted the major sources of national population data in the country. The decennial Population Censuses and the Central Statistics Office/Statistics Sierra Leone inter-censual programme of household Surveys are therefore regarded as essential elements in the country's population programme and development plans.

The Population Censuses together with the related Enumeration Areas (EA) maps provide a sampling frame for the Household Sample Surveys. Therefore, the two data collection systems are considered complementary. Prior to the 2004 Census, the available EA maps were those prepared for the 1974 Census because it had been planned to update the EAs for the 1985 Census and some field work was initiated but due to time constraint and inadequate planning, the exercise could not be completed. Thus, the 1974 EA maps were used for the 1985 Census Enumeration and two or more enumerators assigned to each EA. This might have contributed to the suspected under-enumeration in the 1985 Census. Therefore, the cartographic exercise for the 2004 Census was thoroughly done.

The 2004 Population and Housing Census together with the cartographic work was, therefore, undertaken not only to maintain a regular census programme but also to provide a more accurate updated bench-mark population data as well as a geographic frame for inter-censual surveys and related statistical sample enquiries.

Please note that because of the difference in the methodology used in the censuses prior to 1963, the population totals of the country prior to 1963 can not be compared with the more recent censuses. The population totals for the period 1901 to 2004 were as follows:

· 1901 1,024,178 · 1911 1,400,132 · 1921 1,540,554 · 1931 1,768,480 · 1948 1,858,275 · 1963 2,180,355 · 1974 2,735,159 · 1985 3, 515,812 · 2004 4,976,871

THE OBJECTIVES OF THE 2004 POPULATION AND HOUSING CENSUS

The last census that was held in Sierra Leone was in 1985 and according to normal procedure, there should have been another census in 1995 but due the eleven-year war, it was not possible and the 1985 census data had become completely obsolete and needed to be updated. Therefore the main objectives of the 2004 population and Housing Census were: · To help ensure the availability of a time series of population data to support socio-economic development planning and population programmes as well as to contribute to the development of national capacity for statistical enquiries.

· Continue the regular pattern of the conduct of censuses in Sierra Leone as a continuing scheme of statistical data collection.

· Provide information on housing conditions in the country after the eleven-year war.

FUNDING AND IMPLEMENTATION STRATEGY

A census requires substantial financial, material and human resources. Because of the security situation in the country for the last eleven years, the Sierra Leone Government had to play a greater leadership role than it had in previous censuses as a confidence building measure so as to attract foreign Donors. The Sierra Leone Government demonstrated this by providing funding for the locality listing exercise, which started in January 2001. The Government of Sierra Leone also provided funds for the purchase of vehicles, office and data processing equipments needed for the start of cartographic field work.

Donors started coming on board after they were satisfied that the security situation had improved to the level that can justify their confidence. In the end, the project benefited from two major Donors, UNFPA whose support was mainly in the area of cartography, Geographic Information Systems (GIS) and capacity building at SSL and European Union who supported data collection, data processing, data analysis , evaluation and dissemination.
The objectives were expected to be realized through a number of interrelated activities, which were carried out in the

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

This statistic shows the total population of Nigeria from 2013 to 2023 by gender. In 2023, Nigeria's female population amounted to approximately 112.68 million, while the male population amounted to approximately 115.21 million inhabitants.

Nigeria (Kaduna & Lagos) Round 2 Service Delivery Point (SQ) survey used a three-stage cluster design, with urban-rural as strata in Kaduna. A sample of 60 clusters of enumeration areas (EAs) in Kaduna and 51 clusters enumeration areas (EAs) in the urban stratum in Lagos were drawn from the National Population Commission’s master sampling frame along with a list of EAs contiguous to the index EA. The EAs in Nigeria are usually small, with approximately 48 households on average. To create clusters with a minimum of 200 households, each index EA was listed and mapped. If there were fewer than 200 households identified, the next EA in the list of contiguous EAs was listed and mapped in its entirety. If the total number of households was still less than 200, the entirety of the third EA was listed and so on. Each cluster of EAs serves as the primary sampling unit from which 35 households (40 in Lagos) and up to 3 private health facilities were randomly selected. Public facilities were included if a selected EA fell within the catchment area. Private facilities were included if they fell within the boundaries of the EA. The final sample included 154 SDPs in Kaduna and 123 SDPs in Lagos. Data collection was conducted between August and September 2015. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42

