Chart and table of population level and growth rate for the Nairobi, Kenya metro area from 1950 to 2025.

As of 2043, Nairobi was the most populated city in Kenya, with more than 2.7 million people living in the capital. The city is also the only one in the country with a population exceeding one million. For instance, Mombasa, the second most populated, has nearly 800 thousand inhabitants. As of 2020, Kenya's population was estimated at over 53.7 million people.

While the East African region, including Kenya, is one of first regions believed to have modern humans inhabit it, population growth in the region remained slow to non-existent throughout the 19th century; in the past hundred years, however, Kenya’s population has seen an exponential increase in size, going from 2.65 million in 1920, to an estimated 53.77 million in 2020.

Along with this population growth, Kenya has seen rapid urbanization and industrialization, particularly in recent decades. The metropolitan area of Kenya’s capital, Nairobi, with an estimated population of 9.35 million in 2020, now contains on its own over three and a half times the population of the entire country just a century earlier.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Households: Yes

Universe

All persons who were in Kenya at midnight on Census Night.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Constructed by census agency.

SAMPLE DESIGN: Unknown sample design includes oversample of Nairobi. Data are weighted by age and district of residence.

SAMPLE FRACTION: 6%

SAMPLE UNIVERSE: Unknown.

SAMPLE SIZE (person records): 659,310

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on individuals.

This statistic shows the total population of Kenya from 2013 to 2023 by gender. In 2023, Kenya's female population amounted to approximately 27.82 million, while the male population amounted to approximately 27.52 million inhabitants.

Comprehensive socio-economic dataset for Kenya including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

Abstract

The places we live affect our health status and the choices and opportunities we have (or do not have) to lead fulfilling lives. Over the past ten years, the African Population & Health Research Centre (APHRC) has led pioneering work in highlighting some of the major health and livelihood challenges associated with rapid urbanization in sub-Saharan Africa (SSA). In 2002, the Centre established the first longitudinal platform in urban Africa in the city of Nairobi in Kenya. The platform known as the Nairobi Urban Health and Demographic Surveillance System collects data on two informal settlements - Korogocho and Viwandani - in Nairobi City every four months on issues ranging from household dynamics to fertility and mortality, migration and livelihood as well as on causes of death, using a verbal autopsy technique. The dataset provided here contains key demographic and health indicators extracted from the longitudinal database. Researchers interested in accessing the micro-data can look at our data access policy and contact us.

Geographic coverage

The Demographic Surveillance Area (combining Viwandani and Korogocho slum settlements) covers a land area of about 0.97 km2, with the two informal settlements located about 7 km from each other. Korogocho is located 12 km from the Nairobi city center; in Kasarani division (now Kasarani district), while Viwandani is about 7 km from Nairobi city center in Makadara division (now Madaraka district). The DSA covers about seven villages each in Korogocho and Viwandani.

Analysis unit

Individual

Universe

Between 1st January and 31st December,2015 the Nairobi HDSS covered 86,304 individualis living in 30,219 households distributed across two informal settlements(Korogocho and Viwandani) were observed. All persons who sleep in the household prior to the day of the survey are included in the survey, while non-resident household members are excluded from the survey.

The present universe started out through an initial census carried out on 1st August,2002 of the population living in the two Informal settlements (Korogocho and Viwandani). Regular visits have since then been made (3 times a year) to update information on births, deaths and migration that have occurred in the households observed at the initial census. New members join the population through a birth to a registered member, or an in-migration, while existing members leave through a death or out-migration. The DSS adopts the concept of an open cohort that allows new members to join and regular members to leave and return to the system.

Kind of data

Event history data

Frequency of data collection

Three rounds in a year

Sampling procedure

This dataset is related to the whole demographic surveillance area population. The number of respondents has varied over the last 13 years (2002-2015), with variations being observed at both household level and at Individual level. As at 31st December 2015, 66,848 were being observed under the Nairobi HDSS living in 25,812 households distributed across two informal settlements(Korogocho and Viwandani). The variable IndividualId uniquely identifies every respondent observed while the variable LocationId uniquely identifies the room in which the individual was living at any point in time. To identify individuals who were living together at any one point in time (a household) the data can be split on location and observation dates.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Questionnaires are printed and administered in Swahili, the country's national language.

The questionnaires for the Nairobi HDSS were structured questionnaires based on the INDEPTH Model Questionnaire and were translated into Swahili with some modifications and additions.After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation from the Swahili version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators. The English and Swahili questionnaires were both piloted as part of the survey pretest.

At baseline, a household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. In later rounds questionnaires to track the migration of the population observed at baseline, and additonal questionnaires to capture demographic and health events happening to the population have been introduced.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of STATA data files

Where changes were made by the program, a cold deck imputation is preferred; where incorrect values were imputed using existing data from another dataset. If cold deck imputation was found to be insufficient, hot deck imputation was used, In this case, a missing value was imputed from a randomly selected similar record in the same dataset.

Some corrections are made automatically by the program(80%) and the rest by visual control of the questionnaires (20%).

1. 100% forms filled in by FRAs are rechecked for completeness, ensured that all the necessary event forms are filled in.
2. Spot checks are done on field over data collection by FRAs for reliability of data.
3. FRS instructs revisits wherever required.
4. Forms are checked on sample basis
5. Checks if all the necessary event forms are filled in.
6. Forms with inconsistencies identified at the time of entry are sent back to the field.
7. Creating and managing data entry checks for picking up inconsistencies
8. Monitoring field work: balancing work target and quality.
9. Dealing with data inconsistencies at data level and giving feedbacks to field staff.
10. Conducting training and refresher training wherever required.
11. Data cleaning

Response rate

Over the years the response rate at household level has varied between 95% and 97% with response rate at Individual Level varying between 92% and 95%. Challenges to acheiving a 100% response rate have included: - high population mobility within the study area - high population attrition - respondent fatigue - security in some areas

