This statistic shows the total population of Ethiopia from 2013 to 2023 by gender. In 2023, Ethiopia's female population amounted to approximately 64.21 million, while the male population amounted to approximately 64.49 million inhabitants.

Ethiopia ET: Adjusted Net Enrollment Rate: Primary: % of Primary School Age Children data was reported at 86.019 % in 2015. This records a decrease from the previous number of 86.317 % for 2014. Ethiopia ET: Adjusted Net Enrollment Rate: Primary: % of Primary School Age Children data is updated yearly, averaging 47.353 % from Dec 1987 (Median) to 2015, with 23 observations. The data reached an all-time high of 86.317 % in 2014 and a record low of 19.105 % in 1994. Ethiopia ET: Adjusted Net Enrollment Rate: Primary: % of Primary School Age Children data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Education Statistics. Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Abstract

The 2016 Ethiopia Demographic and Health Survey (EDHS) is the fourth Demographic and Health Survey conducted in Ethiopia. It was implemented by the Central Statistical Agency (CSA) at the request of the Federal Ministry of Health (FMoH). The primary objective of the 2016 EDHS is to provide up-to-date estimates of key demographic and health indicators. The EDHS provides a comprehensive overview of population, maternal, and child health issues in Ethiopia. More specifically, the 2016 EDHS: - Collected data at the national level that allowed calculation of key demographic indicators, particularly fertility and under-5 and adult mortality rates - Explored the direct and indirect factors that determine levels and trends of fertility and child mortality ? Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery - Obtained data on child feeding practices, including breastfeeding - Collected anthropometric measures to assess the nutritional status of children under age 5, women age 15-49, and men age 15-59 - Conducted haemoglobin testing on eligible children age 6-59 months, women age 15-49, and men age 15-59 to provide information on the prevalence of anaemia in these groups - Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviours and condom use - Conducted HIV testing of dried blood spot (DBS) samples collected from women age 15-49 and men age 15-59 to provide information on the prevalence of HIV among adults of reproductive age - Collected data on the prevalence of injuries and accidents among all household members - Collected data on knowledge and prevalence of fistula and female genital mutilation or cutting (FGM/C) among women age 15-49 and their daughters age 0-14 - Obtained data on women’s experience of emotional, physical, and sexual violence.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59
• Health facility

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2016 EDHS is the Ethiopia Population and Housing Census (PHC), which was conducted in 2007 by the Ethiopia Central Statistical Agency. The census frame is a complete list of 84,915 enumeration areas (EAs) created for the 2007 PHC. An EA is a geographic area covering on average 181 households. The sampling frame contains information about the EA location, type of residence (urban or rural), and estimated number of residential households. With the exception of EAs in six zones of the Somali region, each EA has accompanying cartographic materials. These materials delineate geographic locations, boundaries, main access, and landmarks in or outside the EA that help identify the EA. In Somali, a cartographic frame was used in three zones where sketch maps delineating the EA geographic boundaries were available for each EA; in the remaining six zones, satellite image maps were used to provide a map for each EA.

Administratively, Ethiopia is divided into nine geographical regions and two administrative cities. The sample for the 2016 EDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the nine regions and the two administrative cities.

The 2016 EDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Five questionnaires were used for the 2016 EDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Health Facility Questionnaire. These questionnaires, based on the DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Ethiopia. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After all questionnaires were finalised in English, they were translated into Amarigna, Tigrigna, and Oromiffa.

Cleaning operations

All electronic data files for the 2016 EDHS were transferred via IFSS to the CSA central office in Addis Ababa, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of openended questions; it also required generating a file for the list of children for whom a vaccination card was not seen by the interviewers and whose vaccination records had to be checked at health facilities. The data were processed by two individuals who took part in the main fieldwork training; they were supervised by two senior staff from CSA. Data editing was accomplished using CSPro software. During the duration of fieldwork, tables were generated to check various data quality parameters and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in January 2016 and completed in August 2016.

