Elderly literacy rate, population 65+ years, both sexes (%) in Kenya was reported at 57.42 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Elderly literacy rate, population 65+ years, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Elderly illiterate population, 65+ years, both sexes (number) in Kenya was reported at 511838 Persons in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Elderly illiterate population, 65+ years, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

In 2021, the age dependency ratio in Kenya was at **** percent. This meant that there were around ** people aged 0-14 years and 65 years and older per 100 working-age population (aged 15-64 years). The ratio declined from **** percent in 2000, indicating a reduced burden for the working-age population.

Elderly illiterate population, 65+ years, % female in Kenya was reported at 76.76 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Elderly illiterate population, 65+ years, % female - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Graph and download economic data for Age Dependency Ratio: Older Dependents to Working-Age Population for Kenya (SPPOPDPNDOLKEN) from 1960 to 2024 about 64 years +, Kenya, working-age, ratio, and population.

Kenya KE: Population: as % of Total: Male: Aged 15-64 data was reported at 56.522 % in 2017. This records an increase from the previous number of 56.147 % for 2016. Kenya KE: Population: as % of Total: Male: Aged 15-64 data is updated yearly, averaging 48.868 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 56.522 % in 2017 and a record low of 46.680 % in 1982. Kenya KE: Population: as % of Total: Male: Aged 15-64 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. Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average;

Elderly illiterate population, 65+ years, female (number) in Kenya was reported at 392893 Persons in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Elderly illiterate population, 65+ years, female - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

The largest population group in Kenya, as of 2020, comprised people aged **** years, accounting for nearly ** percent of the country's population. From a gender perspective, there were around **** million males and **** million females within this range of ages. The second biggest group consisted of inhabitants aged 25 to 54 years, with **** million men and **** million women. Among Tanzanians aged ***** years and 65 years and older, the female count was slightly higher than the male one.

Elderly literacy rate, population 65+ years, male (%) in Kenya was reported at 76.42 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Elderly literacy rate, population 65+ years, male - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This statistic shows the age structure in Kenya from 2013 to 2023. In 2023, about 37.22 percent of Kenya's total population were aged 0 to 14 years.

In 2024, the labor participation rate among the total population aged between 15 and 64 in Kenya stood at ***** percent. Between 1990 and 2024, the figure dropped by **** percentage points, though the decline followed an uneven course rather than a steady trajectory.

V1.5 The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Kenya: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Abstract

The Aging Survey is part of the 5 themes of the Urbanization, Poverty and Health Dynamics I (UPHD I) Project in Sub-Saharan Africa. The UPHD I project was designed and implement by APHRC.

The study on the migration, poverty and the wellbeing of older people is a longitudinal research project following up older people living in two slums of Nairobi, Kenya. The project is a partnership between University of Southampton, UK and the African Population and Health Research Centre (APHRC) located in Nairobi, Kenya. The overall aim of the study is to investigate the living arrangements and well-being of older people living in informal settlements and how these differ by migration status and socioeconomic circumstances.

Although the overall aim of the study (UPHD I) was to investigate the living arrangements and well-being of older people living in informal settlements and how these differ by migration status and socioeconomic circumstances.

The specific objectives were to study:

1.1 The poverty status, economic activity, demographic profile, living arrangements, and social support networks of older people;

1.2 Understand the determinants of health and health-seeking behaviour among the older people;

1.3 Examine the migration histories, migration intentions and linkages with place of origin of older urban slum dwellers;

1.4 and to understand patterns of care and support in later life and how this is affected by increased mortality of economically active adults.

Geographic coverage

Two informal settlements, Korogocho and Viwandani, in Nairobi City (the capital city) of Kenya.

Analysis unit

The unit of analysis is the individual

Universe

The survey covered all de jure household members (usual residents), aged 50 years and above in the household

Kind of data

Sample survey data [ssd]

Sampling procedure

Two cohorts of older people were recruited. The first cohort was recruited between October 2006 - February 2007 and the second cohort recruited in October -December 2007. Older people 50 years and older who were resident members of the HDSS at the time of the recruitment formed the sampling frame for the study. At the first recruitment (Cohort 1), 2,612 older people were resident in the demographic surveillance area. The second recruitment targeted older people who in-migrated (123) into the surveillance area between the January - September 2007, those who turned 50 years (242) as well as older people who were present in the first recruitment (392) but who contact could not be established. The sampling frame for the baseline was therefore a total of 2,972 older people.

Sampling deviation

All DSS Residents aged 50 years and above living in the study area qualify to respond to this questionnaire. No samples were drawn.

