The total population in Ethiopia was estimated at 132.1 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides the latest reported value for - Ethiopia Population - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Time series data for the statistic Birth_Rate_Crude_Per_1000_People and country Ethiopia. Indicator Definition:Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.The statistic "Birth Rate Crude Per 1000 People" stands at 31.90 per mille as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.51 percentage points compared to the value the year prior.The 1 year change in percentage points is -0.51.The 3 year change in percentage points is -1.41.The 5 year change in percentage points is -1.49.The 10 year change in percentage points is -1.74.The Serie's long term average value is 45.11 per mille. It's latest available value, on 12/31/2023, is 13.21 percentage points lower, compared to it's long term average value.The Serie's change in percentage points from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0.The Serie's change in percentage points from it's maximum value, on 12/31/1985, to it's latest available value, on 12/31/2023, is -19.66.

Abstract copyright UK Data Service and data collection copyright owner.The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.Further information about the survey, including publications, can be downloaded from the Young Lives website. School Survey: A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children's experiences of schooling, and to improve our understanding of:the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groupsschool effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systemsequity issues (including gender) in relation to learning outcomes and the evolution of inequalities within educationThe survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives. A further round of school surveys took place during the 2016-2017 school year. The key focus areas for these were:benchmarking levels of student attainment and progress in key learning domainseffects of school and teacher quality, and school effectivenesseducational transitions at age 15The 2016-2017 school surveys focused on the level of schooling accessed by 15-year-olds in each country, so including Grade 7 and 8 students in Ethiopia (upper primary level), Grade 9 students in India (lower secondary level), and Grade 10 students in Vietnam (upper secondary level). The School Survey data are held separately for each country. The India data are available from the UK Data Archive under SN 7478 and SN 8359, the Vietnam data are available from SN 7663 and SN 8360, and the Peru data have been archived under SN 7479 (no 2016-2017 survey). Further information is available from the Young Lives School Survey webpages.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood. The Young Lives study aims to track the lives of 12,000 children over a 15-year period, surveyed once every 3-4 years. Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, and Round 4 surveyed them at 12 and 19 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves. The survey consists of three main elements: a child questionnaire, a household questionnaire and a community questionnaire. The household data gathered is similar to other cross-sectional datasets (such as the World Bank's Living Standards Measurement Study). It covers a range of topics such as household composition, livelihood and assets, household expenditure, child health and access to basic services, and education. This is supplemented with additional questions that cover caregiver perceptions, attitudes, and aspirations for their child and the family. Young Lives also collects detailed time-use data for all family members, information about the child's weight and height (and that of caregivers), and tests the children for school outcomes (language comprehension and mathematics). An important element of the survey asks the children about their daily activities, their experiences and attitudes to work and school, their likes and dislikes, how they feel they are treated by other people, and their hopes and aspirations for the future. The community questionnaire provides background information about the social, economic and environmental context of each community. It covers topics such as ethnicity, religion, economic activity and employment, infrastructure and services, political representation and community networks, crime and environmental changes. The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country. Further information about the survey, including publications, can be downloaded from the a href="http://www.younglives.org.uk/content/school-survey-0" title="School Survey" School Survey /a webpages.

Time series data for the statistic Fertility_Rate and country Ethiopia. Indicator Definition:Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.The statistic "Fertility Rate" stands at 3.99 births per woman as of 12/31/2023, the lowest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -2.28 percent compared to the value the year prior.The 1 year change in percent is -2.28.The 3 year change in percent is -6.67.The 5 year change in percent is -8.74.The 10 year change in percent is -15.27.The Serie's long term average value is 6.32 births per woman. It's latest available value, on 12/31/2023, is 36.89 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1984, to it's latest available value, on 12/31/2023, is -45.77%.