Sampling error estimates

Not applicable for surveillance data

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate KE031 MicroDataCleaned Starts 219285 2017-05-16 18:25
KE031 MicroDataCleaned Transitions 825036 825036 0 2017-05-16 18:25
KE031 MicroDataCleaned Ends 219285 2017-05-16 18:25
KE031 MicroDataCleaned SexValues 825036 2017-05-16 18:25
KE031 MicroDataCleaned DoBValues 42 824994 825036 0 2017-05-16 18:25

Abstract

The World Bank in collaboration with the Joint Data Center on Forced Displacement, Kenya National Bureau of Statistics (KNBS) and the United Nations High Commissioner for Refugees (UNHCR) conducted a cross-sectional survey on refugee and host populations living in Nairobi. The survey was based on the Kenya Continuous Household Survey (KCHS) and targets both host populations and refugees living in Nairobi. Through a participatory training format, enumerators learned how to collect quality data specific for refugees as well as nationals. Daily data quality monitoring dashboards were produced during the data collection periods to provide feedback to the field team and correct possible errors. The data was collected with CAPI technique through the World Bank developed Survey Solutions software; this ensured high standards of data storage, protection and pre-processing.

The sample is representative of refugees and other residents living in Nairobi. The refugee sample was drawn from UNHCR’s database of refugees and asylum seekers (proGres) using implicit stratification by sub-county and country of origin. The host community sampling frame was drawn using a two-stage cluster design. In the first stage, eligible enumeration areas (EAs) based on the 2019 Population and Housing Census were selected. In the second stage 12 households were sampled from each EA. The survey differentiates between two types of host communities: ‘core’ host communities were drawn from EAs located within the three areas with the largest number of refugee families: Kasarani, Eastleigh North and Kayole. At least 10 percent of the Nairobi refugee families reside in each of these areas. ‘Wider’ host communities cover the rest of the Nairobi population and were drawn from EAs which do not cover the three areas in which many refugees live.

For a subset of households, a women empowerment module was administered by a trained female enumerator to one randomly selected woman in each household aged 15 to 49.

The data set contains two files. hh.dta contains household level information. The ‘hhid’ variable uniquely identifies all households. hhm.dta contains data at the level of the individual for all household members. Each household member is uniquely identified by the variable ‘hhm_id’.

This cross-sectional survey was conducted between May 22 to July 27, 2021. It comprises a sample of 4,853 households in total, 2,420 of which are refugees and 2,433 are hosts.

Geographic coverage

Nairobi county, Kenya

Analysis unit

Household, Individual

Sampling procedure

The survey has two primary samples contained in the ‘sample’ variable: the refugee sample and the host community sample. The refugee sample used the UNHCR database of refugees and asylum seekers in Kenya (proGres) as the sampling frame. ProGres holds information on all registered refugees and asylum seekers in Kenya including their contact information and data on nationality and approximate location of living. We considered only refugees living in Nairobi and implicitly stratified by nationality and location. In total, the sample comprises 2,420 refugee families.

The host community sample differentiates between two types of communities. We consider ‘core’ host communities as residents who live in Eastleigh North, Kayole or Kasarani – at least 10 percent of the Nairobi refugee families reside in each of these areas. Nationals living outside these areas are considered part of the ‘wider’ host community in Nairobi. The samples for both host communities were drawn using a 2-stage cluster design. In the first stage, eligible enumeration areas (EA) were drawn from the list of EAs covering Nairobi taken from the 2019 Population and Housing Census. In the second stage a listing of all host community households was established through a household census within all selected EAs, ensuring that refugee households were excluded to prevent overlap with the refugee sampling frame. 12 households and 6 replacements were drawn per EA. Our total sample consists of 2,433 host community households, 1,221 core hosts and 1,212 wider hosts.

The three sub-samples – refugees, core hosts, and wider hosts – are reflected in the ‘strata’ variable. The EAs which form the primary sampling units for the two host samples are anonymized and included in the ‘psu’ variable. Please note that the ‘psu’ variable clusters refugees under one numeric code (888).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Questionnaire is provided as external resources in pdf format. Questionnaires were produced through the World Bank developed Survey Solutions software. The survey was implemented in English,Swahili and Somali.

Abstract

In 2016, UNHCR became aware of a group of stateless persons living in or near Nairobi, Kenya. Most of them were Shona, descendants of missionaries who arrived from Zimbabwe and Zambia in the 1960s and remained in Kenya. The total number of Shona living in Kenya is estimated to be between 3,000 and 3,500 people. On their first arrival, the Shona were issued certificates of registration, but a change in the Registration of Persons Act of 1978 did not make provision for people of non-Kenyan descent, consequently denying the Shona citizenship. Zimbabwe and Zambia did not consider them nationals either, rendering them stateless. Besides the Shona, there are other groups of stateless persons of different origins and ethnicities, with the total number of stateless persons in Kenya estimated at 18,500. UNHCR and the Government of Kenya are taking steps to address statelessness in the country, among them is the registration of selected groups for nationalization. In April 2019, the Government of Kenya pledged to recognize qualifying members of the Shona community as Kenyan citizens. However, the lack of detailed information on the stateless population in Kenya hinders advocacy for the regularization of their nationality status. Together with the Kenyan Government through the Department of Immigration Services (DIS) and the Kenya National Bureau of Statistics (KNBS), UNHCR Kenya conducted registration and socioeconomic survey for the Shona community from May to July 2019. While the primary objective of the registration was to document migration, residence and family history with the aim of preparing their registration as citizens, this survey was conducted to provide a baseline on the socio-economic situation of the stateless Shona population for comparison with non-stateless populations of Kenya.

