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.

Analysis unit

Persons and households Nairobi oversample. Weighted by district and age.

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: no - Households: Yes - Group quarters:

Universe

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

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Statistics Division Ministry of Finance and Planning

SAMPLE SIZE (person records): 659310.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on individuals.

Historical dataset 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.

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.

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%.

Kikuyu was the largest ethnic group in Kenya, accounting for ** percent of the country's population in 2019. Native to Central Kenya, the Kikuyu constitute a Bantu group with more than eight million people. The groups Luhya and Kalenjin followed, with respective shares of **** percent and **** percent of the population. Overall, Kenya has more than 40 ethnic groups.

Major Towns by PopulationTowns in Kenya: Kenya’s capital city is Nairobi. It is the largest city in East Africa and the region’s Financial, Communication and Diplomatic Capital. In Kenya there are only three incorporated cities but there are numerous municipalities and towns with significant urban populations. Two of the cities, Nairobi and Mombasa are cities whose county borders run the same as their city limits, so in a way they could be thought of as City-CountiesNairobi is the only city in the world with a game park. Nairobi National Park is a preserved ecosystem where you can view wildlife in its natural habitat. Hotels, airlines and numerous tour firms and agencies offer tour packages for both domestic and foreign tourists visiting Nairobi and the park. The tourism industry provides direct employment to thousands of Nairobi residents.

The total population of Kenya was estimated at approximately 52.44 million people in 2024. Following a continuous upward trend, the total population has risen by around 36.72 million people since 1980. Between 2024 and 2030, the total population will rise by around 5.54 million people, continuing its consistent upward trajectory.This indicator describes the total population in the country at hand. This total population of the country consists of all persons falling within the scope of the census.

Abstract

The 2014 Kenya Demographic and Health Survey (KDHS) was designed to provide information to monitor and evaluate population and health status in Kenya and to be a follow-up to the previous KDHS surveys. In addition, it provides new information on indicators previously not collected in KDHS surveys, such as fistula and men’s experience of domestic violence. The survey also aims to provide estimates for selected demographic and health indicators at the county level.

The specific objectives of the 2014 KDHS were to: • Estimate fertility and childhood, maternal, and adult mortality • Measure changes in fertility and contraceptive prevalence • Examine basic indicators of maternal and child health • Collect anthropometric measures for children and women • Describe patterns of knowledge and behaviour related to transmission of HIV and other sexually transmitted infections • Ascertain the extent and pattern of domestic violence and female circumcision

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

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 provincial) 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.

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

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.

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 households was selected for the full questionnaires, and the remaining households were selected for the short questionnaires.

The Household Questionnaire was used to list all of the usual members of the household and visitors who stayed in the household the night before the survey. One of the main purposes of the Household Questionnaire was to identify women and men who were eligible for the individual interview. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight measurements of women age 15-49 and children under age 5.

The Woman’s Questionnaires were used to collect information from women age 15-49.

The Man’s Questionnaire was administered to men age 15-54 living in every second household in the sample. The Man’s Questionnaire collected information similar to that contained in the Woman’s Questionnaire but was shorter because it did not contain questions on maternal and child health, nutrition, adult and maternal mortality, or experience of female circumcision or fistula.

Cleaning operations

Completed questionnaires were sent to the KNBS Data Processing Centre in Nairobi. Office editors who received the questionnaires verified cluster and household numbers to ensure that they were consistent with the sampled list. They also ensured that each cluster had 25 households and that all questionnaires for a particular household were packaged together.

Data entry began on May 28, 2014, with a four-day training session and continued until November 21, 2014. All data were double entered (100 percent verification) using CSPro software. The data processing team included 42 keyers, three office editors, two secondary editors, four supervisors, and one data manager. Secondary editing, which included further data cleaning and validation, ran simultaneously with data entry and was completed on January 28, 2015, in collaboration with ICF International. The KDHS Key Indicators Report was prepared and launched in April 2015.

Response rate

A total of 39,679 households were selected for the sample, of which 36,812 were found occupied at the time of the fieldwork. Of these households, 36,430 were successfully interviewed, yielding an overall household response rate of 99 percent. The shortfall of households occupied was primarily due to structures that were found to be vacant or destroyed and households that were absent for an extended period of time.