Inflation Rate in Ethiopia decreased to 13.60 percent in August from 13.70 percent in July of 2025. This dataset provides the latest reported value for - Ethiopia Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

Introduction Dabat Health and Demographic Surveillance System (HDSS), also called the Dabat Research Center (DRC), was established at Dabat District in 1996 after conducting initial census. Later re-census was done in 2008. The surveillance is run by the College of Medicine and Health Sciences which is one of the colleges/faculties of the University of Gondar. Dabat district is one of the 21 districts in North Gondar Administrative Zone of Amhara Region in Ethiopia. According to the report published by the Central Statistical Agency in 2007, the district has an estimated total population of 145,458 living in 27 rural and 3 urban Kebeles (sub-districts). The altitude of the district ranges from about 1000 meters to over 2500 meters above sea level. The district population largely depends on subsistence agriculture economy. There are two health centers, three health stations, and twenty-nine health posts providing health services for the community. An all-weather road runs from Gondar town through Dabat to some towns of Tigray. Dabat town, the capital of Dabat District, is located approximately 821 km northwest of Addis Ababa and 75 kms north of Gondar town. The surveillance is funded by Centers for Disease Control and Prevention (CDC) through Ethiopian Public Health Association.

Objectives Dabat HDSS/ Dabat Research Centre was established to generate longitudinal data on health and population at district level and provide a study base and sampling frame for community-based research.

Methods Dabat district was initially selected purposively as a surveillance site for its unique three climatic conditions, namely Dega (high land and cold), Woina dega (mid land and temperate) and Kolla (low land and hot). The choice was made with the assumption that there would be differences in morbidity and mortality in the different climatic areas. Accordingly, seven kebeles from Dega, one kebele from Woina dega, and two kebeles from Kolla were selected randomly after stratification of the kebeles by climatic zone.

After the re-census, update has been done regularly every 6 months. During each round, data has been collected using a semi-structured questionnaire which included information related to birth and other pregnancy outcomes, death, migration, and marital status change. Interviews are administered to the heads of the household but in the absence of the head, the next elder family member is interviewed. This is only done after repeated trial of getting the head. While the regular update round is every six months, deaths that occur in the surveillance site are reported immediately to the data collectors by the local guides. After the mourning period, usually 45 days, the trained data collectors administer Verbal Autopsy (VA) questionnaire to the close relative of the deceased to get information on the possible cause(s) of death. Three VA questionnaires are prepared for the age groups 0-28 days, 29 days to 15 years, and greater than 15 years. To assign cause(s) of death, the VA data collected by data collectors is given to physicians who have got training on VA. These physicians independently assign causes of death using the standard International Classification of Diseases (ICD-10).

Geographic coverage

Dabat Health and Demographic Surveillance System (HDSS) included seven rural kebeles (sub districts) and three urban kebeles in Dabat district which is located 75 km North of Gondar town in Ethiopia. There are highlands, midlands and few low land households in the HDSS site.

Analysis unit

Individual

Universe

All individuals residing in Dabat HDSS site.

Kind of data

Event history data

Frequency of data collection

Two rounds per year

Mode of data collection

Face-to-face [f2f]

Research instrument

All questionnaires are prepared in Amharic language. The surveillance questionnaires are related to birth and other pregnancy outcomes, death, and migration.

Cleaning operations

The filled questionnaire is checked by filled supervisors, document clerk, data entry clerks for missings and other violations. In addition, DRC Software, a software developed from Microsoft Access and Visual Basic, checks violations against set of rules for data quality during data entry.

Response rate

100% response rate

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric  Illegal   Lega  Total  Metric RunDate 
ET051 MicroDataCleaned Starts  0  59082  0  0.0 2014-06-27 19:33 
ET051 MicroDataCleaned Transitions 0  129938 129938 0.0 2014-06-27 19:33 
ET051 MicroDataCleaned Ends 0  59082  0  0.0 2014-06-27 19:33

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).

Time series data for the statistic Stillbirth rate (per 1,000 total births) and country Ethiopia. Indicator Definition:Stillbirth rate is the number of fetal deaths at 28 weeks or more of gestation per 1,000 total births. Total birth is the sum of stillbirths (as just defined) and live births.The indicator "Stillbirth rate (per 1,000 total births)" stands at 29.70 as of 12/31/2023, the lowest value since 12/31/2020. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.6689 percent compared to the value the year prior.The 1 year change in percent is -0.6689.The 3 year change in percent is 0.0.The 5 year change in percent is -1.33.The 10 year change in percent is -19.73.The Serie's long term average value is 36.80. It's latest available value, on 12/31/2023, is 19.29 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2019, to it's latest available value, on 12/31/2023, is +0.678%.The Serie's change in percent from it's maximum value, on 12/31/2000, to it's latest available value, on 12/31/2023, is -37.21%.

