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.

This statistic shows the biggest cities in Kenya as of 2019. In 2019, approximately 4.4 million people lived in Nairobi, making it the biggest city in Kenya.

Population in largest city in Kenya was reported at 5541172 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Kenya KE: Population in Largest City: as % of Urban Population data was reported at 31.985 % in 2017. This records a decrease from the previous number of 32.132 % for 2016. Kenya KE: Population in Largest City: as % of Urban Population data is updated yearly, averaging 35.120 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 50.731 % in 1962 and a record low of 31.985 % in 2017. Kenya KE: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Population in the largest city (% of urban population) in Kenya was reported at 32.68 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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 dataset represents an estimated cumulative travel time/cost (raster grid) accessibility map, for Kenya's major cities . The map is an output of the sub-Saharan African Corridor, Mobile Warehouse Location pilot project. Modeled cities are: Nairobi (7,626,752); Mombasa (1,535,899); Nakuru (610,637); Kisumu (567,963) The calculation of cost/time distance surfaces is based on some assumptions: A. Road travel time/cost is computed for large trucks, it is assumed accessibility for large cargo freight vehicles, tertiary and local traffic roads are not included; B. Lake and river navigation are treated as a surface (polygons) not taking into consideration navigation infrastructure (points). The production of the travel time surfaces followed the steps: rasterization of transportation network vector layers and surfaces; production of cost/time layer; computation of a cumulative cost/time layer from cities (Major Cities Accessibility Map).

Abstract

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Analysis unit

Household Urban center

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya State of the Cities Baseline Survey is aimed to produce reliable estimates of key indicators related to demographic profile, infrastructure access and economic profile for each of the 15 towns and cities based on representative samples, including representative samples of households (HHs) residing in slum and non-slum areas. For this baseline household survey, NORC used a two- or three-stage stratified cluster sampling design within each of the 15 urban centers. Our first-stage sampling frame was based on the 2009 census frame of enumeration areas. For each of the 15 towns and cities, NORC received the sampling frame of EAs from the Kenya National Bureau of Statistics (KNBS). In the first stage, NORC selected a sample of enumeration areas (PSUs). The second stage involved a random selection of households (SSUs) from each selected EA. In order to manage the field interviewing efficiently, we drew a fixed number of HHs from each selected EA, irrespective of EA size. The third stage arose in instances of very large EAs (EAs containing more than 200 households) in which EAs were divided into 2, 3 or 4 segments, from which one segment was selected randomly for household selection.

Stratification of Enumeration Areas: A few stratification factors were available for stratifying the EAs to help to achieve the survey objectives. As mentioned earlier, for this baseline survey we wanted to draw representative samples from slum and non-slum areas and also to include poor/non-poor households (HHs). For the 2009 census, depending on the location, KNBS divided the EAs into three categories: rural, urban, and peri-urban.

Although there is a clear distinction of EAs into slum and non-slum areas, it is hard to classify EAs into poor and non-poor categories. To guarantee enough representation of HHs living in slum and non-slum areas (also referred to as formal and informal areas) as well as HHs living below and above the poverty line, NORC stratified the first-stage sampling units (EAs) into strata, based on EA type (3 types) and settlement type (2 types). Given the resources available, we believe this stratification would serve our purpose as HHs living in slum and in rural areas tend to be poor. Table 1 in Appendix C of final Overview Report (provided under the Related Materials tab) presents the allocation of sampled EAs across the strata for each of the 15 cities in the baseline survey.

Sampling households is not as straightforward as the first-stage sampling of EAs, since the 2009 census frame of HHs does not exist. In the absence of a household sampling frame, NORC carried out a listing of HHs within each EA selected in the first stage. Trained listers, accompanied by local cluster guides (local residents with some form of authority in the EA), systematically listed all households in each selected EA, gathering the address, names of head of household and spouse, household description, latitude and longitude. To ensure completeness of listing data, avoid duplication and improve ease of locating households that were eventually selected for interview, listers enumerated households by chalking household identification number above the household doorway (an accepted practice for national surveys). The sampling frame of HHs produced from the listing activity was, therefore, up-to-date and included new formal and informal settlements that appeared after the 2009 census.