Geographic coverage

Githurai, Nairobi, Kiambaa and Kinoo

Analysis unit

Household and individual

Universe

All Shona living in Nairobi and Kiambu counties, Kenya

Kind of data

Census/enumeration data [cen]

Sampling procedure

The objective of the socio-economic survey was to cover the entire Shona population living in areas of the Nairobi and Kiambu counties. This included Shona living in Githurai, Kiambaa, Kinoo and other urban areas in and around Nairobi. Data collection for the socioeconomic survey took place concurrently with a registration verification. The registration verification was to collect information on the Shona's migration history, residence in Kenya and legal documentation to prepare their registration as citizens. The registration activity including questions on basic demographics also covered some enumeration areas outside the ones of the socio-economic survey, such as institutional households in Hurlingham belonging to a religious order who maintain significantly different living conditions than the average population. The total number of households for which socio-economic data was collected for is 350 with 1,692 individuals living in them. A listing of Shona households using key informant lists and respondent-driven referral to identify further households was conducted by KNBS and UNHCR before the start of enumeration for the registration verification and socio-economic survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following sections are included: household roster, education, employment, household characteristics, consumption and expenditure.

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, recoding and local suppression).

Response rate

Overall reponse rate was 99 percent, mainly due to refusal to participate.

Kenya recorded ***** male births per 100 female births in 2021. The country's gender ratio kept stable since 2011, after increasing from ***** in 2000.

Abstract

The 2022 Kenya Demographic and Health Survey (2022 KDHS) is the seventh DHS survey implemented in Kenya. The Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders implemented the survey. Survey planning began in late 2020 with data collection taking place from February 17 to July 19, 2022. 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 organizations that facilitated the successful implementation of the survey through technical or financial support were the Bill & Melinda Gates Foundation, the World Bank, the United Nations Children's Fund (UNICEF), the United Nations Population Fund (UNFPA), Nutrition International, the World Food Programme (WFP), the United Nations Entity for Gender Equality and the Empowerment of Women (UN Women), the World Health Organization (WHO), the Clinton Health Access Initiative, and the Joint United Nations Programme on HIV/AIDS (UNAIDS).

SURVEY OBJECTIVES The primary objective of the 2022 KDHS is to provide up-to-date estimates of demographic, health, and nutrition indicators to guide the planning, implementation, monitoring, and evaluation of population and health-related programs at the national and county levels. The specific objectives of the 2022 KDHS are to: Estimate fertility levels and contraceptive prevalence Estimate childhood mortality Provide basic indicators of maternal and child health Estimate the Early Childhood Development Index (ECDI) Collect anthropometric measures for children, women, and men Collect information on children's nutrition Collect information on women's dietary diversity Obtain information on knowledge and behavior related to transmission of HIV and other sexually transmitted infections (STIs) Obtain information on noncommunicable diseases and other health issues Ascertain the extent and patterns of domestic violence and female genital mutilation/cutting

Geographic coverage

National coverage

Analysis unit

Household, individuals, county and national level

Universe

The survey covered sampled households

Sampling procedure

The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently operates to conduct household-based sample surveys in Kenya. In 2019, Kenya conducted a Population and Housing Census, and a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomized into four equal subsamples. The survey sample was drawn from one of the four subsamples. The EAs were developed into clusters through a process of household listing and geo-referencing. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, resulting in 92 strata since Nairobi City and Mombasa counties are purely urban.

The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas, and, for some indicators, at the county level. Given this, the sample was designed to have 42,300 households, with 25 households selected per cluster, resulting into 1,692 clusters spread across the country with 1,026 clusters in rural areas and 666 in urban areas.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Eight questionnaires were used for the 2022 KDHS: 1. A full Household Questionnaire 2. A short Household Questionnaire 3. A full Woman's Questionnaire 4. A short Woman's Questionnaire 5. A Man's Questionnaire 6. A full Biomarker Questionnaire 7. A short Biomarker Questionnaire 8. A Fieldworker Questionnaire.

The Household Questionnaire collected information on: o Background characteristics of each person in the household (for example, name, sex, age, education, relationship to the household head, survival of parents among children under age 18) o Disability o Assets, land ownership, and housing characteristics o Sanitation, water, and other environmental health issues o Health expenditures o Accident and injury o COVID-19 (prevalence, vaccination, and related deaths) o Household food consumption

The Woman's Questionnaire was used to collect information from women age 15-49 on the following topics: o Socioeconomic and demographic characteristics o Reproduction o Family planning o Maternal health care and breastfeeding o Vaccination and health of children o Children's nutrition o Woman's dietary diversity o Early childhood development o Marriage and sexual activity o Fertility preferences o Husbands' background characteristics and women's employment activity o HIV/AIDS, other sexually transmitted infections (STIs), and tuberculosis (TB) o Other health issues o Early Childhood Development Index 2030 o Chronic diseases o Female genital mutilation/cutting o Domestic violence

The Man's Questionnaire was administered to men age 15-54 living in the households selected for long Household Questionnaires. The questionnaire collected information on: o Socioeconomic and demographic characteristics o Reproduction o Family planning o Marriage and sexual activity o Fertility preferences o Employment and gender roles o HIV/AIDS, other STIs, and TB o Other health issues o Chronic diseases o Female genital mutilation/cutting o Domestic violence

The Biomarker Questionnaire collected information on anthropometry (weight and height). The long Biomarker Questionnaire collected anthropometry measurements for children age 0-59 months, women age 15-49, and men age 15-54, while the short questionnaire collected weight and height measurements only for children age 0-59 months.

The Fieldworker Questionnaire was used to collect basic background information on the people who collected data in the field. This included team supervisors, interviewers, and biomarker technicians.

All questionnaires except the Fieldworker Questionnaire were translated into the Swahili language to make it easier for interviewers to ask questions in a language that respondents could understand.

Cleaning operations

Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and flag any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis.

Response rate

A total of 42,022 households were selected for the sample, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15-49 were identified as eligible for individual interviews. Interviews were completed with 32,156 women, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were the similar (95%). In the households selected for the male survey, 16,552 men age 15-54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%.