Response rate

A total of 18,008 households were selected for the sample, of which 17,067 were occupied. Of the occupied households, 16,650 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 16,583 eligible women were identified for individual interviews. Interviews were completed with 15,683 women, yielding a response rate of 95%. A total of 14,795 eligible men were identified in the sampled households and 12,688 were successfully interviewed, yielding a response rate of 86%. Although overall there was little variation in response rates according to residence, response rates among men were higher in rural than in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and 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 the questions by either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2016 Ethiopia DHS (EDHS) to minimise this type of error, non-sampling errors are impossible to avoid and are difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2016 EDHS is only one of many samples that could have been selected from the same population, by using the same design and the expected size. Each of those 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.

Sampling error is usually measured in terms of the standard error for a particular statistic (such as mean or percentage), 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% 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 2016 EDHS sample is the result of a multi-stage stratified design and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, with programs developed by ICF International. These programs use the Taylor linearisation method of 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 final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar

Age and sex structures: WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Tatem et al and Pezzulo et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map population age and sex counts for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).

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 57 years for men and 59 for women. 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 4.12 children per woman, compared to a worldwide average of 2.31 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 2023, people living in Nigeria amounted to around 224 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. Nigeria and Ethiopia also had high rates, with 5.24 and 4.16 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.

Objectives: Direct comparative work in morphology and growth on widely dispersed wild primate taxa is rarely accomplished, yet critical to understanding ecogeographic variation, plastic local varia- tion in response to human impacts, and variation in patterns of growth and sexual dimorphism. We investigated population variation in morphology and growth in response to geographic variables (i.e., latitude, altitude), climatic variables (i.e., temperature and rainfall), and human impacts in the vervet monkey (Chlorocebus spp.).

Methods: We trapped over 1,600 wild vervets from across Sub-Saharan Africa and the Caribbean, and compared measurements of body mass, body length, and relative thigh, leg, and foot length in four well-represented geographic samples: Ethiopia, Kenya, South Africa, and St. Kitts & Nevis.

Results: We found significant variation in body mass and length consistent with Bergmann’s Rule in adult females, and in adult males when excluding the St. Kitts & Nevis population, which was more sexually dimorphic. Contrary to Rensch’s Rule, although the South African population had the largest average body size, it was the least dimorphic. There was significant, although very small, variation in all limb segments in support for Allen’s Rule. Females in high human impact areas were heavier than those with moderate exposures, while those in low human impact areas were lighter; human impacts had no effect on males.

Conclusions: Vervet monkeys appear to have adapted to local climate as predicted by Bergmann’s and, less consistently, Allen’s Rule, while also responding in predicted ways to human impacts. To better understand deviations from predicted patterns will require further comparative work in vervets.

Methods The data derive from field collections made over many years using a common protocol: Ethiopia in 1973, Kenya in 1978-79; South Africa in 2002–2008, and several African countries and the Caribbean in 2009– 2011 in collaboration with the International Vervet Research Consortium (Jasinska et al., 2013). The International Vervet Research Consortium is a multidisciplinary research group that has, in addition to morphological variation, studied variation in patterns of growth and development (Schmitt et al., 2018), genetic/genomic (Jasinska, et al., 2013; Schmitt et al., 2018; Svardal et al., 2017; Turner et al. 2016a; Warren et al. 2015) and transcriptomic (Jasinska et al., 2017) variation, SIV immune response (Ma et al., 2013, 2014; Svardal et al., 2017), hor- monal variation (Fourie et al., 2015), C4 isotopes variation in hair (Loudon et al., 2014), gut parasite and disease variation (Gaetano et al., 2014; Senghore et al., 2016), genital morphology and appearance (Cramer et al., 2013; Rodriguez et al., 2015a,b), and other biological parameters within the genus Chlorocebus.

