In 2023, there were around 211 million children aged 0-4 years in Africa. In total, the population aged 17 years and younger amounted to approximately 680 million. In contrast, only approximately 52 million individuals were aged 65 years and older as of the same year. The youngest continent in the world Africa is the continent with the youngest population worldwide. As of 2024, around 40 percent of the population in Sub-Saharan Africa was aged 15 years and younger, compared to a global average of 25 percent. Although the median age on the continent has been increasing annually, it remains low at around 20 years. There are several reasons behind the low median age. One factor is the low life expectancy at birth: On average, the male and female populations in Africa live between 61 and 65 years, respectively. In addition, poor healthcare on the continent leads to high mortality, also among children and newborns, while the high fertility rate contributes to lowering the median age. Cross-country demographic differences Africa’s demographic characteristics are not uniform across the continent. The age structure of the population differs significantly from one country to another. For instance, Niger and Uganda have the lowest median age in Africa, at 15.1 and 16.1 years, respectively. Not surprisingly, these countries also register a high crude birth rate. On the other hand, North Africa is the region recording the highest life expectancy at birth, with Tunisia and Algeria leading the ranking in 2025.

In 2024, around ** percent of the total population of Sub-Saharan Africa was aged 15 to 64 years. Moreover, children younger than 15 years constituted nearly ** percent of the inhabitants. Overall, Africa has a young population. Only ***** percent of the individuals in the Sub-Saharan region were aged 65 years and older. As of 2023, Niger, Uganda, Angola, and Mali had a median age below 16.5 years, the lowest on the continent.

Africa has the youngest population in the world. Among the 35 countries with the lowest median age worldwide, only three fall outside the continent. In 2023, the median age in Niger was 15.1 years, the youngest country. This means that at this age point, half of the population was younger and half older. A young population reflects several demographic characteristics of a country. For instance, together with a high population growth, life expectancy in Western Africa is low: this reached 58 years for men and 60 for women in 2024. Overall, Africa has the lowest life expectancy in the world.

Africa’s population is still growing Africa’s population growth can be linked to a high fertility rate, along with a drop in death rates. Despite the fertility rate on the continent following a constant declining trend, it remains far higher compared to all other regions worldwide. It was forecast to reach 4.02 children per woman, compared to a worldwide average of 2.25 children per woman in 2024. Furthermore, the crude death rate in Africa overall dropped, only increasing slightly during the coronavirus (COVID-19) pandemic. The largest populations on the continent Nigeria, Ethiopia, Egypt, and the Democratic Republic of Congo are the most populous African countries. In 2025, people living in Nigeria amounted to over 237 million, while the number for the three other countries exceeded 100 million each. Of those, the Democratic Republic of Congo sustained the fourth-highest fertility rate in Africa in 2023. Nigeria and Ethiopia also had high rates, with 4.48 and 3.99 births per woman, respectively. Although such a high fertility rate is expected to slow down, it will still impact the population structure, growing younger nations.

Share of youth not in education, employment or training, total (% of youth population) in South Africa was reported at 34.61 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Africa - Share of youth not in education, employment or training, total - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Central African Republic: (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).

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

Abstract

The Survey of Activities of Young People was conducted by Statistics South Africa and commissioned by the Department of Labour, primarily to gather information necessary for formulating an effective programme of action to address the issue of harmful work done by children in South Africa. Technical assistance for the survey was provided by the International Labour Organisation (ILO) and a consultant appointed by the Department of Labour. Stats SA also worked with an advisory committee, consisting of representatives from national government departments most directly concerned with child labour (the Departments of Labour,Welfare,Education and Health), non-governmental organisations, and the United Nations Children's Fund (Unicef).

Geographic coverage

The survey has national coverage

Analysis unit

Households and individuals

Universe

The sampled population was household members in South Africa. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data

Sampling procedure

The sample frame was based on the 1996 Population Census Enumerator Areas (EA) and the number of households counted in 1996 Population Census. The sampled population excluded all prisoners in prison, patients in hospitals, people residing in boarding houses and hotels (whether temporary or semi-permanent), and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households. Coverage rules for the survey were that all children of usual residents were to be included even if they were not present. This means that most boarding school pupils were included in their parents’ household. The 16 EA types from the 1996 Population Census were condensed into four area types. The four area types were Formal Urban, Informal Urban, Tribal, and Commercial Farms. A decision was made to drop the Institution type EAs.

