Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING PROCEDURE The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households' economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample. The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

(a) Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

(b) Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

(c) Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

OTHER PROCESSING The electronic datasets are organized by questionnaire with the following labels on file names in parentheses: household (hh), community (com), post-planting agriculture (pp), post-harvest agriculture (ph), and livestock (ls). The data within each questionnaire do not contain any constructed variables. For example, the ESS data provide most all variables needed to construct an estimate of total household consumption, but the data set does not contain an estimated value of total consumption. The only compiled data that are included with the ESS files are the geo-spatial variables.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

Abstract

The Ethiopia Socioeconomic Survey (ESS) is a collaborative project between the Central Statistics Agency of Ethiopia (CSA) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology.

ESS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households in agriculture activities in the country. The ESS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time makes the ESS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESS is the first panel survey to be carried out by the CSA that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the new ESS4 is based on the updated 2018 pre-census cartographic database of enumeration areas by CSA. The ESS4 sample is a two-stage stratified probability sample. The ESS4 EAs in rural areas are the subsample of the AgSS EA sample. That means, the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e. the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematically with PPS. This is designed in way that automatically results in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling for the ESS4 is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e. systematic random sampling. One important issue to note in ESS4 sampling is that the total number of agriculture households per EA remains 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA.

For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA. Table 3.2 presents the distribution of sample households for ESS4 by region, urban and rural stratum. A total of 7527 households are sampled for ESS4 based on the above sampling strategy.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.

The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.

The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.

Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).

Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).

Ethiopia ET: Rural Population Growth data was reported at 1.908 % in 2017. This records a decrease from the previous number of 1.957 % for 2016. Ethiopia ET: Rural Population Growth data is updated yearly, averaging 2.464 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3.315 % in 1992 and a record low of 1.107 % in 1978. Ethiopia ET: Rural Population Growth 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: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

Ethiopia ET: Urban Population Growth data was reported at 4.674 % in 2017. This records a decrease from the previous number of 4.731 % for 2016. Ethiopia ET: Urban Population Growth data is updated yearly, averaging 4.824 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 5.780 % in 1964 and a record low of 3.232 % in 1978. Ethiopia ET: Urban Population Growth 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: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

Abstract

The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host communities to design a better mix of approaches which could help the government in designing livelihood opportunities for these communities. The SPS 2017 contains information on employment, barriers to labor force participation, livelihood structures of refugees before displacement, education and economic conditions as well as access to services, and perceptions. The data combines detailed household questionnaire information with displacement-specific information including drivers of displacement, access to resettlement mechanisms, and return intentions. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology as detailed in Pape and Mistiaen (2018).

Geographic coverage

The Skills Profile Survey 2017 covered refugees in camps, and surrounding host communities, in Ethiopia. Refugees of four nationalities were surveyed: Eritrean, Somali, South Sudanese, and Sudanese. Only the refugees living in camps were surveyed, because tracing households outside the camps was not feasible. However, 66 percent of all refugees in Ethiopia live in camps, while those that live outside camps are largely Eritrean. Host communities, defined as Ethiopian non-displaced households living within a 5km radius of a camp, were also surveyed.

Analysis unit

Household and individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The SPS 2017 is a household survey with a multi-stage stratified random sample. The sampling frame was the list of refugee camps, sites and locations as of January 2017, provided by UNHCR-Ethiopia. The sample consisted of four strata based on four regions: Tigray Afar (Eritrean refugees), Gambella (South Sudanese refugees), Benishangul Gumuz (Sudanese refugees), and Somali (Somali refugees). Each region hosts predominantly one refugee nationality leading to an implicit stratification based on nationality. In each stratum, camps were divided into Enumeration Areas (EAs) of equal size using GIS technology, and 82 EAs were selected per stratum. Within the stratum, the number of EAs per camp was selected proportional to the size of the camp. Within camps, EAs were selected with equal probability. Within each selected EA, all households were listed and then 12 were randomly selected for interview.

The host community sample consisted of the same four regional strata. Within each stratum, areas within a 5km radius of a camp, were divided into EAs of equal size. Of these EAs, those classified as 'residential' by Open Street Maps were used as the sampling frame. 42 EAs were selected per stratum. Within a stratum, EAs were selected using probability proportional to size, with the probability of selection of an EA corresponding to the area of the EA. Within each selected EA, all households were listed and then 12 were randomly selected for interview.