Abstract

The places we live affect our health status and the choices and opportunities we have (or do not have) to lead fulfilling lives. Over the past ten years, the African Population & Health Research Centre (APHRC) has led pioneering work in highlighting some of the major health and livelihood challenges associated with rapid urbanization in sub-Saharan Africa (SSA). In 2002, the Centre established the first longitudinal platform in urban Africa in the city of Nairobi in Kenya. The platform known as the Nairobi Urban Health and Demographic Surveillance System collects data on two informal settlements - Korogocho and Viwandani - in Nairobi City every four months on issues ranging from household dynamics to fertility and mortality, migration and livelihood as well as on causes of death, using a verbal autopsy technique. The dataset provided here contains key demographic and health indicators extracted from the longitudinal database. Researchers interested in accessing the micro-data can look at our data access policy and contact us.

Geographic coverage

The Demographic Surveillance Area (combining Viwandani and Korogocho slum settlements) covers a land area of about 0.97 km2, with the two informal settlements located about 7 km from each other. Korogocho is located 12 km from the Nairobi city center; in Kasarani division (now Kasarani district), while Viwandani is about 7 km from Nairobi city center in Makadara division (now Madaraka district). The DSA covers about seven villages each in Korogocho and Viwandani.

Analysis unit

Individual

Universe

Between 1st January and 31st December,2015 the Nairobi HDSS covered 86,304 individualis living in 30,219 households distributed across two informal settlements(Korogocho and Viwandani) were observed. All persons who sleep in the household prior to the day of the survey are included in the survey, while non-resident household members are excluded from the survey.

The present universe started out through an initial census carried out on 1st August,2002 of the population living in the two Informal settlements (Korogocho and Viwandani). Regular visits have since then been made (3 times a year) to update information on births, deaths and migration that have occurred in the households observed at the initial census. New members join the population through a birth to a registered member, or an in-migration, while existing members leave through a death or out-migration. The DSS adopts the concept of an open cohort that allows new members to join and regular members to leave and return to the system.

Kind of data

Event history data

Frequency of data collection

Three rounds in a year

Sampling procedure

This dataset is related to the whole demographic surveillance area population. The number of respondents has varied over the last 13 years (2002-2015), with variations being observed at both household level and at Individual level. As at 31st December 2015, 66,848 were being observed under the Nairobi HDSS living in 25,812 households distributed across two informal settlements(Korogocho and Viwandani). The variable IndividualId uniquely identifies every respondent observed while the variable LocationId uniquely identifies the room in which the individual was living at any point in time. To identify individuals who were living together at any one point in time (a household) the data can be split on location and observation dates.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Questionnaires are printed and administered in Swahili, the country's national language.

The questionnaires for the Nairobi HDSS were structured questionnaires based on the INDEPTH Model Questionnaire and were translated into Swahili with some modifications and additions.After an initial review the questionnaires were translated back into English by an independent translator with no prior knowledge of the survey. The back translation from the Swahili version was independently reviewed and compared to the English original. Differences in translation were reviewed and resolved in collaboration with the original translators. The English and Swahili questionnaires were both piloted as part of the survey pretest.

At baseline, a household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. In later rounds questionnaires to track the migration of the population observed at baseline, and additonal questionnaires to capture demographic and health events happening to the population have been introduced.

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of STATA data files

Where changes were made by the program, a cold deck imputation is preferred; where incorrect values were imputed using existing data from another dataset. If cold deck imputation was found to be insufficient, hot deck imputation was used, In this case, a missing value was imputed from a randomly selected similar record in the same dataset.

Some corrections are made automatically by the program(80%) and the rest by visual control of the questionnaires (20%).

1. 100% forms filled in by FRAs are rechecked for completeness, ensured that all the necessary event forms are filled in.
2. Spot checks are done on field over data collection by FRAs for reliability of data.
3. FRS instructs revisits wherever required.
4. Forms are checked on sample basis
5. Checks if all the necessary event forms are filled in.
6. Forms with inconsistencies identified at the time of entry are sent back to the field.
7. Creating and managing data entry checks for picking up inconsistencies
8. Monitoring field work: balancing work target and quality.
9. Dealing with data inconsistencies at data level and giving feedbacks to field staff.
10. Conducting training and refresher training wherever required.
11. Data cleaning

Response rate

Over the years the response rate at household level has varied between 95% and 97% with response rate at Individual Level varying between 92% and 95%. Challenges to acheiving a 100% response rate have included: - high population mobility within the study area - high population attrition - respondent fatigue - security in some areas

Sampling error estimates

Not applicable for surveillance data

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate KE031 MicroDataCleaned Starts 219285 2017-05-16 18:25
KE031 MicroDataCleaned Transitions 825036 825036 0 2017-05-16 18:25
KE031 MicroDataCleaned Ends 219285 2017-05-16 18:25
KE031 MicroDataCleaned SexValues 825036 2017-05-16 18:25
KE031 MicroDataCleaned DoBValues 42 824994 825036 0 2017-05-16 18:25