As noted, the 2014 KDHS sample was divided into halves, with one half of households receiving the full Household Questionnaire, the full Woman’s Questionnaire, and the Man’s Questionnaire and the other half receiving the short Household Questionnaire and the short Woman’s Questionnaire. The household response rate for the full Household Questionnaire was 99 percent, as was the household response rate for the short Household Questionnaire.

In the households selected for and interviewed using the full questionnaires, a total of 15,317 women were identified as eligible for the full Woman’s Questionnaire, of whom 14,741 were interviewed, generating a response rate of 96 percent. A total of 14,217 men were identified as eligible in these households, of whom 12,819 were successfully interviewed, generating a response rate of 90 percent.

In the households selected for and interviewed with the short questionnaires, a total of 16,855 women were identified as eligible for the short Woman’s Questionnaire, of whom 16,338 were interviewed, yielding a response rate of 97 percent.

Response rates are lower in the urban sample than in the rural sample, more so for men. The principal reason for non-response among both eligible men and eligible women was failure to find them at home despite repeated visits to the household. The lower response rates for men reflect the more frequent and longer absences of men from the household

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry

Kenya had over ** million households according to the last census done in 2019. The majority, some *** million, lived in urban areas, while *** million dwelled in rural zones. Nairobi City was the county with more households, approximately *** million.

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

The 1993 Kenya Demographic and Health Survey (KDHS) was a nationally representative survey of 7,540 women age 15-49 and 2,336 men age 20-54. The KDHS was designed to provide information on levels and trends of fertility, infant and child mortality, family planning knowledge and use, maternal and child health, and knowledge of AIDS. In addition, the male survey obtained data on men's knowledge and attitudes towards family planning and awareness of AIDS. The data are intended for use by programme managers and policymakers to evaluate and improve family planning and matemal and child health programmes. Fieldwork for the KDHS took place from mid-February until mid-August 1993. All areas of Kenya were covered by the survey, except for seven northem districts which together contain less than four percent of the country's population.

The KDHS was conducted by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics of the Government of Kenya. Macro International Inc. provided financial and technical assistance to the project through the intemational Demographic and Health Surveys (DHS) contract with the U.S. Agency for International Development.

OBJECTIVES

The KDHS is intended to serve as a source of population and health data for policymakers and the research community. It was designed as a follow-on to the 1989 KDHS, a national-level survey of similar size that was implemented by the same organisations. In general, the objectives of KDHS are to: - assess the overall demographic situation in Kenya, - assist in the evaluation of the population and health programmes in Kenya, - advance survey methodology, and - assist the NCPD to strengthen and improve its technical skills to conduct demographic and health surveys.

The KDHS was specifically designed to: - provide data on the family planning and fertility behaviour of the Kenyan population to enable the NCPD to evaluate and enhance the National Family Planning Programme, - measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, urban/rural residence, availability of contraception, breastfeeding habits and other socioeconomic factors, and - examine the basic indicators of maternal and child health in Kenya.

KEY FINDINGS

The 1993 KDHS reinforces evidence of a major decline in fertility which was first revealed by the findings of the 1989 KDHS. Fertility continues to decline and family planning use has increased. However, the disparity between knowledge and use of family planning remains quite wide. There are indications that infant and under five child mortality rates are increasing, which in part might be attributed to the increase in AIDS prevalence.

Geographic coverage

The 1993 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population.

Analysis unit

• Household
• Women age 15-49
• Men age 20-54
• Children under five

Universe

The population covered by the 1993 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband age 20-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The sample for the 1993 KDHS was national in scope, with the exclusion of all three districts in Northeastern Province and four other northern districts (Isiolo and Marsabit from Eastern Province and Samburu and Turkana from Rift Valley Province). Together the excluded areas account for less than four percent of Kenya's population. The KDHS sample points were selected from a national master sample maintained by the Central Bureau of Statistics, the third National Sample Survey and Evaluation Programme (NASSEP-3), which is an improved version of NASSEP2 used in the 1989 survey. This master sample follows a two-stage design, stratified by urban-rural residence, and within the rural stratum, by individual district. In the first stage, 1989 census enumeration areas (EAs) were selected with probability proportional to size. The selected EAs were segmented into the expected number of standard-sized clusters to form NASSEP clusters. The entire master sample consists of 1,048 rural and 325 urban ~ sample points ("clusters"). A total of 536 clusters---92 urban and 444 rural--were selected for coverage in the KDHS. Of these, 520 were successfully covered. Sixteen clusters were inaccessible for various reasons.