Abstract

The 2013 Nigeria Demographic and Health Survey (NDHS) was designed to provide data to monitor the population and health situation in Nigeria with an explicit goal of providing reliable information about maternal and child health and family planning services. The primary objective of the 2013 NDHS was to provide up-to-date information on fertility levels, marriage, fertility preferences, awareness and use of family planning methods, child feeding practices, nutritional status of women and children, adult and childhood mortality, awareness and attitudes regarding HIV/AIDS, and domestic violence. This information is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving health and family planning services in the country.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample for the 2013 NDHS was nationally representative and covered the entire population residing in non-institutional dwelling units in the country. The survey used as a sampling frame the list of enumeration areas (EAs) prepared for the 2006 Population Census of the Federal Republic of Nigeria, provided by the National Population Commission. The sample was designed to provide population and health indicator estimates at the national, zonal, and state levels. The sample design allowed for specific indicators to be calculated for each of the six zones, 36 states, and the Federal Capital Territory, Abuja.

Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into localities. In addition to these administrative units, during the 2006 population census, each locality was subdivided into census enumeration areas. The primary sampling unit (PSU), referred to as a cluster in the 2013 NDHS, is defined on the basis of EAs from the 2006 EA census frame. The 2013 NDHS sample was selected using a stratified three-stage cluster design consisting of 904 clusters, 372 in urban areas and 532 in rural areas. A representative sample of 40,680 households was selected for the survey, with a minimum target of 943 completed interviews per state.

A complete listing of households and a mapping exercise were carried out for each cluster from December 2012 to January 2013, with the resulting lists of households serving as the sampling frame for the selection of households. All regular households were listed. The NPC listing enumerators were trained to use Global Positioning System (GPS) receivers to calculate the coordinates of the 2013 NDHS sample clusters.

A fixed sample take of 45 households were selected per cluster. All women age 15-49 who were either permanent residents of the households in the 2013 NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed. In a subsample of half of the households, all men age 15-49 who were either permanent residents of the households in the sample or visitors present in the households on the night before the survey were eligible to be interviewed. Also, a subsample of one eligible woman in each household was randomly selected to be asked additional questions regarding domestic violence.

For further details on sample size and design, see Appendix B of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2013 NDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire.

The Household Questionnaire was used to list all of the usual members of and visitors to the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, marital status, education, and relationship to the head of the household. Information on other characteristics of household members was collected as well, including current school attendance and survivorship of parents among those under age 18. If a child in the household had a parent who was sick for more than three consecutive months in the 12 months preceding the survey or a parent who had died, additional questions related to support for orphans and vulnerable children were asked. Furthermore, if an adult in the household was sick for more than three consecutive months in the 12 months preceding the survey or an adult in the household had died, questions were asked relating to support for sick people or people in households where a member had died.

The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of water; type of toilet facilities; materials used for the floor of the house; ownership of various durable goods; ownership of agricultural land; ownership of livestock, farm animals, or poultry; and ownership and use of mosquito nets and long-lasting insecticidal nets. The Household Questionnaire was further used to record height and weight measurements for children age 0-59 months and women age 15-49. In addition, data on the age and sex of household members in the Household Questionnaire were used to identify women and men who were eligible for individual interviews.

The Woman’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following main topics: • Background characteristics (age, religion, education, literacy, media exposure, etc.) • Reproductive history and childhood mortality • Knowledge, source, and use of family planning methods • Fertility preferences • Antenatal, delivery, and postnatal care • Breastfeeding and infant feeding practices • Child immunisation and childhood illnesses • Marriage and sexual activity • Women’s work and husbands’ background characteristics • Malaria prevention and treatment • Women’s decision making • Awareness of AIDS and other sexually transmitted infections • Maternal mortality • Domestic violence

The Man’s Questionnaire was administered to all men age 15-49 in every second household in the 2013 NDHS sample. The Man’s Questionnaire collected much of the same information found in the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

