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.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Kenya data available from WorldPop here.

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.

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.

Total population in Nairobi Kenya, 2021

This datasets shows the total formal and informal population settlements in Nairobi and its environs.

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.

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.

Kenya had over 12 million households according to the last census done in 2019. The majority, some 7.4 million, lived in urban areas, while 4.7 million dwelled in rural zones. Nairobi City was the county with more households, approximately 1.5 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.

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.

3.138.369 (Persons) in 2009.

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 2022 Kenya Demographic and Health Survey (2022 KDHS) was implemented by the Kenya National Bureau of Statistics (KNBS) in collaboration with the Ministry of Health (MoH) and other stakeholders. The survey is the 7th KDHS implemented in the country.

The primary objective of the 2022 KDHS is to provide up-to-date estimates of basic sociodemographic, nutrition and health indicators. Specifically, the 2022 KDHS collected information on: • Fertility levels and contraceptive prevalence • Childhood mortality • Maternal and child health • Early Childhood Development Index (ECDI) • Anthropometric measures for children, women, and men • Children’s nutrition • Woman’s dietary diversity • Knowledge and behaviour related to the transmission of HIV and other sexually transmitted diseases • Noncommunicable diseases and other health issues • Extent and pattern of gender-based violence • Female genital mutilation.

The information collected in the 2022 KDHS will assist policymakers and programme managers in monitoring, evaluating, and designing programmes and strategies for improving the health of Kenya’s population. The 2022 KDHS also provides indicators relevant to monitoring the Sustainable Development Goals (SDGs) for Kenya, as well as indicators relevant for monitoring national and subnational development agendas such as the Kenya Vision 2030, Medium Term Plans (MTPs), and County Integrated Development Plans (CIDPs).

Geographic coverage

National coverage

Analysis unit

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

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men ageed 15-54, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

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 uses to conduct household-based sample surveys in Kenya. The frame is based on the 2019 Kenya Population and Housing Census (KPHC) data, in which 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 randomised into four equal subsamples. A survey can utilise a subsample or a combination of subsamples based on the sample size requirements. The 2022 KDHS sample was drawn from subsample one of the K-HMSF. The EAs were developed into clusters through a process of household listing and geo-referencing. The Constitution of Kenya 2010 established a devolved system of government in which Kenya is divided into 47 counties. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, which resulted 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 separately, and, for some indicators, at the county level. The sample size was computed at 42,300 households, with 25 households selected per cluster, which resulted in 1,692 clusters spread across the country, 1,026 clusters in rural areas, and 666 in urban areas. The sample was allocated to the different sampling strata using power allocation to enable comparability of county estimates.

The 2022 KDHS employed a two-stage stratified sample design where in the first stage, 1,692 clusters were selected from the K-HMSF using the Equal Probability Selection Method (EPSEM). The clusters were selected independently in each sampling stratum. Household listing was carried out in all the selected clusters, and the resulting list of households served as a sampling frame for the second stage of selection, where 25 households were selected from each cluster. However, after the household listing procedure, it was found that some clusters had fewer than 25 households; therefore, all households from these clusters were selected into the sample. This resulted in 42,022 households being sampled for the 2022 KDHS. Interviews were conducted only in the pre-selected households and clusters; no replacement of the preselected units was allowed during the survey data collection stages.

For further details on sample design, see APPENDIX A of the survey report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used in the 2022 KDHS: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Kenya. In addition, a self-administered Fieldworker Questionnaire was used to collect information about the survey’s fieldworkers.

Cleaning operations

CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed with a mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, Serpro S.A., and The DHS Program. Programming of questionnaires into the Android application was done by ICF, while configuration of tablets was completed by KNBS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data.