As in the 1989 KDHS, selected districts were oversampled in the 1993 survey in order to produce more reliable estimates for certain variables at the district level. Fifteen districts were thus targetted in the 1993 KDHS: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Machakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu; in addition, Nairobi and Mombasa were also targetted. Although six of these districts were subdivided shortly before the sample design was finalised) the previous boundaries of these districts were used for the KDHS in order to maintain comparability with the 1989 survey. About 400 rural households were selected in each of these 15 districts, just over 1000 rural households in other districts, and about 18130 households in urban areas, for a total of almost 9,000 households. Due to this oversampling, the KDHS sample is not self-weighting at the national level.

After the selection of the KDHS sample points, fieldstaff from the Central Bureau of Statistics conducted a household listing operation in January and early February 1993, immediately prior to the launching of the fieldwork. A systematic sample of households was then selected from these lists, with an average "take" of 20 households in the urban clusters and 16 households in rural clusters, for a total of 8,864 households selected. Every other household was identified as selected for the male survey, meaning that, in addition to interviewing all women age 15-49, interviewers were to also interview all men age 20-54. It was expected that the sample would yield interviews with approximately 8,000 women age 15-49 and 2,500 men age 20-54.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the KDHS: a Household Questionnaire, a Woman's Questionnaire, a Man's Questionnaire and a Services Availability Questionnaire. The contents of these questionnaires were based on the DHS Model B Questionnaire, which is designed for use in countries with low levels of contraceptive use. Additions and modifications to the model questionnaires were made during a series of meetings organised around specific topics or sections of the questionnaires (e.g., fertility, family planning). The NCPD invited staff from a variety of organisations to attend these meetings, including the Population Studies Research Institute and other departments of the University of Nairobi, the Woman's Bureau, and various units of the Ministry of Health. The questionnaires were developed in English and then translated into and printed in Kiswahili and eight of the most widely spoken local languages in Kenya (Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Meru, and Mijikenda).

a) The Household Questionnaire was used to list all the usual members and visitors of selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

b) The Woman's Questionnaire was used to collect information from women aged 15-49. These women were asked questions on the following topics: Background characteristics (age, education, religion, etc.), Reproductive history, Knowledge and use of family planning methods, Antenatal and delivery care, Breastfeeding and weaning practices, Vaccinations and health of children under age five, Marriage, Fertility preferences, Husband's background and respondent's work, Awareness of AIDS. In addition, interviewing teams measured the height and weight of children under age five (identified through the birth histories) and their mothers.

c) Information from a subsample of men aged 20-54 was collected using a Man's Questionnaire. Men were asked about their background characteristics, knowledge and use of family planning methods, marriage, fertility preferences, and awareness of AIDS.

d) The Services Availability Questionnaire was used to collect information on the health and family planning services obtained within the cluster areas. One service availability questionnaire was to be completed in each cluster.

Cleaning operations

All questionnaires for the KDHS were returned to the NCPD headquarters for data processing. The processing operation consisted of office editing, coding of open-ended questions, data entry, and editing errors found by the computer programs. One NCPD officer, one data processing supervisor, one questionnaire administrator, two office editors, and initially four data entry operators were responsible for the data processing operation. Due to attrition and the need to speed up data processing, another four data entry operators were later hired

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

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).

Kenya had a Protestant population of nearly 15.8 million people, according to the last country census conducted in 2019. Around 1.36 million Protestants lived in the capital Nairobi, the largest amount among all Kenyan counties. Nearly 882,800 people living in Kiambu adhered to Protestantism, while 715,700 Protestants dwelled in Bungoma. The religion had the highest number of followers in the country.

Abstract

The Bungoma County Multiple Indicator Cluster Survey (MICS5) was conducted in collaboration with the Population Studies and Research Institute (PSRI) of the University of Nairobi, the Kenya National Bureau of Statistics (KNBS) and the United Nations Children's Fund (UNICEF).The Kenya National Bureau of Statistics implemented (MICS5) in 2013-2014 in the three counties of Bungoma, Kakamega and Turkana as part of Global MICS round five.

