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

This is Kenya population dataset for the census held in 2019. About Kenya 1. The demography of Kenya is monitored by the Kenyan National Bureau of Statistics. 2. Kenya is a multi-ethnic state in the Great Lakes region of East Africa. 3. It is inhabited primarily by Bantu and Nilotic populations, with some Cushitic-speaking ethnic minorities in the north. 4. Its total population was at 47 558,296 as of the 2019 census.

Abstract

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

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

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Households: Yes

Universe

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

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Constructed by census agency.

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

SAMPLE FRACTION: 6%

SAMPLE UNIVERSE: Unknown.

SAMPLE SIZE (person records): 659,310

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on individuals.

Information about the ethnic affiliation(s) and characteristics of a human population. Includes, for example, information about: the ethnic groups located within a geographic region, their community social structures, their mutual associations and conflicts with other groups, their historic roles and influence, and the physical distribution of their members. Ethnic groups are human populations whose members identify with each other, usually on the basis of having a common cultural traditions and heritage (for example: as distinguished by customs, language, religious practices, or common history) or a presumed common genealogy or ancestry.

Christianity is the main religion adopted in Kenya. As of 2019, over ** percent of the population identified as Christians, among which **** percent were Protestants, **** percent Catholics, **** percent Evangelicals, and ***** percent from African Instituted Churches. Furthermore, nearly ** percent of Kenyans were Muslim.

Using unique data from one of Africa's largest informal settlements, the Kibera slum in Nairobi, we provide evidence of ethnic patronage in the determination of rental prices and investments. Slum residents pay higher rents and live in lower quality housing (measured via satellite pictures) when the landlord and the locality chief belong to the same ethnicity. Conversely, rental prices are lower, and investments higher when residents and chiefs are co-ethnics. Our identification relies on the exogenous appointment of chiefs and is supported by several tests, including a regression discontinuity design.

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.

This dissertation examines ethnic patronage, local conflict, and election fraud in Kenya in three separate essays. Fraud, violence, and ethnicity are difficult to measure, and they often play a central role in narratives and theories about African politics. The essays in this dissertation draw on natural language processing, spatial statistics, and demography to improve measurement of these concepts and, in turn, our understanding of how they function in Kenya. The approaches developed here can be generalized to conflict, ethnicity, and fraud in other contexts. The first essay presents a method for extracting ethnic information from names. Existing methods give biased estimates by ignoring uncertainty in the mapping between names and ethnicity. I apply my improved, approximately unbiased method to data on political appointments from 1963 to 2010 in Kenya, and find that existing narratives about distributive politics do not accord with empirical patterns. The second essay examines patterns of violent ethnic targeting during Kenya's 2007-2008 post-election violence. I focus on patterns of arson, one of the key types of violence used in the Rift Valley. I find that incidence of arson is related to the presence of ethnic outsiders, and even more strongly related to measures of land quality, accessibility, and electoral competition. Using a difference-in-differences design, I show that arson caused a significant decrease in the number of Kikuyu and other immigrant ethnic groups registered to vote; no such decline is observed in indigenous ethnic groups. The third essay documents the prevalence of dead voters on Kenya's voter register prior to the contentious 2007 presidential elections, and shows how dead registered voters may have facilitated electoral fraud. Simply accounting for the number of dead voters demonstrates that turnout was greater than 100% in several opposition constituencies, and implausibly high in most of the incumbent president's home province. Ecological inference suggests that ballot-s tuffing occurred in candidate strongholds, rather than competitive constituencies. These results are consistent with the opposition party's allegations of fraud.

Studies of conflict-prone settings claim that political leaders can increase electoral support by appealing to perceived ethnic grievances. Yet there is little empirical research on how appeals to group-based grievances work and the types of voters most likely to respond to such appeals. To explore the political effects of ethnic grievance appeals, we conduct a survey experiment in Kenya’s Rift Valley, a region where a long history of conflict over land has sharpened ethnic tensions. We find that appeals to grievances have surprisingly little effect among most voters. We observe a positive effect only among ethnic “insiders” who feel land insecure, a small share of the sample population. Further, though imprecisely estimated, we show that exposure to prior violence may condition how some individuals respond to the appeals, decreasing support for candidates who employ divisive rhetoric. Finally, the results show that appeals to an ethnic-based land grievance are no more effective than a generic land appeal, indicating that group injustice frames have little effect. From a normative perspective these results are encouraging: they suggest that voters in conflict-prone settings may be less easily swayed by divisive ethnic rhetoric than much of the literature presumes.