Vervet monkeys were trapped at locations across sub-Saharan Africa, including South Africa, Botswana, Zambia, Ethiopia, The Gambia, Ghana, and on the Caribbean islands of St. Kitts and Nevis (Figure 1). Trapping in Africa employed individual drop traps as described by Brett, Turner, Jolly, & Cauble (1982) and Grobler and Turner (2010), while trapping in St. Kitts and Nevis was done by local trappers using large group traps (Jasinska et al., 2013). Animals were anesthetized while in the trap and then removed to a processing area. Sex was determined by visual and manual inspection, while age classes were assigned from dental eruption sequences and based on previous observations (Table 2). All animals were weighed with either an electronic or hanging scale, and measured with a tape measure and sliding calipers. Parameters and protocols describing all measurements are available through the Bones and Behavior Working Group (2015; http://www.bonesandbehavior. org/). All animals were released to their social group after sampling and recovery from anesthesia. Observations during trapping allowed us to confirm the animals’ social group and local population affiliation.

For the present study, we chose metrics representative of skeletal size (body length, thigh length, leg length, and foot length) and body mass from a total of 1,613 vervets in four geographically and genomi- cally distinct populations: Ch. aethiops in Ethiopia, Ch. p. hilgerti in Kenya, Ch. p. pygerythrus in South Africa, and Ch. sabaeus on the Carib- bean islands of St. Kitts and Nevis (Table 3). The Caribbean populations are known to be descended from West African Ch. sabaeus brought to the Caribbean several hundred years ago (Warren et al., 2015). Of the whole sample, 288 females and 460 males were dentally immature. Sexual maturity is typically not reached in vervets until near the time of canine tooth eruption, here denoting the beginning of dental age 6 (Cramer et al., 2013; Rodriguez et al., 2015a); although somatic and skeletal growth often continues beyond the emergence of the third molar, which is here denoted as adult (Bolter & Zihlman, 2003). As is common, dental age and skeletal age are presumed to be similarly cor- related across the genus, meaning that comparable dental age implies comparable skeletal developmental age across populations (Seselj, 2013).

All measurements were developed by CJJ and TRT and other measurers (CAS and JDC) were trained directly by TRT. During training, repeated measures of the same individual were conducted in tandem with TRT until concordance was reached.

The location of each trapping site is reported in decimal degrees (Table 1), and for most sites measured using hand-held GPS units. For those trapping sites lacking GPS readings, a general latitude and longi- tude for the trapping area (e.g., game reserve, town) was used. Human impact at each trapping location was assessed according to conditions during the time of trapping using a previously published index devel- oped by Pampush (2010) to study variation in vervet body size, and subsequently used by Loudon et al. (2014) and Fourie et al. (2015) (Table 1). This index includes presence/absence measures of reliable access to (1) agricultural land, (2) human food, (3) rubbish or garbage dumps, and (4) whether animals are regularly provisioned, as well as a three-level scale of human activity within the presumed home range of the group (low, moderate, or high). In the index, point values are assigned to each value, with the lowest tier of human impact each receiving a 1, scaling up by 1 for each level. Added together, these val- ues comprise a human impact group ranging from low (lowest score in each category; index 5 5), to moderate (index 5 6–8), to high (index- 5 9–11). These measures take into account only the ecological impact of humans, and do not address local ecological variables (such as native plant productivity) that might also influence body size and growth. As a proxy for these measures, we collected several climatic variables for trapping sites from the WorldClim 2 database, which has a spatial reso- lution of about 1 km2 (Fick & Hijmans, 2017). Climatic variables consid- ered for inclusion in our models were (1) annual mean temperature (in degrees Celsius), (2) temperature seasonality (measured as the standard deviation of annual mean temperature multiplied by 100), (3) the mini- mum temperature of the coldest month (in degrees Celsius), (4) the mean temperature of the coldest quarter of the year, (5) annual precipi- tation (in mm), and (6) precipitation seasonality (measured as thecoefficient of variation of monthly precipitation). Climate data were accessed via the R package raster v. 2.6-7 (Hijmans & van Etten, 2012), and assigned to trapping sites based on latitude and longitude.