The EAs were stratified by province, and within a province by the four area types defined above. The sample size (6110 households) was disproportionately allocated to strata by using the square root method. Within the strata the EAs were ordered by magisterial district and the EA-types included in the area type (implicit stratification). PSUs consisted of ONE or more EAs of size 100 households to ensure sufficient numbers for screening. Statistics SA was advised by child labour experts that there was a likelihood of high rates of child labour in the Urban Informal and Rural Farm areas. The sample allocation to Rural Commercial Farms was therefore increased to a minimum of 20 PSUs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Phase one questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

Phase two questionnaire The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The data files contain data from sections of the questionnaires as follows:

PERSON: Data from Section 1, 2 and 3 of the questionnaire HHOLD : Data from Section 4 ADULT : Data from Section 5 YOUNGP: Data from Section 6, 7, 8 and 9

In 2024, the population of Africa was projected to grow by 2.27 percent compared to the previous year. The population growth rate on the continent has been constantly over 2.5 percent from 2000 onwards, and it peaked at 2.63 percent in 2013. Despite a slowdown in the growth rate after that, the continent's population will continue to increase significantly in the coming years. The second-largest population worldwide In 2023, the total population of Africa amounted to almost 1.5 billion. The number of inhabitants had grown steadily in the previous decades, rising from approximately 831 million in 2000. Driven by a decreasing mortality rate and a higher life expectancy at birth, the African population was forecast to increase to about 2.5 billion individuals by 2050. Africa is currently the second most populous continent worldwide after Asia. However, forecasts showed that Africa could gradually close the gap and almost reach the size of the Asian population in 2100. By that year, Africa might count 3.8 billion people, compared to 4.6 billion in Asia. The world's youngest continent The median age in Africa corresponded to 19.2 years in 2024. Although the median age has increased in recent years, the continent remains the youngest worldwide. In 2023, roughly 40 percent of the African population was aged 15 years and younger, compared to a global average of 25 percent. Africa recorded not only the highest share of youth but also the smallest elderly population worldwide. As of the same year, only three percent of Africa's population was aged 65 years and older. Africa and Latin America were the only regions below the global average of ten percent. On the continent, Niger, Uganda, and Angola were the countries with the youngest population in 2023.

Actual value and historical data chart for South Africa Share Of Youth Not In Education Employment Or Training Female Percent Of Female Youth Population

About this dataset

Context

This list ranks the 3 cities in the Young County, TX by South African population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by South African Population: This column displays the rank of city in the Young County, TX by their South African population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• South African Population: The South African population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as South African. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Young County South African Population: This tells us how much of the entire Young County, TX South African population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Central African Republic CF: Age Dependency Ratio: % of Working-Age Population: Young data was reported at 100.879 % in 2023. This records a decrease from the previous number of 101.180 % for 2022. Central African Republic CF: Age Dependency Ratio: % of Working-Age Population: Young data is updated yearly, averaging 85.465 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 101.180 % in 2022 and a record low of 73.395 % in 1960. Central African Republic CF: Age Dependency Ratio: % of Working-Age Population: Young data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Population and Urbanization Statistics. Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.;World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2024 Revision.;Weighted average;

South Africa ZA: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth Population data was reported at 28.570 % in 2017. This records a decrease from the previous number of 28.590 % for 2016. South Africa ZA: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth Population data is updated yearly, averaging 28.590 % from Dec 2013 (Median) to 2017, with 5 observations. The data reached an all-time high of 29.260 % in 2013 and a record low of 27.950 % in 2015. South Africa ZA: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank: Employment and Unemployment. Share of youth not in education, employment or training (NEET) is the proportion of young people who are not in education, employment, or training to the population of the corresponding age group: youth (ages 15 to 24); persons ages 15 to 29; or both age groups.; ; International Labour Organization, ILOSTAT database. Data retrieved in November 2017.; Weighted Average;