Abstract

The 1998 Kenya Demographic and Health Survey (KDHS) is a nationally representative survey of 7,881 wo 881 women age 15-49 and 3,407 men age 15-54. The KDHS was implemented by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics (CBS), with significant technical and logistical support provided by the Ministry of Health and various other governmental and nongovernmental organizations in Kenya. Macro International Inc. of Calverton, Maryland (U.S.A.) provided technical assistance throughout the course of the project in the context of the worldwide Demographic and Health Surveys (DHS) programme, while financial assistance was provided by the U.S. Agency for International Development (USAID/Nairobi) and the Department for International Development (DFID/U.K.). Data collection for the KDHS was conducted from February to July 1998. Like the previous KDHS surveys conducted in 1989 and 1993, the 1998 KDHS was designed to provide information on levels and trends in fertility, family planning knowledge and use, infant and child mortality, and other maternal and child health indicators. However, the 1998 KDHS went further to collect more in-depth data on knowledge and behaviours related to AIDS and other sexually transmitted diseases (STDs), detailed “calendar” data that allows estimation of contraceptive discontinuation rates, and information related to the practice of female circumcision. Further, unlike earlier surveys, the 1998 KDHS provides a national estimate of the level of maternal mortality (i.e. related to pregnancy and childbearing).The KDHS data are intended for use by programme managers and policymakers to evaluate and improve health and family planning programmes in Kenya. Fertility. The survey results demonstrate a continuation of the fertility transition in Kenya. At current fertility levels, a Kenyan women will bear 4.7 children in her life, down 30 percent from the 1989 KDHS when the total fertility rate (TFR) was 6.7 children, and 42 percent since the 1977/78 Kenya Fertility Survey (KFS) when the TFR was 8.1 children per woman. A rural woman can expect to have 5.2 children, around two children more than an urban women (3.1 children). Fertility differentials by women's education level are even more remarkable; women with no education will bear an average of 5.8 children, compared to 3.5 children for women with secondary school education. Marriage. The age at which women and men first marry has risen slowly over the past 20 years. Currently, women marry for the first time at an average age of 20 years, compared with 25 years for men. Women with a secondary education marry five years later (22) than women with no education (17).The KDHS data indicate that the practice of polygyny continues to decline in Kenya. Sixteen percent of currently married women are in a polygynous union (i.e., their husband has at least one other wife), compared with 19 percent of women in the 1993 KDHS, 23 percent in the 1989 KDHS, and 30 percent in the 1977/78 KFS. While men first marry an average of 5 years later than women, men become sexual active about onehalf of a year earlier than women; in the youngest age cohort for which estimates are available (age 20-24), first sex occurs at age 16.8 for women and 16.2 for men. Fertility Preferences. Fifty-three percent of women and 46 percent of men in Kenya do not want to have any more children. Another 25 percent of women and 27 percent of men would like to delay their next child for two years or longer. Thus, about three-quarters of women and men either want to limit or to space their births. The survey results show that, of all births in the last three years, 1 in 10 was unwanted and 1 in 3 was mistimed. If all unwanted births were avoided, the fertility rate in Kenya would fall from 4.7 to 3.5 children per woman. Family Planning. Knowledge and use of family planning in Kenya has continued to rise over the last several years. The 1998 KDHS shows that virtually all married women (98 percent) and men (99 percent) were able to cite at least one modern method of contraception. The pill, condoms, injectables, and female sterlisation are the most widely known methods. Overall, 39 percent of currently married women are using a method of contraception. Use of modern methods has increased from 27 in the 1993 KDHS to 32 percent in the 1998 KDHS. Currently, the most widely used methods are contraceptive injectables (12 percent of married women), the pill (9 percent), female sterilisation (6 percent), and periodic abstinence (6 percent). Three percent of married women are using the IUD, while over 1 percent report using the condom and 1 percent use of contraceptive implants (Norplant). The rapid increase in use of injectables (from 7 to 12 percent between 1993 and 1998) to become the predominant method, plus small rises in the use of implants, condoms and female sterilisation have more than offset small decreases in pill and IUD use. Thus, both new acceptance of contraception and method switching have characterised the 1993-1998 intersurvey period. Contraceptive use varies widely among geographic and socioeconomic subgroups. More than half of currently married women in Central Province (61 percent) and Nairobi Province (56 percent) are currently using a method, compared with 28 percent in Nyanza Province and 22 percent in Coast Province. Just 23 percent of women with no education use contraception versus 57 percent of women with at least some secondary education. Government facilities provide contraceptives to 58 percent of users, while 33 percent are supplied by private medical sources, 5 percent through other private sources, and 3 percent through community-based distribution (CBD) agents. This represents a significant shift in sourcing away from public outlets, a decline from 68 percent estimated in the 1993 KDHS. While the government continues to provide about two-thirds of IUD insertions and female sterilisations, the percentage of pills and injectables supplied out of government facilities has dropped from over 70 percent in 1993 to 53 percent for pills and 64 percent for injectables in 1998. Supply of condoms through public sector facilities has also declined: from 37 to 21 percent between 1993 and 1998. The survey results indicate that 24 percent of married women have an unmet need for family planning (either for spacing or limiting births). This group comprises married women who are not using a method of family planning but either want to wait two year or more for their next birth (14 percent) or do not want any more children (10 percent). While encouraging that unmet need at the national level has declined (from 34 to 24 percent) since 1993, there are parts of the country where the need for contraception remains high. For example, the level of unmet need is higher in Western Province (32 percent) and Coast Province (30 province) than elsewhere in Kenya. Early Childhood Mortality. One of the main objectives of the KDHS was to document current levels and trends in mortality among children under age 5. Results from the 1998 KDHS data make clear that childhood mortality conditions have worsened in the early-mid 1990s; this after a period of steadily improving child survival prospects through the mid-to-late 1980s. Under-five mortality, the probability of dying before the fifth birthday, stands at 112 deaths per 1000 live births which represents a 24 percent increase over the last decade. Survival chances during age 1-4 years suffered disproportionately: rising 38 percent over the same period. Survey results show that childhood mortality is especially high when associated with two factors: a short preceding birth interval and a low level of maternal education. The risk of dying in the first year of life is more than doubled when the child is born after an interval of less than 24 months. Children of women with no education experience an under-five mortality rate that is two times higher than children of women who attended secondary school or higher. Provincial differentials in childhood mortality are striking; under-five mortality ranges from a low of 34 deaths per 1000 live births in Central Province to a high of 199 per 1000 in Nyanza Province. Maternal Health. Utilisation of antenatal services is high in Kenya; in the three years before the survey, mothers received antenatal care for 92 percent of births (Note: These data do not speak to the quality of those antenatal services). The median number of antenatal visits per pregnancy was 3.7. Most antenatal care is provided by nurses and trained midwives (64 percent), but the percentage provided by doctors (28 percent) has risen in recent years. Still, over one-third of women who do receive care, start during the third trimester of pregnancy-too late to receive the optimum benefits of antenatal care. Mothers reported receiving at least one tetanus toxoid injection during pregnancy for 90 percent of births in the three years before the survey. Tetanus toxoid is a powerful weapon in the fight against neonatal tetanus, a deadly disease that attacks young infants. Forty-two percent of births take place in health facilities; however, this figure varies from around three-quarters of births in Nairobi to around one-quarter of births in Western Province. It is important for the health of both the mother and child that trained medical personnel are available in cases of prolonged labour or obstructed delivery, which are major causes of maternal morbidity and mortality. The 1998 KDHS collected information that allows estimation of mortality related to pregnancy and childbearing. For the 10-year period before the survey, the maternal mortality ratio was estimated to be 590 deaths per 100,000 live births. Bearing on average 4.7 children, a Kenyan woman has a 1 in 36 chance of dying from maternal causes during her lifetime. Childhood Immunisation. The KDHS