Sampling deviation

Due to security concerns, revisions were made to the sample during fieldwork. Enumerators in Gambella stratum (hosting South Sudanese refugees) faced repeated security threats and could survey only 439 of the intended 900 refugee households in the region. As the survey team was withdrawn from Gambella region, the host community in Gambella region was not surveyed. The remaining interviews with refugees in Gambella region were substituted by oversampling EAs in Benishangul Gumuz, as 25 percent of the refugee population in this region is South Sudanese. In September 2017, violent conflict in Oromia and Somali regions escalated, rendering some of the camps in Somali stratum inaccessible. The EAs of Jijiga sub-region were replaced by EAs in non-violent areas of Somali stratum. Further, as most refugee camps are in remote areas with sparse host populations, the final number of host households surveyed fell short of the original intended sample of 500 host households per stratum. However, despite the changes in sample, the survey captured roughly similar number of refugee households of the four main refugee nationalities.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire contains modules on Household Member Roster, Household Characteristics, Food Consumption, Non food consumption, Livestock, Durable Good, Wellbeing and Opinions and Forced Displacement, Movement and Return Intentions. The questionnaire is available for download with the dataset.

Cleaning operations

See accompanying Stata do-files, available under the related materials tab.

Response rate

NA

Ethiopia ET: Urban Population data was reported at 21,316,856.000 Person in 2017. This records an increase from the previous number of 20,343,419.000 Person for 2016. Ethiopia ET: Urban Population data is updated yearly, averaging 5,603,857.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 21,316,856.000 Person in 2017 and a record low of 1,424,992.000 Person in 1960. Ethiopia ET: Urban 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: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Sum;

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 Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 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. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

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.

Ethiopia ET: Population in Urban Agglomerations of More Than 1 Million data was reported at 4,215,965.000 Person in 2017. This records an increase from the previous number of 4,039,927.000 Person for 2016. Ethiopia ET: Population in Urban Agglomerations of More Than 1 Million data is updated yearly, averaging 1,690,413.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 4,215,965.000 Person in 2017 and a record low of 519,177.000 Person in 1960. Ethiopia ET: Population in Urban Agglomerations of More Than 1 Million 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: Population and Urbanization Statistics. Population in urban agglomerations of more than one million is the country's population living in metropolitan areas that in 2018 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; ;

Ethiopia ET: Rural Population data was reported at 83,640,582.000 Person in 2017. This records an increase from the previous number of 82,059,777.000 Person for 2016. Ethiopia ET: Rural Population data is updated yearly, averaging 40,091,631.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 83,640,582.000 Person in 2017 and a record low of 20,726,286.000 Person in 1960. Ethiopia ET: Rural 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: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Sum;

Ethiopia ET: Rural Population: % of Total Population data was reported at 79.690 % in 2017. This records a decrease from the previous number of 80.134 % for 2016. Ethiopia ET: Rural Population: % of Total Population data is updated yearly, averaging 87.739 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 93.567 % in 1960 and a record low of 79.690 % in 2017. Ethiopia ET: Rural Population: % of Total 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: Population and Urbanization Statistics. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

As of February 2025, Morocco had an internet penetration of over 92 percent, making it the country with the highest internet penetration in Africa. Libya ranked second, with 88.5 percent, followed by Seychelles with over 87 percent. On the other hand, The Central African Republic, Chad, and Burundi had the lowest prevalence of internet among their population. Varying but growing levels of internet adoption Although internet usage varies significantly across African countries, the overall number of internet users on the continent jumped to around 646 million from close to 181 million in 2014. Of those, almost a third lived in Nigeria and Egypt only, two of the three most populous countries on the continent. Furthermore, internet users are expected to surge, reaching over 1.1 billion users by 2029. Mobile devices dominate web traffic Most internet adoptions on the continent occurred recently. This is among the reasons mobile phones increasingly play a significant role in connecting African populations. As of early January 2024, around 74 percent of the web traffic in Africa was via mobile phones, over 14 percentage points higher than the world average. Furthermore, almost all African countries have a higher web usage on mobile devices compared to other devices, with rates as high as 92 percent in Sudan. This is partly due to mobile connections being cheaper and not requiring the infrastructure needed for traditional desktop PCs with fixed-line internet connections.