Cleaning operations

The processing of the 2013 NDHS data began simultaneously with the fieldwork. Completed questionnaires were edited in the field immediately by the field editors and checked by the supervisors before being dispatched to the data processing centre in Abuja. The questionnaires were then edited and entered by 26 data processing personnel specially trained for this task. Data were entered using the CSPro computer package, and all data were entered twice to allow 100 percent verification. The concurrent processing of the data offered a distinct advantage because of the assurance that the data were error free and authentic. Moreover, the double entry of data enabled easy comparisons and identification of errors and inconsistencies. Inconsistencies were resolved by tallying results with the paper questionnaire entries. Secondary editing of the data was completed in the last week of July 2013. The final cleaning of the data set was carried out by the ICF data processing specialist and completed in August.

Response rate

A total of 40,320 households were selected from 896 sample points, of which 38,904 were found to be occupied at the time of the fieldwork. Of the occupied households, 38,522 were successfully interviewed, yielding a household response rate of 99 percent. In view of the security challenges in the country, this response rate is highly encouraging and appears to be the result of a well-coordinated team effort.

In the interviewed households, a total of 39,902 women age 15-49 were identified as eligible for individual interviews, and 98 percent of them were successfully interviewed. Among men, 18,229 were identified as eligible for interviews, and 95 percent were successfully interviewed. As expected, response rates were slightly lower in urban areas than in rural areas.

Note: See summarized response rates by residence (urban/rural) in Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2013 Nigeria DHS (NDHS) to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2013 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error

This data set has been adapted from UNOCHA Humanitarian Needs Overview (HNO) 2017 data for Nigeria. These indicators were prepared for the purpose of providing data amidst a growing humanitarian crisis across Africa and in Yemen. These indicators include population for all Nigerian states. It includes total and targeted numbers by cluster for the states of Adamawa, Borno, and Yobe for food security, camp coordination and camp management (CCCM), education, health, nutrition, protection, gender-based violence, shelter, and water, sanitation and hygiene (WASH).Featured in Nigeria in Crisis.Fields:country_pcod: Country PCode, two character ISO country code "SS" for South Sudan.district_id: State ID, numeral-only two digit identifier per state (admin 1). Used for constructing P-Code.district_pcod: State PCode, alphanumeric identifier to uniquely identify each state.country: Country Name, "Nigeria."district: State Name, Nigerian name for each state.totpop16: Total Population 2016, 2006 Census conducted by National Population Commission of Nigeria, provided via UNOCHA.idppop: IDP Population, population of internally displaced people. IOM, February 2017.fs_tot: Food Security Total, total people estimated food insecure. UNOCHA, December 2016.fs_targ: Food Security Targeted, total people targeted for food assistance. UNOCHA, December 2016.nut_tot: Nutrition Total, total people estimated in need of nutrition assistance. UNOCHA, December 2016.nut_targ: Nutrition Targeted, total people targeted for nutrition assistance. UNOCHA, December 2016.hlth_tot: Health Total, total people estimated in need of health assistance. UNOCHA, December 2016.hlth_targ: Health Targeted, total people targeted for health assistance. UNOCHA, December 2016.prot_tot: protection Total, total people estimated in need of protection assistance. UNOCHA, December 2016.prot_targ: protection Targeted, total people targeted for protection assistance. UNOCHA, December 2016.gbv_tot: Nutrition Total, total people estimated in need of gender-based violence assistance. UNOCHA, December 2016.gbv_targ: Nutrition Targeted, total people targeted for gender-based violence assistance. UNOCHA, December 2016.wash_tot: Nutrition Total, total people estimated in need of water, sanitation, and hygiene assistance. UNOCHA, December 2016.wash_targ: Nutrition Targeted, total people targeted for water, sanitation, and hygiene assistance. UNOCHA, December 2016.ed_tot: Education Total, total people estimated in need of education assistance. UNOCHA, December 2016.ed_targ: Education Targeted, total people targeted for education assistance. UNOCHA, December 2016.sltr_tot: Shelter Total, total people estimated in need of shelter assistance. UNOCHA, December 2016.sltr_targ: Shelter Targeted, total people targeted for shelter assistance. UNOCHA, December 2016.cccm_tot: CCCM Total, total people estimated in need of camp coordination and camp management assistance. UNOCHA, December 2016.cccm_targ: CCCM Targeted, total people targeted for camp coordination and camp management assistance. UNOCHA, December 2016.