L.J. Hlusko collected these data in Nairobi at the National Museums of Kenya in 2002 and 2003 using fixed-jawed dental calipers (Mitutoyo© Model NTD12-6″C). Each measurement was collected two or three times (three times if the data were collected in 2002, and twice if they were collected in 2003), and the average of those two measurements is presented here. A total of 82 linear dimensions were measured for each individual, although the full set of 82 measurements was not possible to take from all individuals. Mesiodistal and buccolingual dimensions follow standard definitions. The mandibular molar protoconid radial enamel thickness follows the protocol developed in

Hlusko, L.J., Suwa, G., Kono, R.T. and Mahaney, M.C., 2004. Genetics and the evolution of primate enamel thickness: a baboon model. American Journal of Physical Anthropology: The Official Publication of the American Association of Physical Anthropologists, 124(3), pp.223-233. https://doi.org/10.1002/ajpa.10353

Total population in Nairobi Kenya, 2021

Nairobi is the most populated county in Kenya. The area formed by the country's capital and its surroundings has a population of over 4.3 million inhabitants. Of the 47 counties in Kenya, 18 have a population of more than one million people.

Abstract

The 2014 Kenya Demographic and Health Survey (KDHS) provides information to help monitor and evaluate population and health status in Kenya. The survey, which follows up KDHS surveys conducted in 1989, 1993, 1998, 2003, and 2008-09, is of special importance for several reasons. New indicators not collected in previous KDHS surveys, such as noncommunicable diseases, fistula, and men's experience of domestic violence, are included. Also, it is the first national survey to provide estimates for demographic and health indicators at the county level. Following adoption of a constitution in Kenya in 2010 and devolution of administrative powers to the counties, the new 2014 KDHS data should be valuable to managers and planners. The 2014 KDHS has specifically collected data to estimate fertility, to assess childhood, maternal, and adult mortality, to measure changes in fertility and contraceptive prevalence, to examine basic indicators of maternal and child health, to estimate nutritional status of women and children, to describe patterns of knowledge and behaviour related to the transmission of HIV and other sexually transmitted infections, and to ascertain the extent and pattern of domestic violence and female genital cutting. Unlike the 2003 and 2008-09 KDHS surveys, this survey did not include HIV and AIDS testing. HIV prevalence estimates are available from the 2012 Kenya AIDS Indicator Survey (KAIS), completed prior to the 2014 KDHS. Results from the 2014 KDHS show a continued decline in the total fertility rate (TFR). Fertility decreased from 4.9 births per woman in 2003 to 4.6 in 2008-09 and further to 3.9 in 2014, a one-child decline over the past 10 years and the lowest TFR ever recorded in Kenya. This is corroborated by the marked increase in the contraceptive prevalence rate (CPR) from 46 percent in 2008-09 to 58 percent in the current survey. The decline in fertility accompanies a marked decline in infant and child mortality. All early childhood mortality rates have declined between the 2003 and 2014 KDHS surveys. Total under-5 mortality declined from 115 deaths per 1,000 live births in the 2003 KDHS to 52 deaths per 1,000 live births in the 2014 KDHS. The maternal mortality ratio is 362 maternal deaths per 100,000 live births for the seven-year period preceding the survey; however, this is not statistically different from the ratios reported in the 2003 and 2008-09 KDHS surveys and does not indicate any decline over time. The proportion of mothers who reported receiving antenatal care from a skilled health provider increased from 88 percent to 96 percent between 2003 and 2014. The percentage of births attended by a skilled provider and the percentage of births occurring in health facilities each increased by about 20 percentage points between 2003 and 2014. The percentage of children age 12-23 months who have received all basic vaccines increased slightly from the 77 percent observed in the 2008-09 KDHS to 79 percent in 2014. Six in ten households (59 percent) own at least one insecticide-treated net, and 48 percent of Kenyans have access to one. In malaria endemic areas, 39 percent of women received the recommended dosage of intermittent preventive treatment for malaria during pregnancy. Awareness of AIDS is universal in Kenya; however, only 56 percent of women and 66 percent of men have comprehensive knowledge about HIV and AIDS prevention and transmission. The 2014 KDHS was conducted as a joint effort by many organisations. The Kenya National Bureau of Statistics (KNBS) served as the implementing agency by providing guidance in the overall survey planning, development of survey tools, training of personnel, data collection, processing, analysis, and dissemination of the results. The Bureau would like to acknowledge and appreciate the institutions and agencies for roles they played that resulted in the success of this exercise: Ministry of Health (MOH), National AIDS Control Council (NACC), National Council for Population and Development (NCPD), Kenya Medical Research Institute (KEMRI), Ministry of Labour, Social Security and Services, United States Agency for International Development (USAID/Kenya), ICF International, United Nations Fund for Population Activities (UNFPA), the United Kingdom Department for International Development (DfID), World Bank, Danish International Development Agency (DANIDA), United Nations Children's Fund (UNICEF), German Development Bank (KfW), World Food Programme (WFP), Clinton Health Access Initiative (CHAI), Micronutrient Initiative (MI), US Centers for Disease Control and Prevention (CDC), Japan International Cooperation Agency (JICA), Joint United Nations Programme on HIV/AIDS (UNAIDS), and the World Health Organization (WHO). The management of such a huge undertaking was made possible through the help of a signed memorandum of understanding (MoU) by all the partners and the creation of active Steering and Technical Committees.