The global MICS program was developed by UNICEF in the 1990s as an international household survey program to support countries in the collection of internationally comparable data on a wide range of indicators on the situation of children and women. MICS surveys measure key indicators that allow countries to generate data for use in policies and programs and to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments. Technical and financial support were provided by the United Nations Children's Fund.

The results of this survey provided requisite baseline information that can be used to facilitate evidence-based planning, budgeting and programming by policymakers and stakeholders at the county levels. The survey will go a long way in encouraging increased demand for use of statistics by policy makers at devolved levels and will ensure that resources at both county and national levels are used most effectively through well-planned projects/programs that will benefit especially the women and children of the three counties. The MICS5 results were critical in gauging milestones achieved in the field of education, nutrition, child development, health for women and children in the three counties and in evaluating the various health based policies that the government has formulated over the years towards achieving the national welfare objectives.

The 2013-14 MICS5 data was critical in informing the future planning for the three counties, especially in view of the new constitutional dispensation and Vision 2030. It was anticipated that MICS5 would supplement the data collected during the 2014 Kenya Demographic and Health Survey (KDHS). In addition the information collected would inform strategic communication for social and behavior change interventions by government and partners including UNICEF. Furthermore the data contributed to the improvement of data and monitoring systems in the three counties. The primary objectives of the Bungoma County survey are: 1. To provide up-to-date information for assessing the situation of children and women in Bungoma County. 2. To generate data for the critical assessment of the progress made in various areas, and to put additional efforts in those areas that require more attention. 3. To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, and other internationally agreed upon goals, as a basis for future action. 4. To collect disaggregated data for the identification of disparities, to allow for evidence based policy-making aimed at social inclusion of the most vulnerable. 5. To contribute to the generation of baseline data for the post-2015 agenda. 6. To validate data from other sources and the results of focused interventions. 7. To contribute to the improvement of data and monitoring systems in Kenya and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Geographic coverage

National

Analysis unit

• Individuals
• Households

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Bungoma County MICS was to produce statistically reliable estimates of indicators, at county level. The urban and rural areas in Bungoma County were the sampling strata. A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample. MICS5 utilized the recently created fifth National Sample Survey and Evaluation Program (NASSEP V) frame which is a household based master sampling frame developed and maintained by KNBS. The frame was implemented using a multi-tiered structure, in which a set of 4 sub-samples (C1, C2, C3, C4) were developed. It is based on the list of enumeration areas (EAs) from the 2009 Kenya Population and Housing Census. The frame is stratified according to County and further into rural and urban. Each of the sub-samples is representative at county level and at national (i.e. Urban/rural) level and contains 1,340 clusters.

The Primary Sampling Units (PSUs) for the survey were clusters drawn from the NASSEP V sampling frame, so the first component of the probabilities and weights are based on that master sample. Within each stratum the PSUs for the MICS were selected independently from one of the subsamples of the master sample using Equal Probability Selection Method (EPSEM). A total of 50 clusters were selected from the master sample in this way.

Out of the 50 sample clusters selected for Bungoma County, it was established that 30 had been listed more than six months prior to the start of the survey. These listing for these clusters was updated prior to selection of households. For this purpose, listing teams visited each cluster, and listed all occupied households. For the remaining 20 sample clusters a more recent listing was available, so it was used for selecting the sample households.

Mode of data collection

Face-to-face [f2f]

Research instrument

A set of three questionnaires was used in the survey: 1. A household questionnaire which was administered to the household head or any other responsible member of the household. 2. A questionnaire for individual women administered in each household to all women age 15-49 years. 3. An under-5 questionnaire administered to mothers (or caretakers) for all children under-5 years living in the household.

Cleaning operations

Data were entered into the computers using the Census and Surveys Processing System (CSPro) software package, Version 5.0. Data entry was done by a trained team of 14 data entry operators, one archivist/system administrator and one data entry supervisor. For quality assurance purposes, all questionnaires were double-entered and internal consistency checks performed.

