Estimate population figures at local government administrative level and different age groups

Historical dataset of population level and growth rate for the Kano, Nigeria metro area from 1950 to 2025.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

18 of the 37 states in Nigeria were selected using procedures described in the methodology report

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sampling Frame The sampling frame was the 2006 National Population Census. For administrative purposes, Nigeria has 36 states and the Federal Capital Territory. These states are grouped into six geopolitical zones - the North Central, North East, North West, South East, South South and South West. The states in turn are divided into 776 Local Governments. The demographic and political characteristics of the states vary considerably. For example, the number of component local government areas in the states ranges from 8 in Bayelsa State (in the South South) to 44 in Kano State (in the North West). Likewise state populations vary widely from 1.41 million in the Abuja Federal Capital Territory to 9.38 million in Kano State. The National Bureau of Statistics splits the country further into 23, 070 enumeration areas (EAs). While the enumeration areas are equally distributed across the local government areas, with each local government area having 30 enumeration areas, the differences in the number of local government areas across states implies that there are also huge differences in the number of enumeration areas across states. Appendix table 1 summarizes the population according to the 2006 population census (in absolute and proportionate numbers), number of local government areas, and number of enumeration areas in each state .

Given the above, a stratified random sampling technique was thought to be needed to select areas according to population and the expected prevalence of migrants. The National Bureau of Statistics (NBS) provided a randomly selected set of enumeration areas and households spread across all states in the Federation from the 2006 sampling frame. Every state in Nigeria has three senatorial zones (often referred to as North, Central and South or East, Central and West). The NBS sample enumeration areas were distributed such that within each state, local government areas from each senatorial zones were included in the sample, with Local Governments in each state nearly evenly distributed between rural and urban areas. In all, a total of 3188 enumeration areas were selected. These enumeration areas were unevenly spread across States; some states in the North West (Kano, Katsina, and Jigawa), and a few in the South South (Akwa Ibom and Delta) had over 100 enumeration areas selected while others such as Imo and Abia in the South East, and Borno, Gombe and Taraba in the North East, had as few as 20 enumeration areas selected. This selection partially reflected the relative population distribution and number of Local Government Areas in the component states. Annex Table B shows details of the states and geopolitical regions, their shares in population of the country, the number of Local Government Areas and enumeration areas in each state and the number of enumeration areas given in the NBS list that formed the frame for the study.

B. The Sample for the Migration Survey

a. Sample Selection of States, Local Governments and Enumeration Areas Originally, the intention was to have proportionate allocation across all states, using the population of each state in the 2006 Census to select the number of households to be included in the sample. But it was later recognized that this would not yield enough migrant households, particularly those with international migrants, especially as the total number of households that could likely be covered in the sample to was limited to 2000. Consequently, a disproportionate sampling approach was adopted, with the aim of oversampling areas of the country with more migrants. According to Bilsborrow (2006), this approach becomes necessary because migrants are rare populations for which a distinct disproportionate sampling procedure is needed to ensure they are adequately captured. Given the relative rareness of households with out-migrants to international destinations within the 10 year reference period (selected by the World Bank for all countries) prior to the planned survey, sampling methods appropriate for sampling rare elements were desirable, specifically, stratified sampling with two-phase sampling at the last stage.

Establishing the strata would require that there be previous work, say from the most recent Census, to determine migration incidence among the states. However, the needed census data could not be obtained from either the National Bureau of Statistics or the National Population Commission. Therefore, the stratification procedure had to rely on available literature, particularly Hernandez-Coss and Bun (2007), Agu (2009) and a few other recent, smaller studies on migration and remittances in Nigeria. Information from this literature was supplemented by expert judgement about migration from team members who had worked on economic surveys in Nigeria in the past. Information from the literature and the expert assessment indicated that migration from households is considerably higher in the South than in the North. Following this understanding, the states were formed into two strata- those with high and those with low incidence of migration. In all, 18 States (16 in the South and 2 in the North) were put into the high migration incidence stratum while 19 states (18 in the North and 1 in the South) were classified l into the low migration incidence stratum (column C of Appendix Table 1).

The Aggregate population of the 18 states in the high migration incidence stratum was 67.04 million, spread across 10,850 Enumeration areas. Thus, the mean population of an EA in the high migration stratum was 6179. In turn, the aggregate population of the 19 states in the low migration incidence stratum was 72.95 million spread across 12,110 EAs yielding a mean EA population of 6024. These numbers were close enough to assume the mean population of EAs was essentially the same. To oversample states in the high stratum, it was decided to select twice as high a proportion of the states as in the low stratum. To further concentrate the sample and make field work more efficient in being oriented to EAs more likely to have international migrants, we decided to select randomly twice as many LGAs in each state in the high stratum states as in the low stratum states.

