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The Gross Domestic Product (GDP) in Ethiopia was worth 163.70 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Ethiopia represents 0.15 percent of the world economy. This dataset provides the latest reported value for - Ethiopia GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
This statistic shows the share of economic sectors in the gross domestic product (GDP) in Ethiopia from 2013 to 2023. In 2023, the share of agriculture in Ethiopia's gross domestic product was 35.79 percent, industry contributed approximately 24.48 percent and the services sector contributed about 36.98 percent.
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Observations for the current and future years are projections.
The IMF provides these series as part of their Regional Economic Outlook (REO) reports. These reports discuss recent economic developments and prospects for countries in various regions. They also address economic policy developments that have affected economic performance in their regions and provide country-specific data and analysis.
For more information, please see the Regional Economic Outlook (https://www.imf.org/en/publications/reo) publications.
Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available here (http://www.imf.org/external/terms.htm).
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This paper examines the co-benefits of a pathway to net zero emissions (NZE) in Ethiopia focusing on the economic, social and environmental impacts of climate change mitigation and adaption. Using a novel, participatory, systems dynamics modeling approach – the Ethiopia Green Economy Model (GEM) – the authors assess a NZE pathway against a business-as-usual (BAU) scenario to 2050. Assumptions, design of the model, and features of the pathways were gathered through a collaborative initiative, working with representatives of the government of Ethiopia and local experts. The assessment compares the costs of implementing BAU versus NZE development pathways to the co-benefits of climate action. A key policy question is: how will climate action impact growth and investment as well as poverty, income inequality, employment, and ecosystem services? This analysis shows that moving onto a NZE pathway could raise Ethiopia’s GDP growth to 8.1 percent compared to 6.7 percent per year under BAU from 2020 to 2050. Implementation of NZE is estimated to raise cumulative investment costs as well compared to BAU by 2050 but yields significantly more in co-benefits and avoided costs combined, with the latter mainly from energy savings. Economic performance under the NZE pathway will bring about economic structural change, with a decline in agricultural GDP offset by growth in industry and the service sector. Beyond economic growth, a NZE pathway is expected to create employment co-benefits, adding green jobs, while also bringing about faster reduction of extreme and moderate poverty and raising average disposable income. Overall, this broad economy-wide analysis shows a benefit to cost ratio (BCR) greater than 1 by 2030, with $1.04 of benefits generated for every dollar invested, rising to nearly triple this by 2050. Implementation challenges include the need for a dedicated financing strategy and complementary policies to ensure a just transition for unskilled workers; not examined in any detail here, these are topics ripe for future work. Investments in climate mitigation and adaptation in Ethiopia can synergize development along a 2050 NZE pathway, delivering net zero GHG emissions as well as tangible co-benefits across economic, social and environmental outcomes.Higher levels of investment in the NZE scenario leads to faster, more sustainable and inclusive growth compared to BAU in the longer term.Early introduction of NZE actions and policies in land use and forestry, and in energy and transport sectors, improve development outcomes but also present trade-offs, between skilled and unskilled workers for example, for a just transition that require complementary policy effort.Delay in shifting to a NZE pathway risks hindering economic progress and poverty reduction in a future increasing shaped by climate change. Investments in climate mitigation and adaptation in Ethiopia can synergize development along a 2050 NZE pathway, delivering net zero GHG emissions as well as tangible co-benefits across economic, social and environmental outcomes. Higher levels of investment in the NZE scenario leads to faster, more sustainable and inclusive growth compared to BAU in the longer term. Early introduction of NZE actions and policies in land use and forestry, and in energy and transport sectors, improve development outcomes but also present trade-offs, between skilled and unskilled workers for example, for a just transition that require complementary policy effort. Delay in shifting to a NZE pathway risks hindering economic progress and poverty reduction in a future increasing shaped by climate change.