Procedures and standard programs developed under the global MICS program and adapted to the Bungoma County MICS questionnaire were used throughout. Data processing began simultaneously with data collection in November 2013 and was completed in February 2014. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software, Version 21. Model syntax and tabulation plans developed by UNICEF were customized and used for this purpose.

Response rate

Information was collected from a total of 1,246 households representing 95 percent response rate. The composition of these households was 5,983 household members comprising 2,797 males and 3,186 females. The mean household size was 4.8 persons. About 48 percent of the sampled households' population is below 15 years, 48 percent are between age 15-64 years and four percent are age 65 years and above.

Due to data quality issues, data relating to mortality and anthropometric measures were not analyzed and reported. Anthropometric data suffered digit preference for both weight and height, while for mortality, deaths especially among children under-five years were under reported. KDHS 2014 had similar shortcomings.

Sampling error estimates

The sample of respondents selected in the Bungoma County MICS is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data. The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Standard error is the square root of the variance of the estimate. For survey indicators those are means, proportions or ratios, the Taylor series linearization method is used for the estimation of standard errors. For more complex statistics, such as fertility and mortality rates, the Jackknife repeated replication method is used for standard error estimation. - Coefficient of variation (se/r) is the ratio of the standard error to the value (r) of the indicator, and is a measure of the relative sampling error. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling based on the same sample size. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design of the survey is as efficient as a simple random sample for a particular indicator, while a deft value above 1.0 indicates an increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value

Kenya had a Muslim population of roughly 5.6 million people, according to the last country census conducted in 2019. Nearly 50 percent of individuals adhering to Islam lived in the Northern-East counties of Mandera (856.5 thousand people), Garissa (815.8 thousand people), and Wajir (767.3 thousand people). Overall, around 10 percent of Kenya's population identified as Muslim.

Abstract

This report documents demographic characteristics and health conditions of Nairobi City's slum residents based on a representative sample survey of urban informal settlement residents carried out from February to June 2000. The aims of the "Nairobi Cross-sectional Slums Survey (NCSS)" were to determine the magnitude of the general and health problems facing slum residents, and to compare the demographic and health profiles of slum residents to those of residents of other urban and rural areas as depicted in the 1998 Kenya Demographic and Health Survey (KDHS). The NCSS is probably the first comprehensive survey explicitly designed to provide demographic and health indicators for sub-Saharan city slum residents.

Geographic coverage

Informal settlements in Nairobi county, Kenya: Central, Makadara, Kasarani, Embakasi, Pumwani, Westlands, Dagoretti and Kibera

Analysis unit

Individuals and Households

Universe

The survey covered all women aged 15-49 years and adolescent boys and girls aged 12-24 years resident in the househol

Sampling procedure

Based on census enumeration areas used in the 1999 Kenya National Census, a weighted cross-sectional sample was designed that is representative of households in all slum clusters of Nairobi. A two-stage stratified sample design was used. Sample points or enumeration areas (EAs) were selected at the first stage of sampling while households were selected from sampled EAs at the second stage. To generate a sampling frame, the NCSS used all the household listings for Nairobi province from the 1999 census. This listing contains the name of the division, location, sub-location, enumeration area as well as structure number, structure owner, number of dwelling units and use of structure (dwelling, business, dwelling/business). Processing of listing forms and identification of slum EAs were conducted in close collaboration with Central Bureau of Statistics (CBS) staff from both the headquarters and the different locations throughout Nairobi.

Before processing the data to generate a sampling frame, two important activities were undertaken. First, two of the EAs were selected and CBS maps were used to identify structures that were indicated and the name of the structure owner, and to assess the number of dwelling units in the structure. The objective of this exercise was to determine if field teams would be able to find selected structures and dwelling units using the CBS enumeration lists. The second activity sought to validate the completeness of the sampling frame. In this second activity, a random sample of one percent of the slum EAs were selected and a fresh listing of structures and dwelling units in each was conducted. A comparison of these structures and dwelling units with the original listing provided by the CBS showed a difference of only 0.7 percent.

Once the sampling frame was validated for completeness, a database of structures was generated from the listing forms and then expanded using the numbers of dwelling units in a given structure to create a sampling frame based on dwelling units. The frame consisted of 31 locations, with at least one slum enumeration area (EA), 48 sub-locations, 1,364 EAs, 29,895 structures, and 250,620 dwelling units.