IntroductionSub-Saharan Africa (SSA) is plagued by myriads of diseases, mostly infectious; but cancer disease burden is rising among non-communicable diseases. Nigeria has a high burden of cancer, however its remote underserved culturally-conserved populations have been understudied, a gap this study sought to fill.MethodsThis was a cross-sectional multi-institutional descriptive study of histologically diagnosed cancers over a four-year period (January 2019-December 2022) archived in the Departments of Pathology and Cancer Registries of six tertiary hospitals in the northeast of Nigeria. Data obtained included age at diagnosis, gender, tumor site and available cancer care infrastructure. Population data of the study region and its demographics was obtained from the National Population Commission and used to calculate incident rates for the population studied.ResultsA total of 4,681 incident cancer cases from 2,770 females and 1,911 males were identified. The median age at diagnosis for females was 45 years (range 1–95yrs), and 56 years (range 1–99yrs) for males. Observed age-specific incidence rates (ASR) increased steadily for both genders reaching peaks in the age group 80 years and above with the highest ASR seen among males (321/100,000 persons) compared to females (215.5/100,000 persons). Breast, cervical, prostatic, colorectal and skin cancers were the five most common incident cancers. In females, breast, cervical, skin, ovarian and colorectal cancers were the top five malignancies; while prostate, haematolymphoid, skin, colorectal and urinary bladder cancers predominated in men.ConclusionRemote SSA communities are witnessing rising cancer disease burden. Proactive control programs inclusive of advocacy, vaccination, screening, and improved diagnostics are needed.

Abstract

The Food and Agriculture Organization of the United Nations (FAO) has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). FAO launched a sixth-round survey in Nigeria from 21 January to 5 February 2024. The survey was conducted face-to-face and reached 3 441 households. Data collection took place at the beginning of the lean season across five states: Adamawa, Borno, Katsina, Yobe and Zamfara. Data were collected at the level of local government areas (administrative level 2) in Zamfara and at state level (administrative level 1) in Adamawa, Borno, Katsina and Yobe.

For more information, see https://data-in-emergencies.fao.org/pages/monitoring.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Data were collected at the level of local government areas (administrative level 2) in Zamfara and at state level (administrative level 1) in Adamawa, Borno, Katsina and Yobe. A Face-to-Face (F2F) survey was conducted. The sampling frame was generated per state by the National Population Commission, as for the second round. The sample targeted 700 households randomly selected from at least 25 randomly selected EAs per each Admin 1 strata (Adamawa, Borno, Katsina and Yobe). In each EA, 7 households were randomly selected and interviewed. In the case of Admin 2 stratum (Zamfara, 14 local government areas), 175 surveys were targeted by interviewing 25 households from 7 EAs per stratum yielding a total of 2 450 surveys. The combined number of surveys was 3 150 households.

Surveys are designed based on country-specific needs, objectives, and constraints. They aim to achieve a 10 percent margin of error, a 95 percent confidence level, administrative-level granularity, and sufficient sample sizes for key target populations, including agricultural households.

Mode of data collection

Face-to-face [f2f]

Research instrument

A link to the questionnaire has been provided in the downloads tab.

Cleaning operations

The datasets have been edited and processed for analysis by the Food and Agriculture Organization of the United Nations, Data in Emergencies (DIEM) Hub at the Office of Emergencies and Resilience, with some dashboards and visualizations produced. For more information, see https://data-in-emergencies.fao.org/pages/countries.

STATISTICAL DISCLOSURE CONTROL (SDC) The dataset was anonymized using Statistical Disclosure methods by the Food and Agriculture Organization of the United Nations, Data in Emergencies (DIEM) Hub and reviewed by the Statistics Division. All direct identifiers have been removed prior to data submission.