Work was assigned by supervisors and shared via Bluetooth® to interviewers’ tablets. After completion, assigned work was shared with supervisors, who conducted initial data consistency checks and edits and then submitted data to the central servers hosted at KNBS via SyncCloud. 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 identify any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the KNBS and ICF 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 survey, 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. Of these, 32,156 women were interviewed, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were similar (95%). In the households selected for the men’s 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%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Kenya Demographic and Health Survey (2022 KDHS) to minimise this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 KDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 KDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2022 KDHS is a SAS program. This program used the Taylor linearisation method for variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data

Kenya recorded 102.2 male births per 100 female births in 2021. The country's gender ratio kept stable since 2011, after increasing from 101.9 in 2000.

Poverty entails more than a scarcity of material resources—it also involves a shortage of time. To examine the causal benefits of reducing time poverty, we conducted a longitudinal field experiment over six consecutive weeks in an urban slum in Kenya with a sample of working mothers, a population who is especially likely to experience severe time poverty. Participants received vouchers for services designed to reduce their burden of unpaid labor. We compared the effect of these vouchers against equivalently valued unconditional cash transfers (UCTs) and a neutral control condition. Participants completed a detailed survey. As part of this survey, they provided contact information and then they completed a series of subjective well-being measures from prior research. Specifically, participants answered questions about their overall subjective well-being and their positive and negative emotions. Participants then completed demographic items including their gender, age, marital status, whether they were currently living with their partner, or were the head of the household. They also reported on the highest level of formal education they had completed, the number of children they had living at home, their current childcare status, and they answered a series of income questions including how many people they financially supported, how many people relied on their income. Third, they answered a series of employment questions including whether they currently worked for pay, how many jobs they worked, what kinds of jobs they worked in, how much money they earned per month, how their earnings and employment status had changed during the COVID-19 pandemic, and whether they were currently looking for new employment opportunities and why. Fourth, respondents answered questions about the earnings of their household members and the amount of savings and debt that they had, and how these estimates had changed during the COVID-19 pandemic. Participants also reported how many hours they spent on unpaid labor in the past 7 days, and whether they had experienced any of the negative impacts of COVID-19 for their own health and their concerns with COVID-19 exposure. Participants also reported how valuable they felt their time was on a series of different measures. Lastly, participants reported how much money they expected to earn in the next six months as well as their predictions for their expenses over the next 6 months.

Abstract

This research of registered businesses with one to four employees was conducted in Kenya between April 2013 and January 2014, at the same time with Kenya Enterprise Survey 2013. Data from 360 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The objective of the survey was to obtain feedback from enterprises on the state of the private sector and constraints to its growth.

Micro-Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Central, Nyanza, Mombasa, Nairobi, and Nakuru regions

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Ethiopia was selected using stratified random sampling. Two levels of stratification were used: firm sector and geographic region.

For industry stratification, the universe was divided into four manufacturing industries (food, textiles and garments, chemicals and plastics, other manufacturing) and two service sectors (retail and other services).

Regional stratification was defined in five regions: Central, Nyanza, Mombasa, Nairobi, and Nakuru.

2012 Census of Business Establishments of the Kenya National Bureau of Statistics was used as a sample frame for the survey of micro firms.

The enumerated establishments with less than five employees (micro establishments) were used as sample frame for the Kenya micro survey with the aim of obtaining interviews at 360 establishments.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 5.2% (39 out of 756) for micro firms.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

There is a skip pattern in the Service Module Questionnaire for questions that apply only to retail firms.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times, days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