Thus, 12 states were randomly selected with probabilities of selection proportionate to the population size of each state (so states with larger populations were accordingly more likely to fall in the sample) from the high stratum states. Then two LGAs were randomly selected from each sample state and 2 EAs per sample LGA (one urban, one rural) to yield a total of 12 x 2 x 2 or 48 EAs in the high stratum states. For the low stratum, 6 states were randomly selected. From each of these, 1 LGA was randomly picked and 2 EAs were selected per sample LGA to give a total of 6 x 1 x 2 or 12 EAs in the low stratum. This yielded a total of 60 EAs for both strata. Given the expected range of 2000 households to be sampled, approximately 67 households were to be sampled from each local government area or 34 households from each enumeration area.

So far, the discussion has assumed two groups of households - migrant and non-migrant households. However, the study was interested in not just lumping all migrants together, but rather in classifying migrants according to whether their destination was within or outside the country. Migrant households were thus subdivided into those with former household members who were international migrants and those with former household members who were internal migrants. Three strata of households were therefore required, namely:

1. Households with an international migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live in an international destination and has remained abroad;
2. Households with an internal migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live elsewhere in Nigeria (outside the sample LGA) and has not returned to the LGA; and
3. Households with no migrant: No member of the household has left to live elsewhere either within or outside the country since Jan. 1, 2000.

The selection of states to be included in the sample from both strata was based on Probabilities of Selection Proportional to (Estimated) Size or PPES. The population in each stratum was cumulated and systematic sampling was performed, with an interval of 12.16 million for the low stratum (72.95 million divided by 6 States), and 5.59 million for the high stratum (67.04 million divided by 12 States). This yields approximately double the rate of sampling in the high migration stratum, as earlier explained. Using a random start between 0 and 12.16, the following states were sampled in the low stratum: Niger, Bauchi, Yobe, Kano, Katsina, and Zamfara. In the high stratum, states sampled were Abia, Ebonyi, Imo, Akwa Ibom, Delta, Edo, Rivers, Lagos, Ondo, Osun and Oyo. Given its large population size, Lagos fell into the sample twice. The final sample, with LGAs and EAs moving from North to South (i.e. from the low to the high stratum states) is presented in Table 1 below.

The sample was concentrated in the South since that is where it was expected that more households have international migrants. It was expected that the survey would still also be reasonably representative of the whole country and of both internal migrant and non-migrant households through weighting the data. To this effect, field teams were asked to keep careful track at all stages of the numbers of people and households listed compared to the number in the

Nigeria is the African country with the largest population, counting over 230 million people. As of 2024, the largest city in Nigeria was Lagos, which is also the largest city in sub-Saharan Africa in terms of population size. The city counts more than nine million inhabitants, whereas Kano, the second most populous city, registers around 3.6 million inhabitants. Lagos is the main financial, cultural, and educational center in the country. Where Africa’s urban population is booming The metropolitan area of Lagos is also among the largest urban agglomerations in the world. Besides Lagos, another most populated citiy in Africa is Cairo, in Egypt. However, Africa’s urban population is booming in other relatively smaller cities. For instance, the population of Bujumbura, in Burundi, could grow by 123 percent between 2020 and 2035, making it the fastest growing city in Africa and likely in the world. Similarly, Zinder, in Niger, could reach over one million inhabitants by 2035, the second fastest growing city. Demographic urban shift More than half of the world’s population lives in urban areas. In the next decades, this will increase, especially in Africa and Asia. In 2020, over 80 percent of the population in Northern America was living in urban areas, the highest share in the world. In Africa, the degree of urbanization was about 40 percent, the lowest among all continents. Meeting the needs of a fast-growing population can be a challenge, especially in low-income countries. Therefore, there will be a growing necessity to implement policies to sustainably improve people’s lives in rural and urban areas.