Abstract

NASS is an exercise designed to provide accurate and up-to-date agricultural statistics that allows policymakers, researchers, and development partners to make informed decisions that directly impact the well-being of farmers, rural communities, and the broader economy. These statistics are essential for enhancing food security, improving productivity, and addressing regional disparities in agricultural performance. Additionally, robust agricultural data is vital in supporting Nigeria’s efforts to diversify its economy from oil dependency. By identifying key areas for investment, such as crop production, livestock management, and agro-processing, data can guide both public and private sector investments to boost agricultural output and expand exports. Moreover, they help track progress toward national goals while supporting Nigeria's efforts to meet global commitments like the Sustainable Development Goals (SDGs). Hence, NASS provides useful data for understanding the state of the agricultural sector and offer essential production and structural data to support evidence-based planning and implementation of agricultural programs vital for addressing current economic challenges and enhancing the livelihood of many Nigerians. This survey is also essential for monitoring and evaluating the effectiveness of existing agricultural programs and ensuring that resources are allocated efficiently. Capturing detailed data on agriculture practices, outputs, and challenges, the survey supports the planning and implementation of initiatives aimed at improving productivity, enhancing food security, and adapting to challenges like climate change and market fluctuations.

The objectives of the survey are to; i. provide data on agricultural production in 2022/ 2023 and the structure of the sector as a whole to assist the government in policy formulation and programme planning; ii. effectively and efficiently provide appropriate agricultural information to increase public awareness; and iii. provide data that could be used to compute agricultural sector contribution to the Gross Domestic Product (GDP).

Geographic coverage

The National Population Commission (NPC) provided the frame of Enumeration Areas (EAs), newly demarcated for the proposed 2023 Housing and Population Census. This was used as the primary sampling frame. Although data was collected across the 36 states and the Federal Capital Territory (FCT), some Local Government Areas (LGAs) were not covered due to insecurity. The LGAs covered during the survey were seven hundred and sixty-seven (767) out of the 774 LGAs in Nigeria due to security challenges. The affected states/LGAs are Borno state (Monguno, Kukawa and Abadam LGAs) and Orlu, Orsu, Oru East, and Njaba LGAs in Imo state. The number of EAs covered varied from state to state depending on the number of Agricultural EAs and LGAs. Nationally, a total of 15,591 EAs were selected across the 36 States of the Federation and FCT and a total of 152,485 households were designated to be covered.

Analysis unit

Agricultural Households.

Universe

The final sampling units used were agricultural households involved in crop/ livestock farming, and fishery households selected in a subsample of EAs among the sample of EAs covered during the extensive listing survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The final sampling units used were agricultural households involved in crop/ livestock farming, and fishery households selected in a subsample of EAs among the sample of EAs covered during the extensive listing survey. The sampling method of NASS-household is a stratified three-phased sampling as follows: -First phase: Stratified Probability Proportional to Size (PPS) selection of 80 EAs Second phase: systematic sub-sampling of 40 EAs for the extended listing Third phase: two-stage sampling for NASS-household

i. First stage: Stratification of EAs into Agricultural and non-agricultural EAs drawn from the 40EAs listed in each LGA ii. Second stage: Systematic sampling of 10 farming households (crop/ livestock farming) and a systematic selection of complementary households practicing only fishery in fishery-intensive LGAs (18) up to a maximum of 12 households were interviewed in the concerned EAs. That selection was stratified by sorting the listed farming households by various agricultural-related information including farming activities practiced, number of plots, livestock numbers in tropical livestock units, as well as the gender of the household head.

Sample Size and Reallocation A total of 15,591 Enumeration Areas (EAs) were selected for the NASS household survey. The sample was distributed across Local Government Areas (LGAs) based on the estimated total number of plots per LGA. Within each LGA, the sample was further allocated between urban and rural areas in proportion to the estimated agricultural population. In the selected EAs, 152,485 households were finally sampled.