Although vaccination is a cost-effective, equitable, and impactful public health intervention in curbing the spread of infectious disease, low uptake is a significant concern, especially among high-risk population groups. Nearly half of the population is unvaccinated in Nairobi, yet there is a shortage of vaccination information on vulnerable tuberculosis (TB) patients. The interplay of factors influences uptake, and protecting this vulnerable group and the general population from severe disease, hospitalization, and deaths is worthy. The purpose of this study is to determine the prevalence and individual-level enablers and barriers to COVID-19 vaccine uptake among adult TB patients attending selected clinics in Nairobi County, Kenya. This cross-sectional mixed-method study was conducted at TB clinics across six sub-counties in Nairobi County. It included 388 participants sampled from each clinic’s TB register. Quantitative data was collected using a questionnaire, and qualitative data was collected through key informant interviews and focus group discussions. Quantitative data was analyzed using descriptive statistics (frequencies and percentages for categorical variables and mean standard deviation for continuous variables) and inferential statistics (logistic regression). Qualitative data was analyzed through deductive coding and thematic analysis. The prevalence of COVID-19 vaccination was 46.1%, with 38.1% receiving complete vaccination. Mistrust in vaccine management (adjusted odds ratio (aOR)= 0.075, 95% confidence interval (CI): 0.025-0.229, p <0.001) was a significant barrier to COVID-19 vaccine uptake. Perceived covid-19 susceptibility (aOR = 2.901, 95% CI: 1.258-6.688, p = 0.012) and perceived covid-19 seriousness (aOR = 3.294, 95% CI: 1.130-9.604, p = 0.029) were significant enablers of COVID-19 vaccine uptake. Qualitative themes related to individual-level barriers and enablers of COVID-19 vaccine uptake were fear of side effects, stigma, myths, and mistrust in the messaging for barriers and desire to protect others and risk perception as enablers. The study revealed critical individual-level factors related to COVID-19 vaccine uptake. Methods Design and setting The study was an analytical cross-sectional study based on a mixed method (quantitative and qualitative) approach. Research assistants started with collecting quantitative data and later assisted investigators in conducting the interviews. The study was conducted at six sites in Nairobi County, Kenya. Sample and sampling The study targeted adult TB patients receiving treatment between certain months in 2023, EPI logisticians, and TB coordinators. Those eligible consented participants were helped to fill out the electronic questionnaires and focus group discussions (FGD) with different participants later. The EPI logisticians and TB coordinators were the main targets for key informant interviews. The study excluded newly diagnosed TB patients or those initiated on TB treatment on the day of data collection, patients who refused or were unable to state their vaccination status, and patients who were too unwell to participate. The Cochran formula was used to calculate the sample size. Proportion sampling (PPS) was used to allocate proportions to health facilities in Nairobi County. Purposive sampling was employed to identify facilities with high TB patient numbers across the six sub-counties. Proportion sampling (PPS) was used to allocate proportions to each health facility. Simple random sampling was used to select participants for the FGDs, and purposive sampling for the Key Informant Interview (KII). Data collection tools A semi-structured electronic questionnaire was used to collect quantitative data. It had sections A (social demographic information and vaccination details) and B (individual-level factors and COVID-19 vaccine uptake). Pre-testing of the tool was done using 12 TB patients at [redacted] which assisted in the re-ordering of questions, replacement of ambiguous words, and kobo installations on research assistants' phones. This site had population characteristics similar to those of the study sites. A KII guide and an FGD guide were used to collect qualitative data. Both guides have open-ended questions focusing on themes related to the study objectives. Data collection Eight research assistants administered the semi-structured electronic questionnaire face-to-face with participants during their clinic visits. Two research assistants were allocated to [redacted] and [redacted] because of the number of participants, and one research assistant was assigned to the other four facilities. Participants provided informed consent before answering the questionnaire. Each research assistant had their smartphone with the questionnaire accessed via the Kobo collect application and responses were entered as provided by the participants. Six KIIs were conducted with managers and leaders in charge of the selected TB clinics, and six FGDs were held with adult TB patients from the six TB clinics. A series of open-ended questions guided the KIIs and FGDs. The interviews and FDGs took between 20-60 minutes and were audio-recorded. The verbatim recordings were manually transcribed and organized for a thematic analysis approach. The results of the interviews were triangulated with quantitative data.

Abstract

Kenya hosts over half a million refugees, who, along with their hosts in urban and camp areas, face difficult living conditions and limited socioeconomic opportunities. Most refugees in Kenya live in camps located in the impoverished counties of Turkana (40 percent) and Garissa (44 percent), while 16 percent inhabit urban areas—mainly in Nairobi but also in Mombasa and Nakuru.