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This dataset examines financial inclusion and bank stability in Ethiopia, containing panel data from 17 commercial banks over the period 2015-2023. In 2015, there were 17 commercial banks in Ethiopia but to maintain confidentiality, the names of commercial banks have been anonymized and are referred to by generic labels: 1, 2, 3, 4..., and 17. This process allows the dataset to be analyzed and shared openly in support of reproducibility and transparency in research.VariablesBank Stability (ZS): Computed using the Z-score to measure stability.Financial Inclusion Index (IFI): Developed using two-stage Principal Component Analysis (PCA) with 10 conventional and 5 digital indicators.Loan to Deposit Ratio (LDR): Computed based on the loan to deposit ratio.Provision to Loan (PL): Computes the loan loss provision ratio.Natural Logarithm of Total Assets (lnTA): Logarithmic form of total assets.Capital Adequacy Ratio (CAR): Computed by Tier-1 capital and Tier-2 capital divided by risk-weighted assets.Income Diversification (IND): Computed based on the non-interest income to total income ratio.Operational Efficiency Management (EF): Measured using Data Envelopment Analysis (DEA) with five input variables (salary and benefits, provisions, general expenses, branches, and deposits) and two output variables (net interest income and non-interest income).Real Lending Interest Rate (RLIR): Inflation-adjusted interest rate.GDP Growth Rate (GDP): Annual percentage change in GDP.This dataset provides comprehensive insights into the relationships between financial inclusion and bank stability, supporting future research and policy formulation.
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.
National Regional Urban and Rural
The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.
Sample survey data [ssd]
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.
Computer Assisted Personal Interview [capi]
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.
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.
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.
The World Bank is interested in gauging the views of clients and partners who are either involved in development in Ethiopia or who observe activities related to social and economic development. The World Bank Country Assessment Survey is meant to give the Bank's team that works in Ethiopia, more in-depth insight into how the Bank's work is perceived. This is one tool the Bank uses to assess the views of its critical stakeholders. With this understanding, the World Bank hopes to develop more effective strategies, outreach and programs that support development in Ethiopia. The World Bank commissioned an independent firm to oversee the logistics of this effort in Ethiopia.
The survey was designed to achieve the following objectives: - Assist the World Bank in gaining a better understanding of how stakeholders in Ethiopia perceive the Bank; - Obtain systematic feedback from stakeholders in Ethiopia regarding: · Their views regarding the general environment in Ethiopia; · Their perceived overall value of the World Bank in Ethiopia; · Overall impressions of the World Bank as related to programs, poverty reduction, personal relationships, effectiveness, knowledge base, collaboration, and its day-to-day operation; and · Perceptions of the World Bank's communication and outreach in Ethiopia. - Use data to help inform the Ethiopia country team's strategy.
National
Stakeholder
Stakeholders of the World Bank in Ethiopia
Sample survey data [ssd]
In December 2011, 620 stakeholders of the World Bank in Ethiopia were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in the survey were drawn from among the office of the President or Prime Minister; the office of a Minister; the office of a Parliamentarian; employees of a ministry, ministerial department, or implementation agency; consultants/contractors working on World Bank supported projects/programs; project management units (PMUs); local government officials or staff; bilateral or multilateral agencies; private sector organizations; NGOs (including CBOs); the media; independent government institutions; trade unions; academia, research institutes or think tanks; and the judiciary.
Mail Questionnaire [mail]
The Questionnaire consists of 8 Sections:
Background Information: The first section asked respondents for their current position; specialization; familiarity, exposure to, and involvement with the Bank; geographic location; and age.
General Issues facing Ethiopia: Respondents were asked to indicate what they thought were the most important development priorities, which areas would contribute most to poverty reduction and economic growth in Ethiopia, whether Ethiopia is headed in the right direction, and whether the economy and standard living has improved in the past five years, as well as rating the extent to which Ethiopia was headed in the right direction in terms of specific development areas.
Overall Attitudes toward the World Bank: Respondents were asked to rate the extent to which the Bank meets Ethiopia's need for knowledge services, the extent to which the Bank encourages the government to see through reforms, and their agreement with various statements regarding the Bank's programs, poverty mission, relationships, and collaborations in Ethiopia. Respondents were also asked to indicate the areas on which it would be most productive for the Bank to focus its resources and research, what the Bank's level of involvement should be, what they felt were the Bank's greatest values and greatest weaknesses in its work, and with which groups the Bank should work more.