Abstract

Tobacco usage rates are on the rise in low- and middle-income countries (LMIC). Adolescents are especially vulnerable to taking up tobacco use at a young age in some African countries because the tobacco industry aggressively markets to them. Most of the available evidence captures data from 13- to 15-year-olds even though evidence from Sub Saharan Africa (SSA) shows that the age of smoking initiation among young people ranges from as young as 7 years old to about 16 years old. The lack of data on adolescent tobacco use in African countries limits policymakers’ ability to make data-informed decisions on tobacco control policies. The problem that the study aims to address is the lack of quality and timely primary data on adolescent tobacco use which significantly inhibits the country’s ability to appropriately target efforts, engage county governments to action, and increase global attention and funding for adolescent health.

Geographic coverage

Nation-wide household survey (Kenya and Nigeria)

Analysis unit

The study was a household-based with the household head and adolescents to be interviewed.

Individual Household

Universe

The survey covered household head (either male or female) and adolescents aged (10-17 years old)

Sampling procedure

Kenya

Sample size: The sample size for this survey was calculated using the United Nations (UN) formula (see Appendix 2) for estimating sample sizes in prevalence studies for household surveys (UN, 2008). In the computation of the sample, a 95% confidence level was applied, along with a default design effect of 2.0 to account for multistage sampling. A 10% non-response rate was factored into the calculations, consistent with other studies in Kenya (KNBS, 2015). An estimate of 16.2% was used for the expected prevalence of tobacco use among adolescents (Nazir et al., 2019). The adolescent population proportion was estimated at 20.45% and the average household size estimated at 3.9, based on the 2019 Kenya Population and Housing Census (KNBS, 2019). Using these parameters, the calculation resulted in a nationally representative sample of 6,061 adolescents in Kenya, which is sufficient for analysis and national-level inferences. However, to adjust for the 10% non-response rate, a targeted sample size of 6,734 was computed

Sampling procedure:The survey utilized a three-stage stratified cluster sample design.The first stage involved the selection of 16 counties from Kenya's 47 counties. Prior to sampling, the counties were stratified by grouping them into the eight former provinces. Thereafter, a representative and proportionate sample was selected from each province. The number of sampled counties was computed using Taro Yamane's simplified formula for proportions (Tepping, 1968). Nairobi county was included by default because it is a capital city, a region, and a county. The remaining 15 counties were randomly selected based on a computer-generated sequence using R statistical software.The second stage involved random selection of EAs within the 16 sampled counties, which was done with probability proportional to the size of the EA. Prior to EA sample selection, the EA sampling frame was first stratified by residence (rural and urban) and 224 EAs were selected: 81 in urban areas and 143 in rural areas. To generate a household sampling frame and identify households with eligible adolescents, the survey team conducted a household listing operation within the selected EAs. The operation involved visiting each EA to list all eligible households and their addresses.In the third stage, 30 households were randomly selected from each EA. In each selected household, only one adolescent aged 10 to 17 years was interviewed. These interviewees were randomly sampled if multiple adolescents were present in the household.

Nigeria

Sample size: Nigeria: The sample size for this study was estimated using the UN formula for estimating sample sizes in prevalence studies (UN, 2008), with a 95% confidence level. A sample design effect of 2.5 (default value) was applied since sampling was to be conducted at different administrative levels, such as geopolitical zones, states, and EAs. A non-response rate of 20% was factored into the calculations. While non-response rates for adult populations and previous adolescent studies in Nigeria are typically around 10% (NPC & ICF, 2019), a higher rate was considered due to the assumption that the target population may be mobile. The global prevalence of tobacco use among adolescents, reported as 19.4% (Itanyi et al.,2018) was used as the estimated prevalence due to a lack of recent national estimates. The adolescent population proportion was estimated at 17.9%, and the average household size was set at 4.7, based on national statistics from the 2018 Nigeria Demographic and Health Survey (NDHS) (NPC and ICF, 2019). Using these parameters, the calculation resulted in a nationally representative sample of 6,358 adolescents in Nigeria, which is sufficient for analysis and national-level inferences. However, to adjust the 20% non-response rate, a targeted sample size of 7,948 was envisaged.