The first stage of the sampling procedure yielded 98 EAs, while the second stage produced 5463 households. Since dwelling units were neither numbered nor was information collected on household headship during the listing exercise, a method was devised for identifying selected dwelling units within structures. After identifying the right structure (using the map, the name of the owner, the number of dwelling units, and any other physical landmarks noted on the map), fieldworkers identified the selected dwelling unit by first identifying all dwelling units and then counting from the left until they reach the selected number. A dwelling unit generally refers to one or more rooms occupied by the same household within one structure. Although this often corresponds to a room, a household may reside in more than one room. Interviewers were instructed to identify households occupying more than one room and then to count these as one dwelling unit before numbering and identifying the selected dwelling unit.

In each selected dwelling unit, a household questionnaire schedule was completed to identify household members and visitors who would be eligible for individual interviews. All female household members and visitors who slept in the house the previous night and are aged 12 to 49 years were eligible for individual female interviews while all male members and visitors aged 12 to 24 years old were eligible for male interviews. A full census of all sampled households was also carried out. In total, the NCSS administered interviews to 4564 households, 3256 women of reproductive age (15-49), and 1683 adolescent boys (Table 1.2). The 1,934 adolecent girls (whose results are compared with those for boys) comprise 316 aged 12-14 and 1,1618 aged 15-24. Details of the sample design are given in Appendix A.

• The household response rate is computed as the number of completed household interviews divided by the number of eligible households. For the NCSS, 90% of the sampled households (4856) were eligible (i.e. sampled households minus households that were vacant, destroyed, and where all members were absent).

Sampling deviation

None

Mode of data collection

Face-to-face [f2f]

Research instrument

The NCSS instruments were modified from KDHS instruments. Core sections of the 1998 KDHS were replicated without revision, but the service delivery exposure questions were modified so that questions were more relevant to the urban context. The similarity with the DHS questionnaires permitted direct comparison to national, urban, rural, and Nairobi-city results derived from the 1998 KDHS. The fact that the NCSS was carried out less than two years following the DHS ensured that findings were timely enough for useful comparison.

Three instruments were used in this survey: The first one was the household schedule, which enabled us to conduct a full household census from which all eligible respondents were identified. This instrument solicited information on background characteristics of households. The second instrument was for individual women age 12-49, and it had modules on their background and mobility, reproduction, contraception, pregnancy, ante-natal and post-natal care, child immunization and health, marriage, fertility preferences, husband's background and the woman's work and livelihood activities. Information on AIDS and other sexually transmitted infections was also sought, as was information on general and health matters.

The third instrument was the adolescent questionnaire for young women and men age 12-24. The adolescent questionnaire was designed to investigate health, livelihood, and social issues pertaining to adolescents in the slum communities.

NB: All questionnaires and modules are provided as external resources.

Cleaning operations

A total of 49 interviewers (37 women and 12 men), 3 office editors and 4 data-entry clerks were trained for two weeks, from February 17 through March 3, 2000. On the last day of training, the instruments were pre-tested and revised before finalizing them for fieldwork. Fieldwork started on March 5, 2000 and ended on June 4, 2000. Fieldworkers were sent to the field in six teams -each with at least one male interviewer, three or four female interviewers, one supervisor, and a field editor. Three trainees were retained as office editors to edit all questionnaires coming from the field before the questionnaires were sent for data entry.

Response rate

Households : 94.0%

Women (15-49) : 97.0%

Adolescents Girls (12-24): 88.1%

Adolescents Boys (12-24): 91.3%

Key information about Kenya Monthly Earnings

• Kenya Monthly Earnings stood at 540 USD in Jun 2024, compared with the previous figure of 590 USD in Jun 2023
• Kenya Monthly Earnings data is updated yearly, available from Jun 1997 to Jun 2024, with an average number of 430 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

*This indicator will be no longer available from March 2026, 4th onwards. CEIC has a plan to remove the non-standard frequency indicator after the standard frequency indicator has been added and be available on this table. 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 52 million people in Dec 2024
• Unemployment Rate of Kenya increased to 3 % in Dec 2020
• The country's Labour Force Participation Rate dropped to 67 % in Dec 2024