Geographic coverage

County, Urban, Rural and National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2014 KDHS was drawn from a master sampling frame, the Fifth National Sample Survey and Evaluation Programme (NASSEP V). This is a frame that the KNBS currently operates to conduct household-based surveys throughout Kenya. Development of the frame began in 2012, and it contains a total of 5,360 clusters split into four equal subsamples. These clusters were drawn with a stratified probability proportional to size sampling methodology from 96,251 enumeration areas (EAs) in the 2009 Kenya Population and Housing Census. The 2014 KDHS used two subsamples of the NASSEP V frame that were developed in 2013. Approximately half of the clusters in these two subsamples were updated between November 2013 and September 2014. Kenya is divided into 47 counties that serve as devolved units of administration, created in the new constitution of 2010. During the development of the NASSEP V, each of the 47 counties was stratified into urban and rural strata; since Nairobi county and Mombasa county have only urban areas, the resulting total was 92 sampling strata. The 2014 KDHS was designed to produce representative estimates for most of the survey indicators at the national level, for urban and rural areas separately, at the regional (former provincial1) level, and for selected indicators at the county level. In order to meet these objectives, the sample was designed to have 40,300 households from 1,612 clusters spread across the country, with 995 clusters in rural areas and 617 in urban areas. Samples were selected independently in each sampling stratum, using a two-stage sample design. In the first stage, the 1,612 EAs were selected with equal probability from the NASSEP V frame. The households from listing operations served as the sampling frame for the second stage of selection, in which 25 households were selected from each cluster. The interviewers visited only the preselected households, and no replacement of the preselected households was allowed during data collection. The Household Questionnaire and the Woman's Questionnaire were administered in all households, while the Man's Questionnaire was administered in every second household. Because of the non-proportional allocation to the sampling strata and the fixed sample size per cluster, the survey was not self-weighting. The resulting data have, therefore, been weighted to be representative at the national, regional, and county levels.

Sampling deviation

Not available

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2014 KDHS used a household questionnaire, a questionnaire for women age 15-49, and a questionnaire for men age 15-54. These instruments were based on the model questionnaires developed for The DHS Program, the questionnaires used in the previous KDHS surveys, and the current information needs of Kenya. During the development of the questionnaires, input was sought from a variety of organisations that are expected to use the resulting data. A two-day workshop involving key stakeholders was held to discuss the questionnaire design. Producing county-level estimates requires collecting data from a large number of households within each county, resulting in a considerable increase in the sample size from 9,936 households in the 2008-09 KDHS to 40,300 households in 2014. A survey of this magnitude introduces concerns related to data quality and overall management. To address these concerns, reduce the length of fieldwork, and limit interviewer and respondent fatigue, a decision was made to not implement the full questionnaire in every household and, in so doing, to collect only priority indicators at the county level. Stakeholders generated a list of these priority indicators. Short household and woman's questionnaires were then designed based on the full questionnaires; the short questionnaires contain the subset of questions from the full questionnaires required to measure the priority indicators at the county level. Thus, a total of five questionnaires were used in the 2014 KDHS: (1) a full Household Questionnaire, (2) a short Household Questionnaire, (3) a full Woman's Questionnaire, (4) a short Woman's Questionnaire, and (5) a Man's Questionnaire. The 2014 KDHS sample was divided into halves. In one half, households were administered the full Household Questionnaire, the full Woman's Questionnaire, and the Man's Questionnaire. In the other half, households were administered the short Household Questionnaire and the short Woman's Questionnaire. Selection of these subsamples was done at the household level-within a cluster, one in every two

Key information about Kenya Monthly Earnings

• Kenya Monthly Earnings stood at 590 USD in Jun 2023, compared with the previous figure of 639 USD in Jun 2022
• Kenya Monthly Earnings data is updated yearly, available from Jun 1997 to Jun 2023, with an average number of 420 USD
• The data reached the an all-time high of 651 USD in Jun 2020 and a record low of 157 USD in Jun 1997

CEIC calculates Monthly Earnings from annual Average Wage Earnings divided by 12 and converts it into USD. The Kenya National Bureau of Statistics provides Average Wage Earnings in local currency. The Central Bank of Kenya average market exchange rate is used for currency conversions. Monthly Earnings are in annual frequency, ending in June of each year. Monthly Earnings prior to 2008 are based on ISIC Rev. 2.


Further information about Kenya Monthly Earnings

• In the latest reports, Kenya Population reached 49 million people in Dec 2021
• Unemployment Rate of Kenya increased to 3 % in Dec 2020
• The country's Labour Force Participation Rate increased to 74 % in Dec 2023

Demographics, clinical characteristics and number of cases by year of diagnosis for incident cancer cases in Nairobi Kenya (2010–2019) of the cleaned dataset (n = 7584).

Abstract

African Population and Health Research Center (APHRC) had from 2005 to 2010, conducted a longitudinal survey in two formal settlements (Harambee and Jericho) and two informal (slum) settlements (Korogocho and Viwandani) in Nairobi to understand the uptake and patterns of school enrolment after the introduction of the Free Primary Education (FPE) in Kenya. The results of the study showed increased utilization of private informal schools among slum households as compared to the formal settlements.

That is, by 2010, almost two thirds of pupils in the slum settlements were enrolled in private informal schools while in Harambee and Jericho, more than three quarters of the pupils were enrolled in government primary schools with the remaining portion attending high-end private schools.

In 2012, ERP conducted a cross-sectional survey across six major urban centers to investigate, within the context of FPE, if the pattern of school enrolment observed in Korogocho and Viwandani slums could also be observed in other urban slums in Kenya. Below are some key facts from this study. Data is manly disaggregated by school type - government schools (FPE schools), and non-government schools, specifically the formal private schools and low-cost schools.

The study tried to answer four broad questions: What is the impact of free primary education (FPE) on schooling patterns among poor households in urban slums in Kenya? What are the qualitative and quantitative explanations of the observed patterns? Is there a difference in achievement measured by performance in a standardized test on literacy and numeracy administered to pupils in government schools under FPE and non-government schools?