Sampling Procedure: The survey employed a multi-stage stratified cluster sampling design to produce a nationally representative sample of adolescents, covering both urban and rural areas. The first sampling stage involved randomly selecting 13 study states (12 states and the FCT, Abuja) from the national sampling frame of 36 states as provided by the NPC. The states were stratified by grouping them into their respective geopolitical zones, and then a representative and proportionate sample from each zone was randomly selected using a computer-generated sequence. The number of sampled states was calculated using Taro Yamane's simplified formula for proportions. The FCT was included by default due to its status as the capital. In the second stage, 265 EAs were selected using probability proportional to the size of the sampled states. Before selecting the EAs, the sampling frame was stratified by residence (urban/rural). Among the selected EAs, 105 were in urban areas and 160 in rural areas. Prior to field work, the survey team carried out a household listing operation in all selected EAs to obtain an updated list of eligible households in the selected EAs, which served as the sampling frame at the third stage of sample selection. In the third stage, 30 households per EA were randomly selected to reduce clustering effects. In each selected household, one adolescent aged 10 to 17 years was randomly selected to be interviewed (where multiple adolescents were available). If a selected adolescent was unavailable, interviewers made up to three return visits to complete the interview. If the adolescents remained unavailable after the third visit, the survey was closed, and no replacements were made.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f]

Research instrument

The DaYTA standardized questionnaire was developed through intensive review of literature, including other standardized survey questionnaires that are used internationally. Examples include the following: CDC National Youth Tobacco Survey (NYTS) The Global Youth Tobacco Survey (GYTS) Global Adult Tobacco Survey (GATS) ASH Smokefree Great Britain Youth survey (ASH-Y) International Tobacco Control (ICT)-Youth Surveys WHO Tobacco Questions for Surveys of Youth (TQS-Youth) The reviews were complemented by consultations with country stakeholders and field testing to ensure that the questionnaires were appropriate and relevant to policy decisions in and across-countries Both household and individual-level data will be collected as follows: Household data: The household questionnaire will be administered to the consenting head of household or acting head of household. The questionnaire will collect information on demographics and socio-economic status as presented below: Module 1: Household roster - demographic data of household members (de facto residents who stay in the household) Module 2: Household characteristics - socio-economic data. Individual-level data from participating adolescents: Information to be collected through core modules will include the following: Module 1: Socio-demographic characteristics such as age, sex, school year (if in school), average weekly spending money; Functional difficulties i.e. vision, mobility, cognition remembering, self-care and communication. Module 2 - 7: Tobacco use for both smoked tobacco [manufactured/factory-made cigarettes, roll-your-own (RYO)/hand rolled cigarettes, shisha/waterpipe/hookah and emerging tobacco products such as heated tobacco products), and other tobacco products e.g. cigars, cheroots, cigarillos] and smokeless tobacco [chewing tobacco such as tobacco leaf, tobacco leaf and lime; Kuber, applying tobacco such as, tobacco toothpaste-dentobac etc.; tobacco tooth powder-lal, etc.; snuff)], including type, quantity, frequency, dependency, age of initiation, where they smoke, and with whom; Use of novel products such as electronic nicotine/ non-nicotine delivery systems; Access to tobacco and novel products (e.g., how they access, where and for how much); Multi-level (e.g., individual-, household- and environment-level) factors associated with tobacco use among adolescents,19-22 such as in-school/ out-of-school, parents/guardians/other family members’ tobacco use histories, exposure to second-hand tobacco smoke within the home, or tobacco use amongst close friends, exposure to tobacco advertising, promotion or sponsorship, and exposure to anti-tobacco messages. Module 8: Knowledge, Attitudes, Perceptions, intentions about using tobacco and its

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Black or African American Alone (5-year estimate) in Young County, TX (B03002004E048503) from 2009 to 2023 about Young County, TX; African-American; non-hispanic; estimate; TX; 5-year; persons; population; and USA.