Populated place − place or area with clustered or scattered buildings and a permanent human population (city, settlement, town, village) and by definition has no legal boundaries

Comprehensive population and demographic data for Kano Village

Populated place − place or area with clustered or scattered buildings and a permanent human population (city, settlement, town, village) and by definition has no legal boundaries

Populated place − place or area with clustered or scattered buildings and a permanent human population (city, settlement, town, village) and by definition has no legal boundaries

PMA Nigeria (Kano) COVID-19 Female Follow-up (FQFU) phone survey (June 2020) was conducted among females age 15-49 at the time of the COVID-19 Survey who were interviewed at the baseline survey between December 2019 and January 2020, consented to follow-up, and own or had access to a phone (33.6% of the baseline population). Of the 429 eligible respondents, 8.2% were not reached. Of those reached, 98.7% completed the survey for a response rate of 90.7% among contacted women.PMA Nigeria (Lagos) COVID-19 Female Follow-up (FQFU) phone survey was conducted in July 2020 among females age 15-49 at the time of the COVID-19 survey who were interviewed at the baseline survey between December 2019 and January 2020, consented to follow-up, and own or had access to a phone (82.6% of the baseline population). Of the 1174 eligible respondents, 15.6% were not reached. Of those reached, 96.6% completed the survey for a response rate of 81.5% among contacted women

LGAs with the type of hotspots based on mean annual incidence rate and proportion of years with reported cholera cases.

PMA Nigeria (Kano & Lagos) Phase 3 Service Delivery Point Survey includes 25 enumeration areas (EAs) in Kano and 52 EAs in Lagos. The EAs were drawn using the same stratified cluster design with urban-rural strata from the National Population Commission’s master sampling frame. The results are representative at the state level. The final sample included 64 facilities in Kano and 126 facilities in Lagos which completed the interview. Data collection was conducted between December 2021 and February 2022. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/f0vf-qd73

Populated place − place or area with clustered or scattered buildings and a permanent human population (city, settlement, town, village) and by definition has no legal boundaries

Comprehensive population and demographic data for Kano (492) Village

Cholera clusters.

PMA Nigeria (Kano & Lagos) Phase 4 Client Exit Interview Survey includes 25 enumeration areas (EAs) in Kano and 52 EAs in Lagos. The EAs were drawn using the same stratified cluster design with urban-rural strata from the National Population Commission’s master sampling frame. The results are representative at the state level. The final sample included 780 clients in Kano and 624 clients in Lagos which completed the interview. Data collection was conducted between December 2023 and January 2024. More information about this dataset can be found in the corresponding codebook, accessible at https://doi.org/10.34976/1hrp-p268

Monthly collections of mosquito species in the three urban L.G.A.s of Kano.

Abstract

The 2021 Nigeria Malaria Indicator Survey (NMIS) was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and National Bureau of Statistics (NBS).

The primary objective of the 2021 NMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the NMIS collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria in pregnant women, exposure to messages on malaria, care-seeking behaviour, treatment of fever in children, and social and behaviour change communication (SBCC). Children age 6–59 months were also tested for anaemia and malaria infection. The information collected through the NMIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2021 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones, which include 36 states and the Federal Capital Territory (FCT). Nigeria’s geopolitical zones are as follows: • North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT • North East: Adamawa, Bauchi, 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 Rivers • South West: Ekiti, Lagos, Ogun, Osun, Ondo, and Oyo

The 2021 NMIS used the sample frame for the proposed 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), each LGA is divided into wards, and each ward is divided into localities. Localities are further subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster unit for the 2021 NMIS, was defined on the basis of EAs for the proposed 2023 PHC.

A two-stage sampling strategy was adopted for the 2021 NMIS. In the first stage, 568 EAs were selected with probability proportional to the EA size. The EA size is the number of households residing in the EA. The sample selection was done in such a way that it was representative of each state. The result was a total of 568 clusters throughout the country, 195 in urban areas and 373 in rural areas.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2021 NMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. After the questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo.

Cleaning operations

The processing of the 2021 NMIS data began immediately after the start of fieldwork. As data collection was being completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. Data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. 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. 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 also allowed for effective monitoring. Secondary editing of the data was completed in February 2022. The data processing team coordinated this exercise at the central office.

Response rate

A total of 14,185 households were selected for the survey, of which 13,887 were occupied and 13,727 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 14,647 women age 15-49 were identified for individual interviews. Interviews were completed with 14,476 women, 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 in 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, or incorrect data entry. Although numerous efforts were made during the implementation of the 2021 Nigeria Malaria Indicator Survey (NMIS) 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 2021 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, 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, and so on), 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 2021 NMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Sampling errors tables are presented in Appendix B of the final report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month of fieldwork and zone
• Positive rapid diagnostic test (RDT) results by month of fieldwork and zone, Nigeria MIS 2021
• Concordance and discordance between RDT and microscopy results
• Concordance and discordance between national and external quality control laboratories

See details of the data quality tables in Appendix C of the final report.

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.

ICT results for three urban LGAs of Kano state.

Characteristics of study population and univariate analysis of mortality risk factors of COVID-19 patients (N = 195) at the Kwanar Dawaki isolation center, Kano, Nigeria.