Sampling deviation

The probabilities of selecting EAs for NASS households were derived from two stages: the likelihood of their selection in the listing sample and the probability of selection from the subsample of EAs chosen for NASS households. These probabilities were then combined with the probabilities of selecting farming households within the EAs to determine the final selection probabilities for farming households. The design weights were calculated as the inverse of these selection probabilities. These weights were further adjusted to account for non-responses, resulting in final sampling weights used in estimating means, totals, proportions, and other statistics through standard Horvitz-Thompson estimators. Special consideration was given to fishery-related estimates, ensuring that data from the independent sample of households engaged solely in fishery activities were fully incorporated. Due to the complexity of the sampling design, sampling errors were estimated using resampling methods such as Bootstrap and Jackknife techniques.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NASS household questionnaire served as a meticulously designed instrument administered within selected households to gather comprehensive data. The questionnaire was structured into the following sections:

0A. HOLDING IDENTIFICATION 0B. ROSTER OF HOUSEHOLD MEMBERS 0C. AGRICULTURAL ACTIVITIES 0D. AGRICULTURALACTIVITIES 2. PLOT ROSTER AND DETAILS 3. CROP ROSTER 1A: TEMPORARY (NON-VEGETABLE) CROP PRODUCTION 1H: TEMPORARY CROP PRODUCTION (VEGETABLE CROPS) 1B: TEMPORARY CROP DESTINATION 2A: PERMANENT CROP PRODUCTION 2B: PERMANENT CROP DESTINATION 4: SEED AND PLANT USE 3C: INPUT USE 2(DS): PLOT ROSTER AND DETAILS 3(DS): CROP ROSTER 1A(DS): TEMPORARY (NON-VEGETABLE) CROP PRODUCTION - DRY SEASON 1H(DS): TEMPORARY CROP PRODUCTION (VEGETABLE CROPS) - DRY SEASON 1B(DS): TEMPORARY CROP DESTINATION - DRY SEASON 4(DS): SEED AND PLANT USE - DRY SEASON 3C(DS): INPUT USE - DRY SEASON 4A: LIVESTOCK IN STOCK 4B: CHANGE IN STOCK- LARGE AND MEDIUM-SIZED ANIMALS 4C: CHANGE IN STOCK-POULTRY 4G: MILKPRODUCTION 4H: EGG PRODUCTION 4I: OTHERLIVESTOCKPRODUCTS 4J:APIARYPRODUCTION (BEEKEEPING) 5A: FISH FARMING/AQUACULTUREPRODUCTION 6A: FISH HUNTING/CAPTURE 7A: FORESTRYPRODUCTION 9: LABOUR 2_GPS.PLOT GPS MEASUREMENT 99. END OFTHE SURVEY

Cleaning operations

Data processing and analysis involved data cleaning, data analysis, data verification/validation, and table generation. World Food Programme (WFP), Food and Agricultural Organization (FAO), and NBS carried out the data processing and analysis for both the household and corporate farms questionnaires. The corporate farm questionnaire involved manual editing as well as data entry.

Sampling error estimates

Given the complexity of the sample design, sampling errors were estimated through resampling approaches (Bootstrap/Jackknife)

Nigeria (Oyo) Round 1 Service Delivery Point (SQ) survey used a two-stage cluster design with urban-rural as strata. A sample of 80 enumeration areas (EAs) was drawn from the National Population Commission’s master sampling frame. In each EA private health facilities were listed and mapped. The final sample included 221 health, of which 103 are public. Data collection was conducted between November and December 2017. The Oyo state was not included in the national estimates since the Oyo data collection was done separately and its sampling method differed from those of other states. Thus, data for Oyo state is released separately in a different dataset. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/vfbp-bz42

Nigeria (National) Round 5 Household and Female (HQFQ) survey used a three-stage sampling approach within a sample of seven states - Anambra, Kaduna, Kano, Lagos, Nasarawa, Rivers, Taraba. One state per zone was selected using probability proportional to size from among each of Nigeria’s six zones. The seventh state (Kaduna) was allocated to the northwest zone. A total of 302 clusters of enumeration areas (EAs) were drawn from the National Population Commission’s master sampling frame. In each cluster of EAs, households and private health facilities were listed and mapped; 35 households (40 in Lagos) were selected per cluster of EAs. Occupants in selected households were enumerated and eligible females of reproductive age (15-49) were contacted and consented for interview. The final sample included 10,070 households and 11,284 females. Data collection was conducted between April and May 2018 in all states. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/b88s-zx32

This dataset presents statistics for Wholesale Trade: Sales and Commissions of Electronic Markets, Agents, Brokers, and Commission Merchants for the U.S.