For adequate representativeness and to manage the interviewing task efficiently, NORC planned seven completed household interviews per EA. The final recommended sample size for the Kenya State of the Cities baseline survey is found in Table 2 in Appendix C of the final Overview Report.

Because the expected response rate was unknown prior to the start of the field period, the sampling team randomly selected ten households per enumeration area and distributed them to the interviewers working within the EA. Interviewing teams were instructed to complete at least seven interviews per EA from among the ten selected households. Interviewers were instructed to attempt at least three contacts with each selected household, approaching potential respondents on different days of the week and different times of day. Table 2 presents the final number of EAs listed per city and the final number of completed interviews per city. The table also presents the percent of planned EAs and interviews that were completed vs. planned. Please note that in several cities more interviews were completed than planned. As part of NORC's data quality plan, data collection teams were instructed to overshoot slightly the target of seven interviews per EA, if feasible, to mitigate any potential loss of cases due to poor quality or uncooperative respondents. Few cases were lost due to poor quality, therefore the target number of interviews remains over 100 percent in ten of the fifteen cities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by World Bank staff with input from stakeholders in the Kenya Municipal Program and NORC researchers and survey methodologists. The base questionnaire for the project was a 2004 World Bank survey of Nairobi slums. However, an extended iterative review process led to many changes in the questionnaire. The final version that was used for programming provided under the Related Materials tab, and in Volume II of the Overview.

The questionnaire’s topical coverage is indicated by the titles of its nine modules: 1. Demographics and household composition 2. Security of housing, land and tenure 3. Housing and settlement profile 4. Economic profile 5. Infrastructure services 6. Health 7. Household enterprises7 8. Civil participation and respondent tracking

Response rate

The completion rate is reported as the number of households that successfully completed an interview over the total number of households selected for the EA. These are shown by city in Table 5 in Appendix C of the final Overview Report, and have an average rate of 68.66 percent, with variation from 66 to 74 percent (aside from Nairobi at 61.47 percent and Machakos at 56 percent). As described earlier, ten households were selected per EA if the EA contained more than 10 households. For EAs where fewer than ten households were selected for interviews, all households were selected. In some EAs, more than ten households were selected due to a central office error.

Lagos, in Nigeria, ranked as the most populated city in Africa as of 2024, with an estimated population of roughly nine million inhabitants living in the city proper. Kinshasa, in Congo, and Cairo, in Egypt, followed with some 7.8 million and 7.7 million dwellers. Among the 15 largest cities in the continent, another two, Kano, and Ibadan, were located in Nigeria, the most populated country in Africa. Population density trends in Africa As of 2022, Africa exhibited a population density of 48.3 individuals per square kilometer. At the beginning of 2000, the population density across the continent has experienced a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 54 by the year 2027. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 640 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 44 percent in 2021. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating 35 percent of the total population. This trajectory is projected to continue its ascent in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2021, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. In 2023, Africa's population was estimated to expand by 2.35 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.45 percent, reaching its pinnacle at 2.59 percent between 2012 and 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Abstract

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

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

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

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

Geographic coverage

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

Analysis unit

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

Universe

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

Sampling procedure

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

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

Mode of data collection

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

Research instrument

Five survey questionnaires were administered at household level:

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

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

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

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

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

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

Abstract

The Mombasa Informal Settlement Survey 2009 is a representative sample survey drawn using the informal settlement classification of 1999 Census Enumeration Areas (EAs) as the sample frame. The classification of 1999 Census EAs was carried out in major cities of Kenya by the Kenya National Bureau of Statistics (KNBS) under a project funded by United Nations Environment Program (UNEP) in 2003. The 45 EAs were sampled using the probability proportional to size sampling methodology, and information from a total of 1,080 households were collected using structured questionnaires. The Mombasa informal settlement survey is one of the largest household sample surveys ever conducted exclusively for the informal settlements in Mombasa district.

The survey used a two-stage design. In the first stage, EAs were selected and in the second stage households were selected circular systematically using a random start from the list of households. The data was collected by three teams comprising of six members each (one supervisor, one editor, one measurer and three investigators).