aThe HIV testing response rate is defined as the percentage of eligible persons 15–49 years old who participated in HIV testing. Non-responders included those who refused testing, were absent at the time of the survey or there were technical difficulties with blood taking.HIV prevalence and HIV testing response rates in 15–49 year olds by ethnic group in 2003 and 2008 Kenyan Demographic and Health Surveys.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Abstract (en): The Afrobarometer project was designed to collect and disseminate information regarding Africans' views on democracy, governance, economic reform, civil society, and quality of life. This particular survey was concerned with the attitudes and opinions of the citizens of Kenya. Respondents in a face-to-face interview were asked to rate their president and the president's administration in overall performance, to state the most important issues facing their nation, and to evaluate the effectiveness of certain continental and international institutions. Opinions were gathered on the role of the government in improving the economy, whether corruption existed in local and national government, whether government officials were responsive to problems of the general population, and whether local government officials, the police, the courts, the overall criminal justice system, the National Electoral Commission, and the government broadcasting service could be trusted. Respondents were polled on their knowledge of the government, including the identification of government officials, their level of personal involvement in political, governmental, and community affairs, their participation in national elections, and the inclusiveness of the government. Economic questions addressed the past, present, and future of the country's and the respondents' economic conditions, and respondents' living conditions. In addition, opinions were sought on recent conflicts associated with political change within Kenya. Questions addressed the impact on the respondent of the violence that occurred following the December, 2007 general elections in Kenya. Background variables include age, gender, ethnicity, education, religious affiliation and participation, political party affiliation, language spoken most at home, whether the respondent was the head of household, current and past employment status, whether a close friend or relative had died from AIDS, and language used in the interview. In addition, the interviewer's gender, race, and education level is provided. Please visit the Afrobarometer Web site for more information regarding weights. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Response Rates: Approximately 66 percent. Citizens of Kenya age 18 years or older, excluding institutions Smallest Geographic Unit: eumeration area The Afrobarometer uses a clustered, stratified, multi-stage, probability sample design. The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. This objective is reached by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1,200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 3 percent with a confidence level of 95 percent. If the sample size is increased to 2,400, the confidence interval shrinks to plus or minus 2 percent. face-to-face interviewUsers may notice that the missing values designations are wider than the variable formats. This is due to optimization of the data. For those variables, there are no cases with those values.The original data collection was carried out by the University of Nairobi Institute for Development Studies. Additional information about the Afrobarometer Survey can be found at the Afrobarometer Web site.

First name, last name and country of origin of all researchers with inference accuracy ≥70% for a country whose names were relatively poorly recognized by NamSor (i.e., Kenya).

Descriptive statistics of factors associated with breast lesions among women with breast lumps attending two select teaching and referral hospitals in Kenya (n = 651).

Understanding the effects of actions undertaken by human societies on crop evolution processes is a major challenge for the conservation of genetic resources. This study investigated the mechanisms whereby social boundaries associated with patterns of ethnolinguistic diversity have influenced the on-farm distribution of sorghum diversity. Social boundaries limit the diffusion of planting material, practices and knowledge, thus shaping crop diversity in situ. To assess the effect of social boundaries, this study was conducted in the contact zone between the Chuka, Mbeere and Tharaka ethnolinguistic groups in eastern Kenya. Sorghum varieties were inventoried and samples collected in 130 households. In all, 297 individual plants derived from seeds collected under sixteen variety names were characterized using a set of 18 SSR molecular markers and 15 morphological descriptors. The genetic structure was investigated using both a Bayesian assignment method and distance-based clustering. Principal Coordinates Analysis was used to describe the structure of the morphological diversity of the panicles. The distribution of the varieties and the main genetic clusters across ethnolinguistic groups was described using a non-parametric MANOVA and pairwise Fisher tests. The spatial distribution of landrace names and the overall genetic spatial patterns were significantly correlated with ethnolinguistic partition. However, the genetic structure inferred from molecular makers did not discriminate the short-cycle landraces despite their morphological distinctness. The cases of two improved varieties highlighted possible fates of improved materials. The most recent one was often given the name of local landraces. The second one, that was introduced a dozen years ago, displays traces of admixture with local landraces with differential intensity among ethnic groups. The patterns of congruence or discordance between the nomenclature of farmers’ varieties and the structure of both genetic and morphological diversity highlight the effects of the social organization of communities on the diffusion of seed, practices, and variety nomenclature.

jats:p Stripe rust, caused by the fungal pathogen Puccinia striiformis f. sp. tritici, is a major threat to wheat (Triticum spp.) production worldwide. The objective of this study was to determine the virulence of P. striiformis f. sp. tritici races prevalent in the main wheat growing regions of Kenya, which includes Mt. Kenya, Eastern Kenya, and the Rift Valley (Central, Southern, and Northern Rift). Fifty P. striiformis f. sp. tritici isolates collected from 1970 to 1992 and from 2009 to 2014 were virulence phenotyped with stripe rust differential sets, and 45 isolates were genotyped with sequence characterized amplified region (SCAR) markers to differentiate the isolates and identify aggressive strains PstS1 and PstS2. Virulence corresponding to stripe rust resistance genes Yr1, Yr2, Yr3, Yr6, Yr7, Yr8, Yr9, Yr17, Yr25, and Yr27 and the seedling resistance in genotype Avocet S were detected. Ten races were detected in the P. striiformis f. sp. tritici samples obtained from 1970 to 1992, and three additional races were detected from 2009 to 2014, with a single race being detected in both periods. The SCAR markers detected both Pst1 and Pst2 strains in the collection. Increasing P. striiformis f. sp. tritici virulence was found in the Kenyan P. striiformis f. sp. tritici population, and different P. striiformis f. sp. tritici race groups were found to dominate different wheat growing regions. Moreover, recent P. striiformis f. sp. tritici races in East Africa indicated possible migration of some race groups into Kenya from other regions. This study is important in elucidating P. striiformis f. sp. tritici evolution and virulence diversity and useful in breeding wheat cultivars with effective resistance to stripe rust. /jats:p

In the Paris Summer Olympics in 2024, Kenyan athletes won the most gold medals among other African nations, with four gold medals. All four medals were in running, with Beatrice Chebet winning the women's 5,000-meter and 10,000-meter races. Emmanuel Wanyonyi won the men's 800 meter race, while Fatih Kipyegon won the women's 1,500 meter race. Algeria followed with two golds. Kaylia Nemour, an artistic gymnast, and Imane Khelif, a boxer, won the gold medals for the country. In terms of the number of medals won, Kenya, South Africa, and Ethiopia were higher, with a total of 11, six, and four medals, respectively.