The Work of the World Bank: Respondents were asked to rate their level of importance and the Bank's level of effectiveness across twenty-two areas in which the Bank was involved, such as helping to reduce poverty and encouraging greater transparency in governance.
The Way the World Bank does Business: Respondents were asked to rate the Bank's level of effectiveness in the way it does business, including the Bank's knowledge, personal relationships, collaborations, and poverty mission.
Project/Program Related Issues: Respondents were asked to rate their level of agreement with a series of statements regarding the Bank's programs, day-to-day operations, and collaborations in Ethiopia.
The Future of the World Bank in Ethiopia: Respondents were asked to rate how significant a role the Bank should play in Ethiopia's development and to indicate what the Bank could to make itself of greater value and to what reasons respondents attributed failed or slow reform efforts.
Communication and Outreach: Respondents were asked to indicate where they get information about development issues and the Bank's development activities in Ethiopia, as well as how they prefer to receive information from the Bank. Respondents were also asked to indicate their usage of the Bank's website, PICs, and Development Information Corners, and to evaluate these communication and outreach efforts.
A total of 326 stakeholders participated in the country survey (53%).
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Graph and download economic data for U.S. Exports of Goods by F.A.S. Basis to Ethiopia (EXP7749) from Jan 1993 to May 2025 about Ethiopia, exports, goods, and USA.
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.
National Regional Urban and Rural
The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.
Sample survey data [ssd]
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.
Computer Assisted Personal Interview [capi]
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.
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.
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).
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.
National coverage
Households
The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.
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.
Computer Assisted Personal Interview [capi]
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.
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.
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).
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The poverty headcount in Ethiopia is falling. The share of the population below the national poverty line decreased from 30 percent in 2011 to 24 percent in 2016. This decrease was achieved in spite of the fact that the 2015-16 survey was conducted during the severe El-Nino drought. The observed reduction in poverty is robust to the use of alternative deflators. The fall in the poverty headcount was driven mainly by Ethiopia’s strong economic growth over that period. This poverty assessment focuses on the evolution of poverty and other social indicators in Ethiopia between 2011 and 2016. It uses data from a variety of sources, mainly the Household Consumption and Expenditure Survey (HCES), the Welfare Monitoring Surveys (WMS), the Ethiopia Socioeconomic Survey (ESS) and the Demographic and Health Surveys (DHS), to observe trends in monetary and non-monetary dimensions of living standards and to examine the drivers of these trends, with a special focus on government programs. The aim of the poverty assessment is to provide policymakers and development partners with information and analysis that can be used to improve the effectiveness of their poverty reduction and social programs.
Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.
By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.
National coverage.
Individuals
The target population is the civilian, non-institutionalized population 15 years and above.
Observation data/ratings [obs]
The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.
In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.
Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.
In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.
The sample size was 1000.
Computer Assisted Personal Interview [capi]
The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.
Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.
Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank
The Smartphone Devices eCommerce market in Ethiopia is predicted to reach US$4.4m revenue by 2025, reflecting an estimated growth rate of 17% compared to 2024.
Agriculture accounts for 85 percent of employment and 46 percent of GDP in Ethiopia. As a result, development in Ethiopia depends on strengthening rural capacity through extension services and through supporting farmer associations and training centers. However, it is difficult for such development to be equal across gender because women farmers have less access to agricultural technology. Given that women account for about 60 percent of agricultural labour in Ethiopia, it is important to understand how and why they differ from men in Ethiopia's agricultural sector. The Farmer Innovation Fund (FIF) is a component of the Rural Capacity Building Projects (RCBP) which seeks to strengthen the extension system and increase gender equality in extension services. FIF provides funds to farmer groups to implement innovative ideas developed and partially funded by the groups themselves. FIF also plans to decentralize funding from the woreda, or ward, level to the farmer training center level. To evaluate the effectiveness of FIF, an impact evaluation study was conducted in Amhara and Tigray states, where FIF was rolled out as a randomized intervention. The impact evaluation included three surveys: a baseline, conducted in August-October 2010; a midline, carried out in April 2012; and an end line, administered in June 2013. The data collected from the surveys examined how women-only training programs effect women's participation in agricultural and extension services and which kind of training package is the most effective in improving women's economic empowerment. In addition, the impact evaluation studied the effects that participation in training has on intra-household allocation of resources, decision making within households, and domestic violence. Also, variables related to food consumption enabled an analysis of how training programs affect children's nutrition.