Mode of data collection

Face-to-face [f2f]

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 SPSS data files

Detailed documentation of the editing of data can be found in the "Standard Procedures Manual" document provided as an external resource.

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

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

Response rate

The overall survey response was 100 percent. With varying percentages on Individual questions. Omissions occured on individual questions due to refusal to answer, and use of proxy respondents.

Sampling error estimates

No estimation of sampling error was done.

Population aged 0-14 years of Kenya decreased by 1.63% from 37.4 % in 2023 to 36.8 % in 2024. Since the 0.92% decline in 2014, population aged 0-14 years slumped by 13.21% in 2024. Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population.

As of the third quarter of 2020, nearly ** percent of all people aged 45-49 years in Kenya were currently employed or unemployed but seeking work. The age group had the highest labor force participation rate in the country. On the other hand, people between the ages of 15 and 19 years registered the lowest proportion of economically active individuals in the population, at **** percent. In general, for those aged between 30 and 59 years, the labor force rate was over ** percent.

Elderly literacy rate, population 65+ years, female (%) in Kenya was reported at 43.7 % in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Elderly literacy rate, population 65+ years, female - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Distribution of Population age 3 Years and Above by highest Level of Education Reached, Area of Residence and Sex per Sub-County

Abstract

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

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

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

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2022 KDHS was drawn from the Kenya Household Master Sample Frame (K-HMSF). This is the frame that KNBS currently uses to conduct household-based sample surveys in Kenya. The frame is based on the 2019 Kenya Population and Housing Census (KPHC) data, in which a total of 129,067 enumeration areas (EAs) were developed. Of these EAs, 10,000 were selected with probability proportional to size to create the K-HMSF. The 10,000 EAs were randomised into four equal subsamples. A survey can utilise a subsample or a combination of subsamples based on the sample size requirements. The 2022 KDHS sample was drawn from subsample one of the K-HMSF. The EAs were developed into clusters through a process of household listing and geo-referencing. The Constitution of Kenya 2010 established a devolved system of government in which Kenya is divided into 47 counties. To design the frame, each of the 47 counties in Kenya was stratified into rural and urban strata, which resulted in 92 strata since Nairobi City and Mombasa counties are purely urban.

The 2022 KDHS was designed to provide estimates at the national level, for rural and urban areas separately, and, for some indicators, at the county level. The sample size was computed at 42,300 households, with 25 households selected per cluster, which resulted in 1,692 clusters spread across the country, 1,026 clusters in rural areas, and 666 in urban areas. The sample was allocated to the different sampling strata using power allocation to enable comparability of county estimates.

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

Cleaning operations

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

Work was assigned by supervisors and shared via Bluetooth® to interviewers’ tablets. After completion, assigned work was shared with supervisors, who conducted initial data consistency checks and edits and then submitted data to the central servers hosted at KNBS via SyncCloud. Data were downloaded from the central servers and checked against the inventory of expected returns to account for all data collected in the field. SyncCloud was also used to generate field check tables to monitor progress and identify any errors, which were communicated back to the field teams for correction.

Secondary editing was done by members of the KNBS and ICF central office team, who resolved any errors that were not corrected by field teams during data collection. A CSPro batch editing tool was used for cleaning and tabulation during data analysis.

Response rate

A total of 42,022 households were selected for the survey, of which 38,731 (92%) were found to be occupied. Among the occupied households, 37,911 were successfully interviewed, yielding a response rate of 98%. The response rates for urban and rural households were 96% and 99%, respectively. In the interviewed households, 33,879 women age 15-49 were identified as eligible for individual interviews. Of these, 32,156 women were interviewed, yielding a response rate of 95%. The response rates among women selected for the full and short questionnaires were similar (95%). In the households selected for the men’s survey, 16,552 men age 15-54 were identified as eligible for individual interviews and 14,453 were successfully interviewed, yielding a response rate of 87%.

Sampling error estimates

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

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

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

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

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

Data

Kenya KE: Population: Male: Aged 65 and Above data was reported at 595,131.000 Person in 2017. This records an increase from the previous number of 569,826.000 Person for 2016. Kenya KE: Population: Male: Aged 65 and Above data is updated yearly, averaging 295,857.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 595,131.000 Person in 2017 and a record low of 130,131.000 Person in 1960. Kenya KE: Population: Male: Aged 65 and Above 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: Population and Urbanization Statistics. Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2017 Revision.; Sum;

In December 2024, there were over ** million Facebook users in Kenya, making up **** percent of the population. Overall, **** percent of users were aged between 18 and 24 years, and **** percent of users were aged between 25 and 34 years.