The objective of the Mombasa Informal Settlement Survey 2009 is to provide estimates relating to the wellbeing of children and women living in the informal settlements of Mombasa, to create baseline information and to enable policymakers, planners, researchers, and program managers to take actions based on credible evidence. In Mombasa Informal Settlement Survey 2009, information on specific areas such as reproductive health, child mortality, child health, nutrition, child protection, childhood development, water and sanitation, hand washing practices, education, and HIV/AIDS and orphans were collected. The results indicate that the conditions of people living in the informal settlements are very poor and need immediate attention.

Geographic coverage

Mombasa district

Analysis unit

• individuals,
• households.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, 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 Mombasa Informal Settlement Survey, Kenya (MICS4) was to produce statistically reliable estimates of development indicators related to children and women living in the informal settlements of Mombasa. A two-stage cluster sampling approach was used for the selection of the survey sample.

The target sample size for the Mombasa Informal Settlement Survey was calculated as 1,080 households. For the calculation of the sample size, the key indicator used was proportion of institutional deliveries.

The resulting number of households from this exercise was 1,074 households which is the sample size needed, however, it was decided to cover 1,080 households. The average cluster size was determined as 24 households, based on a number of considerations, including the budget available, and the time that would be needed per team to complete one cluster. This implies a total of 45 clusters for the Mombasa informal settlement survey.

The sampling procedures are more fully described in "Kenya Mombasa Informal Settlements Multiple Indicator Cluster Survey 2009 - Report" pp.95-96.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes Household Listing, Education, Water and Sanitation, Indoor Residual Spraying, Insecticide Treated Mosquito Nets (ITN), Children Orphaned & Made Vulnerable By HIV/AIDS, Child Labour, Child Discipline, Disability, Handwashing Facility, and Salt Iodization.

In addition to a household questionnaire, the Questionnaire for Individual Women was administered to all women aged 15-49 years living in the households. The women's questionnaire includes Child Mortality, Birth history, Tetanus Toxoid, Maternal and Newborn Health, Marriage/Union, Contraception, Attitude towards Domestic Violence, Female Genital Mutilation/Cutting, Sexual Behaviour and HIV/AIDS.

The Questionnaire for Children Under-Five was administered to mothers or caretakers of children under 5 years of age living in the households. The children's questionnaire includes Birth Registration and Early Learning, Childhood Development, Vitamin A, Breastfeeding, Care of Illness, Malaria, Immunization, and Anthropometry.

Cleaning operations

Data were entered using the CSPro software. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed, and the whole process was monitored initially by the MICS Global data processing specialist, followed by KNBS data processing expert. Procedures and standard programs developed under the global MICS project and adapted to the modified questionnaire were used throughout. Data entry began simultaneously with data collection in February 2009 and was completed at the end of March 2009. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, and the model syntax and tabulation plans developed by UNICEF were customized for this purpose.

Response rate

Of the 1,080 households selected for the sample, 1,076 were found occupied. Of these, 1,016 were successfully interviewed yielding a household response rate of 94.4 percent. In the interviewed households, 878 women (age 15-49) were identified and information collected from 821 women in these households, yielding a response rate of 93.5 percent. In addition, 464 children under age five were listed in the household questionnaire, and information on 454 children were obtained, which corresponds to a response rate of 97.8 percent. Overall response rates of 88.3 and 92.4 are calculated for the women's and under-5's interviews respectively.

Sampling error estimates

Sampling errors are a measure of the variability between all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey results.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator. - 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. The square root of the design effect (deft) is used to show the efficiency of the sample design. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the 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. For any given statistic calculated from the survey, the value of that statistics will fall within a range of plus or minus two times the standard error (p + 2.se or p - 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from the survey data, SPSS Version 17 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and un-weighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest. Three of the selected indicators are based on households, 10 are based on household members, 14 are based on women, and 14 are based on children under 5. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of household population
• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed under-5s
• Age distribution of under-five children
• Heaping on ages and periods
• Completeness of reporting
• Presence of mother in the household and the person interviewed for the under-5 questionnaire
• School attendance by single age
• Sex ratio at birth among children ever born and living
• Distribution of women by time since last birth

The results of each of these data quality tables are shown in appendix D in document "Kenya Mombasa Informal Settlements Multiple Indicator Cluster Survey 2009 - Report" pp.102-109.