The burden of animal disease is widespread globally and is especially severe for developing countries dependent on livestock production. Ethiopia has the largest livestock population in Africa and the second-largest human population on the continent. Ethiopia is one of the fastest-growing economies in Africa; however, much of the population still lives in extreme poverty, and most households depend on agriculture. Animal disease negatively affects domestic livestock production and limits growth potential across the domestic agricultural supply chain. This research investigates the economic effects of livestock disease burden in Ethiopia by employing a computable general equilibrium model in tandem with animal health loss estimates from a compartmental livestock population model. Two scenarios for disease burden are simulated to understand the effects of improved animal health on domestic production, prices, trade, gross domestic product (GDP), and economic welfare in Ethiopia. Results show that improved animal health may increase Ethiopian GDP by up to 3.6%, which improves national welfare by approximately $US 2.5 billion. This research illustrates the economic effects of improved livestock health, which is critical for Ethiopian households and the national economy.

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.

A stable copy of the systematic map dataset of the most recent evidence on ruminant production-limiting disease prevalence and associated mortality in Ethiopia in the form of an excel file is provided here to ensure long term storage of the dataset. This systematic map dataset can be accessed through the livestockdata.org website via a Tableau visualization which has extensive filtering and searching capabilities. As new references are added, the dashboard will be updated and act as a living systematic map of the ruminant disease evidence in Ethiopia. Updates will happen at least twice a year using a machine learning methodology applied by informatics experts. The development of the dataset was funded by the Bill & Melinda Gates Foundation (Grant no: R83537) through the work of SEBI-Livestock.

This dataset is part of Work Package 1 (WP1) of the HABITABLE project, which aims to expand knowledge on migration decision-making processes under climate change by examining the existence of social tipping points where social-ecological systems are reconfigured due to nonlinear changes. The dataset comprises rich and novel longitudinal data collected from households in Ethiopia, Ghana, Kenya, Mali, and Thailand to assess whether such social tipping points exist and how they operate.

Recommended use

1. Migration research under climate change
2. Social tipping points analysis
3. Adaptive capacity and resilience studies
4. Gendered adaptation strategies research

Study design and data collection

• Study Type: Longitudinal prospective observational study
• Number of Waves: 2 (administered with at least 12 months between each wave between 2022 and 2024)
• Target Population: Adults aged 18+ living primarily in rural areas
• Survey Instruments:
  • Household Questionnaire (administered to household heads)
  • Spouse Questionnaire (administered to the household head’s spouse/partner)
• Sampling Strategy: Multi-stage cluster sampling
• Data Collection Method: Computer-Assisted Personal Interviewing (CAPI)
• Ethical Considerations: Ethical approval obtained from relevant institutional review boards; all participants provided informed consent.

Abstract

To facilitate the use of data collected through the high-frequency phone surveys on COVID-19, the Living Standards Measurement Study (LSMS) team has created the harmonized datafiles using two household surveys: 1) the country’ latest face-to-face survey which has become the sample frame for the phone survey, and 2) the country’s high-frequency phone survey on COVID-19.

The LSMS team has extracted and harmonized variables from these surveys, based on the harmonized definitions and ensuring the same variable names. These variables include demography as well as housing, household consumption expenditure, food security, and agriculture. Inevitably, many of the original variables are collected using questions that are asked differently. The harmonized datafiles include the best available variables with harmonized definitions.

Two harmonized datafiles are prepared for each survey. The two datafiles are: 1. HH: This datafile contains household-level variables. The information include basic household characterizes, housing, water and sanitation, asset ownership, consumption expenditure, consumption quintile, food security, livestock ownership. It also contains information on agricultural activities such as crop cultivation, use of organic and inorganic fertilizer, hired labor, use of tractor and crop sales. 2. IND: This datafile contains individual-level variables. It includes basic characteristics of individuals such as age, sex, marital status, disability status, literacy, education and work.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals

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

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

Ethiopia Socioeconomic Survey (ESS) 2018-2019 and Ethiopia COVID-19 High Frequency Phone Survey of Households (HFPS) 2020 data were harmonized following the harmonization guidelines (see “Harmonized Datafiles and Variables for High-Frequency Phone Surveys on COVID-19” for more details).

