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.

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

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.

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

This study covers Nairobi, one of four African cities surved between 2013 and 2019 by the African Center for Cities. The African Center for cities is based at the University of Cape Town and is a partner of the Hungry Cities Partnership (HCP).

The HCP studies include household data on food insecurity, household food purchasing dynamics, nutritional discounting taking place in households, foods consumed and multidimensional measures of poverty. The household data is complimented with household member data and food retailer (vendor) data, including infomation on vendor employees.

The Hungry Cities Partnership is an international network of cities and city-based partner organizations which focuses on the relationships between rapid urbanization, informality, inclusive growth and urban food systems in the Global South.

Geographic coverage

The household sample aims to be representative of the city of Nairobi.

Analysis unit

Households and individuals

Universe

Households and Vendors in Nairobi.

Kind of data

Sample survey data

Sampling procedure

Household: The survey report explains a two-stage sampling process. Firstly, 23 of the 111 administrative "sub-locations" in Nairobi were selected. 3 were selected randomly from each of the 8 divisions, excluding Kasarani which only had 2 selected. Then for the second stage, households were selected randomly from these 23 administrative locations. The number of households selected was proportional to the size (measured in households) of the administrative location, a "portional-to-population" strategy that was also employed in the Maputo household survey from 2014.

Vendor: 1267 food vendors were interviewed across Nairobi. The documentation sounds like a two-stage process was also followed. It states that at least 3 neighbourhoods were randomly selected in each of the eight administrative divisoins. The documentation states that vendors were randomly selected in the second stage, but then adds the following "respondents were randomly selected depending on the form and density of the location of business enterprises in the residential neighbourhood, category of food vendor, types of food items sold by the vendor, as well as willingness to participate in the survey." It is as such not clear to what extent this process was random.

Sampling deviation

Due to security reasons, the survey could only take place during daylight hours and as such the study may have missed businesses operating solely at night.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two questionnaires per city, a household questionnaire and a vendor questionnaire. The household questionnaire has a subsection for household members (persons), and the vendor quesitonnaire has a subsection for employees. Answers to these subsections are supplied in separete datafiles, which can be matched to (merged with) the questoinnaire as necessary.

DataFirst has not received documentation to confirm this, but it is likely that the protocols from the other cities were followed, in that 1) vendor surveys were administered to the person directly responsible for the running of the business using handheld tablets and 2) the household survey was administered to a senior adult member of the household, someone who could speak for the household.

Cleaning operations

Datafiles were received by DataFirst in SPSS (.sav) and Excel (.xlsx) format. Variables had to be named and variable labels were taken from question text. Variables were named accoriding to question number and subject matter, in a hierachical fasion.

An effort was made to keep question numbers and value labels consistent across cities where the same questions were asked for the 2013-2019 surveys. For the vendor data, Cape Town, Maputo and Nairobi had almost identical questionnaires and so the question numbers were naturally the same across these cities (harmonized). For the household data, Maputo, Nairobi and Windhoek were similar and could be harmonized. This means users could try stack these datafiles. This also means that list numbers/value codes might not match the questionnaire for a given city.

Missing values of 97, 98, and 99 were converted to -97, -98 and -99. There were some question numbers wrong in the vendor data questionnaires (typos) that were corrected.

In the household data, the confusingly numbered 10.c and 10.d were renamed to 10b1 and 10b2, to avoid confusion with 10c. and 10d., which were different questions.

Data appraisal

In general the lists change subtly between cities, for example the lists of foods in question 8 of the household data. As such the user should take caution when comparing across cities, and refer to the questionnaires. When the lists differed in a potentially confusing way, list item letters (a-z) were left in the variable name as a second way for the user to check that the data match the questionnaire correctly.

Nairobi has been the Kenyan county most affected by the coronavirus (COVID-19) pandemic. As of March 31, 2022, the capital registered most of the confirmed COVID-19 cases in the country, around 129 thousand. The amount corresponded to nearly 40 percent of the total cases in Kenya. In Kiambu, within the Nairobi Metropolitan Region, 19,778 infected people were registered, whereas Mombasa, Kenya's oldest and second largest city, had 17,794 cases. As of March 2021, Kenya started the vaccination campaign against the coronavirus with doses received through the COVAX initiative.

Kenya's economy rebounds amid vaccination campaign

The coronavirus outbreak had a significant negative impact on Kenya's economy. In the second quarter of 2020, the quarterly country’s GDP decreased by 5.5 percent, the first contraction in recent years. Around one year later, in the third quarter of 2021, Kenya already registered an improved economic performance, with the quarterly GDP growth rate measured at 9.9 percent. The educational sector pushed the result, with an expansion of 65 percent. Mining and quarrying, and accommodation and food services followed, each with a 25 percent growth rate.

Signs of recovery in the tourism sector

Extensively known for its rich nature and wildlife, Kenya felt dramatically the impacts of the COVID-19 pandemic in the tourism industry. The sector's contribution to the country’s GDP roughly halved in 2020, compared to 2019. By the end of 2021, however, signals of recovery were already spotted. The monthly number of arrivals in both Jomo Kenyatta and Moi international airports in December that year corresponded to roughly 70 percent of that registered in December 2019. Additionally, as of March 2022, the bed occupancy rate in Kenyan hotels amounted to 57 percent, against 23 percent in March 2021.

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.

Nigeria has the largest population in Africa. As of 2024, the country counted over 232.6 million individuals, whereas Ethiopia, which ranked second, has around 132 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 116 million people. In terms of inhabitants per square kilometer, Nigeria only ranks seventh, while Mauritius has the highest population density on the whole African continent. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

In Nairobi, Kenya, renting a one bedroom apartment in the city center cost almost as much as a three bedroom apartment outside the city center in June 2023. A three bedroom apartment cost 413 U.S. dollars a month outside the center, while in a central location, a similar apartment was 924 U.S. dollars.

Kampala (Uganda) and Nairobi (Kenya) were some of the African cities with the highest construction costs for hotel buildings in 2023. An average 5-star luxury hotel in Kampala cost approximately 922,000 U.S. dollars per key to construct. In 2021, Nigeria and Ghana were some of the African countries with the highest number of infrastructure and capital projects.