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

The STEP (Skills Toward Employment and Productivity) Measurement program is the first ever initiative to generate internationally comparable data on skills available in developing countries. The program implements standardized surveys to gather information on the supply and distribution of skills and the demand for skills in labor market of low-income countries.

The uniquely-designed Household Survey includes modules that measure the cognitive skills (reading, writing and numeracy), socio-emotional skills (personality, behavior and preferences) and job-specific skills (subset of transversal skills with direct job relevance) of a representative sample of adults aged 15 to 64 living in urban areas, whether they work or not. The cognitive skills module also incorporates a direct assessment of reading literacy based on the Survey of Adults Skills instruments. Modules also gather information about family, health and language.

Geographic coverage

• The STEP target population is the urban population aged 15 to 64 (inclusive).

Analysis unit

The units of analysis are the individual respondents and households. A household roster is undertaken at the start of the survey and the individual respondent is randomly selected among all household members aged 15 to 64 included. The random selection process was designed by the STEP team and compliance with the procedure is carefully monitored during fieldwork.

Universe

The target population is defined as all non-institutionalized persons aged 15 to 64 (inclusive) living in private dwellings in the urban areas of the country at the time of the data collection. This includes all residents, except foreign diplomats and non-nationals working for international organizations
The following are considered "institutionalized" and excluded from the STEP survey:
- Residents of institutions (prisons, hospitals, etc)
- Residents of senior homes and hospices
- Residents of other group dwellings such as college dormitories, halfway homes, workers' quarters, etc

Other acceptable exclusions are:
- Persons living outside the country at the time of data collection, e.g., students at foreign universities
Deviation Requested from the Standard: The statistical population is composed of core urban households and excludes the categories identified here, as well as itinerants (as classified in the Population Census 2009 in Kenya).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size was 3894 households. The Kenya sample design is a stratified 3 stage sample design. The sample was stratified by 4 geographic areas: 1-Nairobi, 2-Other Large Cities (over 100,000 households), 3- Medium cities (60,000 to 100,000 HHs), and 4-Other Urban Areas. For detailed description of the sample design and sampling methodologies, refer to Part 3 of the National Survey Design Planning Report (NSDPR) as well as the STEP Survey Weighting Procedures Summary. Both documents are provided as external resources.

Sampling deviation

War marred and unstable regions of Kenya were excluded from the survey. Itinerants (as classified in the Population Census 2009 in Kenya) were also excluded.

Mode of data collection

Face-to-face [f2f]

Research instrument

The STEP survey instruments include: (i) A Background Questionnaire developed by the WB STEP team. (ii) A Reading Literacy Assessment developed by Educational Testing Services (ETS).

All countries adapted and translated both instruments following the STEP Technical Standards: 2 independent translators adapted and translated the Background Questionnaire and Reading Literacy Assessment, while reconciliation was carried out by a third translator. In Kenya the section of the questionnaire assessing behavior and personality traits (Module 6) was translated into Swahili to adapt to respondents' language preferences, so that the respondent could choose to answer in either English or Swahili.
- The survey instruments were both piloted as part of the survey pretest. - The adapted Background Questionnaires are provided in English as external resources. The Reading Literacy Assessment is protected by copyright and will not be published.

Cleaning operations

EEC Canada Inc. was responsible for data entry and processing.

The STEP Data management process is as follows:

1. Raw data is sent by the survey firm
2. The WB STEP team runs data checks on the Background Questionnaire data.
   • ETS runs data checks on the Reading Literacy Assessment data.
   • Comments and questions are sent back to the survey firm.
3. The survey firm reviews comments and questions. When a data entry error is identified, the survey firm corrects the data.
4. The WB STEP team and ETS check the data files are clean. This might require additional iterations with the survey firm.
5. Once the data has been checked and cleaned, the WB STEP team computes the weights. Weights are computed by the STEP team to ensure consistency across sampling methodologies.
6. ETS scales the Reading Literacy Assessment data.
7. The WB STEP team merges the Background Questionnaire data with the Reading Literacy Assessment data and computes derived variables.

Response rate

An overall response rate of 91.8% was achieved in the Kenya STEP Survey. Table 21 of the STEP Survey Weighting Procedures Summary provides the detailed percentage distribution by final status code.