Geographic coverage

Kenya - in six urban slums of Nairobi spread across 6 towns - Nairobi, Mombasa, Nyeri, Eldoret, Nakuru and Kisumu. In total 5854 households and 230 schools were covered.

Analysis unit

A cross-sectional survey focusing on households with individuals aged between 5 and 19, as well as schools and pupils in grades 3 and 6. Data therefore exits at household, individuals, schools and student levels.

Universe

This is a cross sectional study that was conducted in seven slum sites spread across six towns namely Nairobi, Mombasa, Kisumu, Eldoret, Nakuru and Nyeri and targetted hoseholds with individuals aged between 5 and 19 years and schools located within the study site or within a 1KM radius. For the schools to be included in the study they had to have both grade 3 and 6, which were target grades for this study.

Sampling procedure

This was a cross-sectional study involving schools and households. The study covered six purposively selected major towns (Eldoret, Kisumu, Mombasa, Nairobi, Nakuru and Nyeri) in different parts of Kenya (see Map 1) to provide case studies that could lead to a broader understanding of what is happening in urban informal settlements. The selection of a town was informed by presence of informal settlements and its administrative importance, that is, provincial headquarter or regional business hub. A three-stage cluster sampling procedure was used to select households in all towns with an exception of Nairobi. At the first stage, major informal settlement locations were identified in each of the six towns. The informal settlement sites were identified based on enumeration areas (EAs) designated as slums in the 2009 National Population and Housing Census conducted by the Kenya National Bureau of Statistics (KNBS). After identifying all slum EAs in each of the study towns, the location with the highest number of EAs designated as slum settlements was selected for the study. At the second stage of sampling, 20% of EAs within each major slum location were randomly selected. However, in Nakuru we randomly selected 67% (10) EAs while in Nyeri all available 9 EAs were included in the sample. This is because these two towns had fewer EAs and therefore the need to oversample to have a representative number of EAs. In total, 101 EAs were sampled from the major slum locations across the five towns. At the third stage, all households in the sampled EAs were listed using the 2009 census' EA maps prepared by KNBS. During the listing, 10,388 households were listed in all sampled EAs. Excluding Nairobi, 4,042 (57%) households which met the criteria of having at least one school-going child aged 5-20 years were selected for the survey. In Nairobi, 50% of all households which had at least one school-going child aged between 5 and 20 years were randomly sampled from all EAs existing in APHRC schooling data collected in 2010. A total of 3,060 households which met the criteria were selected. The need to select a large sample of households in Nairobi was to enable us link data from the current study with previous ones that covered over 6000 households in Korogocho and Viwandani. By so doing, we were able to get a representative sample of households in Nairobi to continue observing the schooling patterns longitudinally. In all, there were 7,102 eligible households in all six towns. A total of 14,084 individuals within the target age bracket living in 5,854 (82% of all eligible households) participated in the study. The remaining 18% of eligible households were not available for the interview as most of them had either left the study areas, declined the interview, or lacked credible respondents in the household at the time of the data collection visit or call back.

For the school-based survey, schools in each town were listed and classified into three groups based on their location: (i) within the selected slum location; (ii) within the catchment area of the selected slum area - about 1 km radius from the border of the study locations; and (iii) away from a selected slum. In Nairobi, schools were selected from existing APHRC data. During the listing exercise, lists of schools were also obtained from Municipality/City Education Departments in selected towns. The lists were used to counter-check the information obtained during listing. All schools located within the selected slum areas and those situated within the catchment area (1 km radius from the border of the slum) were included in the sample as long as they had a grade 6 class or intended to have one in 2012. The selection of schools within an informal settlement and those located within 1 km radius was because they were more likely to be accessed by children from the target informal settlement. Two hundred and forty-five (245) schools met the selection criteria and were included in the sample. Two hundred and thirty (230) primary schools (89 government schools, 94 formal private, and 47 low-cost schools) eventually participated in the survey. A total of 7,711 grade 3, 7,319 grade 6 pupils and 671 teachers of the same grades were reached and interviewed. All 230 head teachers (or their deputies) were interviewed on school characteristics.

Mode of data collection

Face-to-face [f2f]; Focus groups; Assessment; Filming (classroom observation).

Research instrument

Five survey questionnaires were administered at household level:

(i). An individual schooling history questionnaire was administered to individuals aged 5 - 20. The questionnaire was directly administered to individuals aged 12 - 20 and administered to a proxy for children younger than 12 years. Ideally, the proxy was the child's parent or guardian, or an adult familiar with the individual's schooling history and who usually resides in the same household. The questionnaire had two main sections: school participation for the current year (year of interview), and school participation for the five years preceding the year of interview (i.e. 2007 to 2011). The section on schooling participation on the current year collected information on the schooling status of the individual, the type, name and location of the school that the individual was attending, grade, and whether the individual had changed schools or dropped out of school in the current year. Respondents also provided information on the reasons for changing schools and dropping out of school, where applicable. The section on school participation for previous years also collected similar information. The questionnaire also collected information on the individual's year of birth and when they joined grade one.

(ii). A household schedule questionnaire was administered to the household head or the spouse. It sought information on the members of the household, their relationship to the household head, their gender, age, education and parental survivorship.

(iii). A parental/guardian perception questionnaire was administered to the household head or the parent/guardian of the child. It collected information on the parents/guardians' perceptions on Free Primary Education since its implementation, household support to school where child(ren) attends and household schooling decision.

(iv). A parental/guardian involvement questionnaire was strictly administered to a parent or guardian who usually lives in the household and who was equipped with adequate knowledge of the individual's schooling information (i.e. credible respondent). The questionnaire was completed for each individual of the targeted age bracket (5-20 years). The information on the child comprised questions on the gender of the child, parental/guardian aspirations for the child's educational attainment, and parental beliefs about the child's ability in school and their chances of achieving the aspired level.

(v). A household amenities and livelihood questionnaire was administered to the household head or the spouse or a member of the household who could give reliable information. The questionnaire collected information on duration of stay in the