The high-frequency phone survey on COVID-19 has multiple rounds of data collection. When variables are extracted from multiple rounds of the survey, the originating round of the survey is noted with “_rX” in the variable name, where X represents the number of the round. For example, a variable with “_r3” presents that the variable was extracted from Round 3 of the high-frequency phone survey. Round 0 refers to the country’s latest face-to-face survey which has become the sample frame for the high-frequency phone surveys on COVID-19. When the variables are without “_rX”, they were extracted from Round 0.

Response rate

See “Ethiopia - Socioeconomic Survey 2018-2019” and “Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020” available in the Microdata Library for details.

Abstract

Arba Minch HDSS operates in nine Kebeles (the smallest administrative unit in Ethiopia) of the Arba Minch Zuria district starting form 2009 (the distric have 31 Kebeles). One of the Kebele is urban and the remaining eight are rural. Census was conducted in 2009 updating has been conducted every 6 month (biannually). During each round, the site collects information related to births, deaths, marriages and migrations (Core data of the site).

The main objective of the site is to monitor basic vital events indicators and generate relevant health, demographic and socioeconomic information for policies and programs. In addition, the site will supports graduate and post graduate level research undertakings and conduct molecular to population level collaborative research with local and international stakeholders. Mortality has been measured using WHO Verbal autopsy questionnaire (2007 and 2012 WHO-VA questionnaire) and cause of death are examined. Use of WHO-VA questionnaire 2012 model was started from September 2014. Another basic objective is to study changes in marriage and fertility patterns, household, family and kinship. During the census, there were 63,276 (49.46% female) individuals in the Kebeles of the site living in 12,907 households.

Geographic coverage

Demographic Surveillance area situated in Southern Nations, Nationalities and Peoples Region (SNNPR) of Ethiopia, which is located to the south part of the country.

Analysis unit

Individual

Universe

The demographic surveillance covered currently resident household members of all Kebeles (small adminstratiove units in Ethiopia).

Kind of data

Event history data

Frequency of data collection

Two rounds per year

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance area.

Sampling deviation

Not Applicable

Mode of data collection

Proxy Respondent [proxy]

Research instrument

• Household register
• Pregnancy outcome form
• Pregnancy observation form
• In migration form
• Out migration form
• Death form
• Marital change form

Response rate

Year Response Rate 2009 100% 2010 100% 2011 100% 2012 100% 2013 100% 2014 100%

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate ET061 MicroDataCleaned Starts 100876 2017-05-18 11:42
ET061 MicroDataCleaned Transitions 0 235728 235728 0 2017-05-18 11:43
ET061 MicroDataCleaned Ends 100876 2017-05-18 11:43
ET061 MicroDataCleaned SexValues 235728 2017-05-18 11:43
ET061 MicroDataCleaned DoBValues 8 235720 235728 0 2017-05-18 11:43

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories 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. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

Time series data for the statistic Contributing family workers, male (% of male employment) (modeled ILO estimate) and country Ethiopia. Indicator Definition:Contributing family workers are those workers who hold "self-employment jobs" as own-account workers in a market-oriented establishment operated by a related person living in the same household.The indicator "Contributing family workers, male (% of male employment) (modeled ILO estimate)" stands at 22.66 as of 12/31/2023, the lowest value at least since 12/31/1992, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -2.56 percent compared to the value the year prior.The 1 year change in percent is -2.56.The 3 year change in percent is -5.16.The 5 year change in percent is -1.34.The 10 year change in percent is -5.57.The Serie's long term average value is 23.47. It's latest available value, on 12/31/2023, is 3.45 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1992, to it's latest available value, on 12/31/2023, is -6.23%.