The baseline survey covered 2,675 households. Within each household, surveys were given to men and women. From the 2,675 households, 869 are non-FIF households that were used as a pure control group and the remaining 1,806 were FIF households. A simple lottery design was used to randomly assign half of FIF households to the treatment group and half to the control group. In addition to the FIF households, women in treatment households received FIF training, while women in the control households did not.
Regional
Households
Sample survey data [ssd]
A FIF subset was rolled out in 40 of the 100 targeted kebeles, or neighbourhoods, as a randomized intervention at the farmer-group level in Amhara and Tigray regions. The sample size of baseline survey was 2,675 households. A simple lottery among the FIF sample was used to divide the sample into treatment and control groups. Specifically, from the 2,675 households, 1,806 were randomly assigned to the treatment group and 869 were assigned to the control group. In the FIF program the incentive was given both to male and female FIF members while nothing was given to those in the control group.
Face-to-face [f2f]
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This paper addresses knowledge gaps in the biomass, productivity and value of livestock for the pastoral, mixed crop-livestock and specialized dairy systems in Ethiopia. Population size, reproductive performance, mortality, offtake and productivity of cattle were calculated from official statistics and a meta-analysis of data available in the published literature. This information was then used to estimate biomass and output value for 2020 using a herd dynamics model. The mixed-crop livestock system dominates the Ethiopian cattle sector, with 55 million cattle (78% total population) and contributing 8.52 billion USD to the economy through the provision of meat, milk, hides and draft power in 2021. By comparison, the pastoral (13.4 million head) and specialized dairy (1.8 million head) systems are much smaller. Productivity varied between different production systems, with differences in live body weight, productivity and prices from different sources. The estimated total cattle biomass was 14.8 billion kg in 2021, i.e., 11.3 billion kg in the mixed crop-livestock system, 2.60 billion kg in the pastoral system and 0.87 billion kg in the specialized dairy system. The total economic asset values of cattle in the mixed crop-livestock, pastoral and specialized dairy systems were estimated as 24.8, 5.28 and 1.37 billion USD, respectively. The total combined output value (e.g., beef, milk and draft power) of cattle production was 11.9 billion USD, which was 11.2% of the GDP in Ethiopia in 2021. This work quantifies the importance of cattle in the Ethiopian economy. These estimates of herd structure, reproductive performance, productivity, biomass, and economic value for cattle production systems in Ethiopia can be used to inform high-level policy, revealing under-performance and areas to prioritize and provide a basis for further technical analysis, such as disease burden.
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Ethiopia has seen many changes since 2016, which until now, has been the reference year for data about the level and pattern of poverty in the country. The narrative around poverty was that years of high growth resulted in a significant reduction in poverty, but by less than expected because growth was uneven between rural and urban areas which received most of the gains from growth and there was a slow shift of labor from agriculture into the fast-growing segments of the economy. Since 2016, GDP per capita growth has decelerated - to 4.6 percent during 2016-2022 compared to nearly 7.4 percent during 2010-2016 - not least because of multiple crises, including a global pandemic, droughts, locust infestation, conflict, and market shocks. This Poverty and Equity Assessment (PEA) updates the understanding of poverty and inequality in the country, using new data collected from 2021. This data was collected amidst security concerns, which posed challenges during the data collection process. Despite these challenges, data quality checks have verified that the collected information is reliable and representative of the country, excluding areas that were inaccessible, such as Tigray. The PEA updates statistics on poverty rates, inequality, the poverty profile, and identifies the drivers of these trends (Part 1). It provides an in-depth understanding of the key drivers of poverty in the country (Part 2) and charts the course for reducing poverty in the years to come (Part 3). Below are some high-level messages drawn from the analysis presented in the seven chapters of the report. Additional details are accessible in background papers accompanying the report.
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Graph and download economic data for U.S. Imports of Goods by Customs Basis from Ethiopia (IMP7749) from Jan 1993 to Apr 2025 about Ethiopia, imports, goods, and USA.