Socio-demographic characteristics of study participants in Eastern Ethiopia, 2019 (n = 1164).

Abstract

The Urban Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the inactive population of the country on continuous basis. The variables collected in the survey: socio-demographic characteristics of household members; economic activity during the last seven days and six months; including characteristics of employed persons such as hours of work, occupation, industry, employment status, and earnings from paid employment; unemployment and characteristics of unemployed persons.

The general objective of the 2016 Urban Employment and Unemployment Survey is to provide statistical data on the distribution, characteristics and size of the economic activity status i.e. employed, unemployed population of the country at urban levels on annual basis. The specific objectives of the survey are to: • collect statistical data on the potential manpower and those who are available to take part in various socio-economic activities; • update the data and determine the size and distribution of the labour force participation and the status of economic activity for different sub-groups of the population at different levels of the country; and also to study the socio-economic and demographic characteristics of these groups; • identify the size, distribution and characteristics of employed population i.e. working in the formal or informal employment sector of the economy and earnings from paid employees by occupation and Industry...etc; • provide data on the size, characteristics and distribution of unemployed population and rate of unemployment; • provide data that can be used to assess the situation of women's employment or the participation of women in the labour force; and • generate annual time series data to trace changes over time

Geographic coverage

The survey covered all urban parts of the country except three zones of Afar and six zones of Somali, where the residents are pastoralists.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2007 Population and Housing Census was used as frame to select 30 households from the sample enumeration areas.

The country was divided into two broad categories. 1) Major urban centers: All regional capitals and five other major urban centers were included in this category. This category had a total of 16 reporting levels. A stratified two-stage cluster sample design was implemented to select the samples. The primary sampling units were EAs, from each EA 30 households were selected as a second stage unit.

2) Other urban centers: In this category, all other urban centers were included. A stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. From each EA 30 households were selected at the third stage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire that was used to collect the data had five sections:

Section - 1: Area identification of the selected household: this section dealt with area identification of the respondents such as region, zone, wereda, etc.

Section - 2: Socio- demographic characteristics of households: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status and type of migration, disability, literacy status, educational Attainment, types of training and marital status.

Section - 3: Economic activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, status in employment, earnings from employment, job mobility, service year for paid employees employment in the formal and informal sector and time related under employment.

Section - 4: Unemployment and characteristics of unemployed persons: this section focused on the size, rate and characteristics of the unemployed population.

Section - 5: Economic activities during the last six months: this section consists of the usual economic activity status refereeing to the long reference period i.e. engaged in productive activities during most of the last six months, reason for not being active.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork, field supervisors and statisticians of the head and branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This was an important part of data processing operation to maintain the quality of the data. Consistency checks and re-checks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject matter experts from Manpower Statistics Team of the CSA.

Response rate

Response rate of the survey was 99.8%

Sampling error estimates

Estimation procedures, estimates, and CV's for selected tables are provided in the Annex II and III of the survey final report.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. 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.

Socio-demographic characteristics of study participants with a hospital diagnosis of cardiopathy, 2015–2018, Ethiopia.

Socio-demographic characteristics of study participants with a hospital diagnosis of ARF/RHD, 2015–2018, Ethiopia.

Treatment outcome by demographic and clinical characteristics.

Distribution of socio-demographic and economic characteristics by hypertension among adults aged 25–64 years in Wolaita, southern Ethiopia 2018 (n = 2483).

Ethiopia ET: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth Population data was reported at 5.663 % in 2013. Ethiopia ET: Share of Youth Not in Education, Employment or Training: Male: % of Male Youth Population data is updated yearly, averaging 5.663 % from Dec 2013 (Median) to 2013, with 1 observations. Ethiopia ET: 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 Ethiopia – Table ET.World Bank.WDI: 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 September 2018.; Weighted average;