Refugees in Kenya are not systematically included in national surveys, creating a lack of comparable socioeconomic data on camp-based and urban refugees, and their hosts. As the third of a series of surveys focusing on closing this gap, this Socioeconomic Survey of Urban Refugees's aim is to understand the socioeconomic needs of urban refugees in Kenya, especially in the face of ongoing conflicts, environmental hazards, and others shocks, as well as the recent government announcement to close Kenya’s refugee camps, which highlights the potential move of refugees from camps into urban settings.

The SESs are representative of urban refugees and camp-based refugees in Turkana County. For the Kalobeyei 2018 and Urban 2020–21 SESs, households were randomly selected from the UNHCR registration database (proGres), while a complete list of dwellings, obtained from UNHCR’s dwelling mapping exercise, was used to draw the sample for the Kakuma 2019 SES. The Kalobeyei SES and Kakuma SES were done via Computer-Assisted Personal Interviews (CAPI). Due to COVID-19 social distancing measures, the Urban SES was collected via Computer Assisted Telephone Interviewing (CATI). The Kalobeyei SES covers 6,004 households; the Kakuma SES covers 2,127 households; and the Urban SES covers 2,438 households in Nairobi, Nakuru, and Mombasa.

Questionnaires are aligned with national household survey instruments, while additional modules are added to explore refugee-specific dynamics. The SES includes modules on demographics, household characteristics, assets, employment, education, consumption, and expenditure, which are aligned with the Kenya Integrated Household Budget Survey (KIHBS) 2015–16 and the recent Kenya Continuous Household Survey (KCHS) 2019.

Additional modules on access to services, vulnerabilities, social cohesion, mechanisms for coping with lack of food, displacement trajectories, and durable solutions are administered to capture refugee-specific challenges.

Geographic coverage

Nairobi, Mombasa, Nakuru

Analysis unit

Households and individuals

Universe

All refugees registered with UNHCR via ProGres, verified via the Verification Exercise conducted in 2021

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was conducted using the UNHCR proGres data as the sampling frame. Due to the COVID-19 lockdown, the survey data was collected via telephone. Hence, the survey is representative of households with active phone numbers registered by UNHCR in urban Kenya – Nairobi, Mombasa and Nakuru. A sample size of 2,500 was needed to ensure a margin of error of less than 5 percent at a confidence level of 95 percent for groups represented by at least 50 percent of the population.

The sample for the urban SES is designed to estimate socioeconomic indicators, such as food insecurity, for groups whose share represents at least 50 percent of the population. Considering the total urban refugee population as of August 2020 and the proportions of main countries of origin, as well as a 10 percent nonresponse rate, the target sample size is 2,500 households in total, with 1,250 in Nairobi, 700 in Nakuru, and 550 in Mombasa. A total of 2,438 households were reached: 1,300 in Nairobi, 409 in Nakuru, and 729 in Mombasa.

The units in ProGres list are UNHCR proGres families, which are different from households as defined in standard household surveys. Upon registration, UNHCR groups individuals into ‘proGres’ families which do not necessarily meet the criteria to be considered a household. A proGres family is usually comprised by no more than one household. In turn, a household can be integrated by one or more proGres families.

Households were selected as the unit of observation to ensure comparability with national household surveys. Households are a set of related or unrelated people (either sharing the same dwelling or not) who pool ration cards and regularly cook and eat together. As proGres families were sampled, the identification of households was done by an introductory section that confirms that each member of the selected proGres family is a member of the household and whether there are other members in the households that belong to other ProGres families. Thus, the introductory section documents the number of proGres families present in the household under observation.

Before selecting the survey strata, the team attempted to better understand the type of bias observed by focusing on refugees with access to phones. From the proGres data, phone penetration in urban areas is high (Nairobi and Mombasa: 93 percent, Nakuru: 95 percent). To understand the type of bias observed by focusing on refugees with access to phone, we looked at socio-economic outcomes for proGres family refugees with access to a phone number and those without

Mode of data collection

Computer Assisted Telephone Interview [cati]