Nakuru is a city in the Rift Valley region of Kenya. It is the capital of Nakuru County, and is the third largest city in Kenya.

Mean body and leg titers for Aedes aegypti from three major cities in Kenya exposed to chikungunya virus.

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

Abstract

The Kenya Innovation Survey is a national innovation survey undertaken from March to June 2012. The survey was designed to measure the innovation activity based on a set of core indicators to inform policies that will help the country configure the national system of innovation in order to respond to socio-economic challenges. The survey was based on the Oslo Manual by OECD. The survey covered Nairobi, Mombasa, Kisumu, Nakuru and Eldoret.

This innovation survey, the first in Kenya, was carried out in order to generate crucial learning lessons to inform the planning of the main national innovation survey to be undertaken at a later date. However, the overall objective of the innovation survey, being part of the national ST&I system of indicators that is under development, is to build Kenya’s capacity to develop and use innovation indicators in designing and implementing ST&I policies and strategies for national development.

The survey is therefore an attempt to probe the activity of innovation through the collection of data on various aspects of innovation in order to develop relevant innovation indicators and specific innovation policies for the country. These indicators will then enable key stakeholders to understand the state of the national innovation system and its capacity to deliver the intended results so as to address the components that need attention.

The innovation survey is designed to: • Develop and cause the adoption of internationally comparable innovation indicators; • Build human and institutional capacities to collect innovation indicators; • Inform the country on the state of innovation; and • Provide both qualitative and quantitative data on innovation at firm level.

Geographic coverage

Firms in Major Towns of Kenya (urban)

Mombasa City, Nakuru Town, Eldoret Town and Kisumu City

Analysis unit

• Firms
• Establishment

Universe

Selected towns

Kind of data

Aggregate data [agg]

Sampling procedure

The sample frame consisted of all registered firms, public/private universities and public research institutions, national polytechnics and NGOs. The firms were randomly selected by ISIC sector from the frame. A total of 194 firms were selected in Nairobi and its environs while 102 firms were selected upcountry as follows: Mombasa (25 firms), Kisumu (25 firms), Eldoret (24 firms) and Nakuru (25 firms).

Mode of data collection

Other [oth]

Research instrument

The questionnaire was divided into eleven parts as follows: • Part 1: General information of the firm • Part 2: Product (goods or services) innovation • Part 3: Process innovation • Part 4: Ongoing or abandoned Innovation activities • Part 5: Performed innovation activities and expenditures • Part 6: Sources of information and co-operation for innovation activities • Part 7: Effects / Objectives of innovation • Part 8: Factors hampering innovation activities • Part 9: Intellectual property rights • Part 10: Organizational and marketing innovation • Part 11: Specific innovations

NOTE: The full questionnaire is attached in the external resource

Cleaning operations

In this survey, data processing personnel were drawn from the Kenya National Bureau of Statistics assisted by some officers from the Ministry of Higher Education, Science and Technology. The questionnaires were received from the field, recorded and edited in preparation for data capture.

Data processing and analysis were done at the Kenya National Bureau of Statistics. The Census and Survey Software Programme (CSPro) was used for data capture,editing, validation and tabulation. In developing the data capture system, certain controls were in-built to check the characters entered afterwhich validation was done in preparation for the production of frequency tables and in readiness for data analysis.

Response rate

The Innovation Survey covered business firms in Nairobi, Mombasa, Kisumu, Nakuru and Eldoret. A total of 293 firms were targeted in this innovation survey. Out of these, 160 firms completed and returned the questionnaires, thus representing a 54.6 percent overall response rate. The different regions response rate are listed as follows: Nairobi - 43.3% Mombasa - 68.0% Kisumu - 60.0% Nakuru - 92.0% Eldoret - 87.5% Total - 54.6%

Levels of household food insecurity in Nairobi by household structure.

Abstract

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

Geographic coverage

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

Analysis unit

Individual

Universe

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

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

Kind of data

Event history data

Frequency of data collection

Three rounds in a year

Sampling procedure

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

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

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

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

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

Cleaning operations

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

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

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

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

Response rate

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

Sampling error estimates

Not applicable for surveillance data

Data appraisal

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