This dataset supports the manuscript “Climate change incidence, risk perception, and food security among smallholders in Tigray, Ethiopia†. The dataset contains three folders and a file from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on climate change conducted in August 2019 in Tigray; and (3) 4km x 4km monthly grided Climate data (Rainfall, Max & min temperature). The files include socioeconomic data and household features, beliefs and opinions on climate change, and climatological data (monthly rainfall, maximum and minimum temperatures). The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and represents the region. It can be useful for studies on climate change risk perception and adaptation, environmental protection, and..., I collected socioeconomic data from the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for the Tigray region. I also conducted a follow-up survey on the beliefs and opinions of respondents on climate change in August 2019 in Tigray. I also collected climatological data (Rainfall, Max & min temperature) from the Ethiopian National Meteorological Services Agency (NMA) for the years 1983 - 2015. I processed the socioeconomic data using user-written codes in STATA v.17, the climatological data using R. I performed a Fixed effects analysis of climate change risk perception and random effects Ordered Logit analysis of food insecurity determinants using the socioeconomic data and climate change trend analysis using climatological data., , # Climate change incidence, risk perception, and food security nexus

https://doi.org/10.5061/dryad.76hdr7t3d

The dataset contains three folders and one file from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on land use change conducted in August 2019 in Tigray; and (3) 4km x 4km monthly grided Climate data (Rainfall, Max & min temperature).

Description of the data and file structure

This dataset underpins the research presented in the manuscript titled “Assessing Climate Change Impacts on Risk Perception and Food Security Among Small-Holders in Tigray, Ethiopia: A Panel Data Analysis with Trend and Variability Tests,†currently under review by Hindawi. The dataset is composed of two core data...

Abstract copyright UK Data Service and data collection copyright owner.The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.Further information about the survey, including publications, can be downloaded from the Young Lives website. SN 9007 - Young Lives: Head Teacher Telephone Survey, Ethiopia and India, 2020In 2020, a follow-up survey of head teachers was conducted to understand how the COVID-19 situation affected schools in Ethiopia and India. The survey investigated how schools provided support to children and families while schools remained closed, the effects of this on children's learning, and their plans for reopening. The survey was conducted by Policy Studies Institute (PSI) in Ethiopia, Centre for Economic and Social Studies (CESS) in India and the University of Oxford. Both PSI and CESS obtained permission from the government for this survey to be conducted with head teachers. The research was done in collaboration with the REAL Centre, who were also carrying out surveys of head teachers (and teachers) during the school closures. Main Topics: The main topics include:Covid-19 effects on schools and educationschool closures due to the pandemicschool support to children' and families during the Covid-19 pandemic Purposive selection/case studies Telephone interview: Computer-assisted (CATI)

Abstract

Tigray is one of the nine administrative regions in Ethiopia. It is comprised of seven zones, of which the Eastern zone is base for the Kiltie Awlaelo Health and Demographic Surveillance Site. The Kiltie Awlaelo HDSS includes 10 kebeles (districts) selected from Eastern zone considering agroclimatic, rural/urban and other several factors to assure representativeness. Nine of the study districts are rural and only one is from urban. The site is located 802 km North of Addis Ababa, the capital of Ethiopia.The surveillance was started in 2009, with a baseline population of 65, 848 (urban 87.2% and 13.7% from rural) living in 14,454 households.

The objective of this surveillance is to provide important demographic and health related indicators with international, national and local policy importance. In this surveillance, socio-demographic characterstics, dates of birth, death, in-migration and outmigration and martial chage are continiously updated. Socio-demographic characterstics are updated once per year and events like birth, death, inmigration and outmigration and marital status change are updated every six months.

Geographic coverage

Kilite Awlaelo HDSS has 10 kebelles (smallest administrative unit in Ethiopia). Nine of them are rural kebelles and one kebelle is urban.

Analysis unit

Individual

Universe

All residents of the HDSS

Kind of data

Event history data

Frequency of data collection

Two rounds per year

Sampling procedure

Not Applicable

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

The following forms were used: - Registration - Migration: (a) Inmigration, (b) Outmigration - Pregnancy: (a) Pregnancy Observation, (b) Pregnancy Outcome - Residence - Birth - Death

Cleaning operations

Data was left censored to 1 Jan 2010 to account for the start-up phase of the surveillance.

Response rate

Response rate in near to 100%

Sampling error estimates

Not Applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate ET031 MicroDataCleaned Starts 81069 2017-05-16 09:38
ET031 MicroDataCleaned Transitions 183204 183204 0 2017-05-16 09:38
ET031 MicroDataCleaned Ends 81069 2017-05-16 09:38
ET031 MicroDataCleaned SexValues 183204 2017-05-16 09:38
ET031 MicroDataCleaned DoBValues 4 183200 183204 0 2017-05-16 09:38