In 2024, around **** percent of the African youth, those aged between 15 and 24 years old, were expected to be unemployed. According to data from the International Labor Organization, this figure has remained stable since 2021. The rate of unemployment among youths in the continent has fluctuated in the period under review, overall slightly dropping in comparison to the share in 2012, the lowest in the period reviewed.

Youth illiterate population, 15-24 years, both sexes (number) in Central African Republic was reported at 623445 Persons in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Central African Republic - Youth illiterate population, 15-24 years, both sexes - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

South Africa ZA: Age Dependency Ratio: % of Working-Age Population: Young data was reported at 44.137 % in 2017. This records a decrease from the previous number of 44.469 % for 2016. South Africa ZA: Age Dependency Ratio: % of Working-Age Population: Young data is updated yearly, averaging 69.827 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 78.491 % in 1966 and a record low of 44.137 % in 2017. South Africa ZA: Age Dependency Ratio: % of Working-Age Population: Young data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Population and Urbanization Statistics. Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.; ; World Bank staff estimates based on age distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average;

Around *** out of 10 young people in Africa have never traveled within the continent, according to a survey conducted in 2021. Among the youth in North Africa, the share was even higher. Nearly ** percent of young people in this sub-region had not traveled to African countries other than their home country. On the other hand, ** percent of young South Africans had already visited at least one country in Africa.

Youth illiterate population, 15-24 years, male (number) in Central African Republic was reported at 263922 Persons in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Central African Republic - Youth illiterate population, 15-24 years, male - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

In the face of rapid demographic transitions, Sub-Saharan African countries stand at a critical juncture where the potential for harnessing a demographic dividend to fuel economic growth is immense. This demographic shift presents both challenges and opportunities, with the right investments in health, education, and employment, countries can turn the growing youth population into a powerful engine for development, driving substantial and sustainable economic progress across the region. This study examines the demographic structure effect on economic growth in the context of structural changes in 26 sub-Saharan African countries. Using data from 1992 to 2019 in the PMG-ARDL, FMOLS, and DOLS estimates, we find that demographic structure has a positive influence on economic growth in the long run, which occurs through effective structural change, that is, structural changes that occur with an increase in labor productivity growth. Indeed, our results show that structural changes are relevant in transforming African youth debt into demographic dividends. The study investigates the impact of demographic structure on economic growth within the context of structural changes in 26 sub-Saharan African countries from 1992 to 2019. It provides a detailed analysis of the impact of demographic transition, characterized by declining fertility rates and an expanding working-age population, on economic growth in sub-Saharan Africa. It highlights the importance of structural changes, such as labor productivity and sectoral composition variations, to transform demographic advantages into sustainable economic growth. Using robust econometric methods (PMG-ARDL, FMOLS, and DOLS), the research demonstrates a significant positive long-term impact of demographic structure on economic development, mediated by effective structural change. The policy implications include promoting family planning and education for young girls, which will help reduce dependency ratios, accelerate demographic transitions, and encourage industrialization and innovation to drive structural change and improve labor productivity. Incorporating demographic characteristics such as education levels and health status into economic planning will help maximize the benefits of demographic transitions. Recommendations include encouraging demographic and sectoral policies to effectively manage demographic transitions and promote structural change and innovation. Future research should include country-specific analyses to address heterogeneity and incorporate additional indicators such as education and health to capture their nuanced impacts on economic growth. The results of this study are significant for policymakers, researchers, and development practitioners working in sub-Saharan Africa. By providing empirical evidence on the interaction between demographic structure and structural change, the study offers valuable insights into strategies for leveraging the demographic dividend to fuel sustainable economic growth in the region. This research contributes to a better understanding of how to navigate demographic transitions and structural changes to achieve long-term economic development.

Graph and download economic data for Unemployment Rate - 16-19 Yrs., Black or African American (LNS14000018) from Jan 1972 to Sep 2025 about 16 to 19 years, African-American, household survey, unemployment, rate, and USA.