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The relationship between inflation, income inequality, and economic growth is a subject of intense debate among economic researchers and policymakers. This study aims to analyze this relationship in Ethiopia using advanced statistical techniques such as VEC (vector error correction) model with Granger causality, and Johansen’s cointegrated. The study covers the period from 1980 to 2022 and includes pre and post-estimation diagnosis tests to ensure the accuracy of the model. The results indicate the presence of a long-run relationship among inflation, income inequality, and economic growth, as confirmed by Johansen’s cointegrated test. Additionally, the vector error correction model shows a strong long-run relationship between economic growth, income inequality, and inflation. In the short run, there is a significant association between income inequality and economic growth, as well as between inflation and economic growth. The Granger causality test reveals a bidirectional causality between economic growth and income inequality and between economic growth and inflation. However, there is a unidirectional causality from inflation to income inequality. Based on these findings, it is suggested that the government should implement various strategies and policies, including redistribution policies, social safety nets, promoting inclusive economic growth, coordinating effective monetary and fiscal policies, implementing progressive taxation, and reforming the labor market.
The UN Joint Programme focused on Rural Women’s Economic Empowerment (UNJP-RWEE) was launched in Ethiopia in 2014 by UN Women, the Food and Agriculture Organization of the UN (FAO), the World Food Programme (WFP), and the International Fund for Agriculture Development (IFAD). UNJP-RWEE was a five-year long initiative with the objective of accelerating the economic empowerment of rural women in the regions of Oroma and Afar. The project provided women with greater access to credit through women-run rural savings and credit cooperatives (RUSACCOs), as well as numeracy, literacy, finance, and business-development training; agricultural livestock and technology transfers; agricultural training; and community-run educational conversations in healthy eating choices and nutrition. To assess the extent to which the UNJP was effective in empowering women economically, an impact evaluation was conducted by the FAO in partnership with IFAD, and IFPRI.
The FAO received a grant from GAAP2-IFPRI, facilitated by the Gates Foundation, to conduct a quasi-experimental impact evaluation with a difference-in-difference approach using a revised version of the Women’s Empowerment in Agriculture Index (WEAI), the Pro-WEAI. In Oromia, the list of beneficiaries including their Kebeles were retrieved from the baseline survey. In total 750 households were surveyed in the Oromia region. In Afar, the number of beneficiary households interviewed increased to 450, 250 of which were beneficiaries and the rest control. In addition to the 95 beneficiary household included at baseline, 155 new beneficiaries were included. Two additional control Kebeles were also included in Afar. Follow-up interviews were conducted with 389 women in the beneficiary communities and 358 women in the comparison communities, and 303 men in the beneficiary households and 314 men in the comparison communities. In all, there are 736 households where the same female respondent was administered the survey at both baseline and end-line.
Regional coverage
Households
Sample survey data [ssd]
In the Oromia National Regional State, the same Woredas that were covered in the baseline are maintained. The list of project beneficiaries including their Kebeles was retrieved from the baseline survey which was conducted in 2017. In addition, control Kebeles were also obtained from the previous survey database. In the baseline, in the beneficiary communities, a random sample was drawn from the RUSSACO members, and from comparable kebeles. Six beneficiary kebeles were selected in Oromia: (1) Illuf Dirre and (2) Nannoo Chemerri in the Yaya Gulele Woreda; (3) Bura Adelle and (4) Wabe Burkitu in the Dodola Woreda; and (5) Abine Garmamme and (6) Annenno Shisho in the Adami Tulu Woreda. The comparison kebeles are adjacent communities in which the UNJP-RWEE does not operate but that are similar in size; have similar agricultural systems, livelihoods; and cultural norms, and thus are deemed valid counterfactuals. In Oromia, the control communities are: (1) Lemi; (2) Dedfe; (3) Haleko Gulenta Boke; (4) Werji Washingula; (5) Baressa; and (6) Keta Berenda. In Afar National Regional State, in addition to households interviewed at baseline, an additional 150 beneficiary households were interviewed in: (1) Asboda and (2) Boyina in the Dubti Woreda, and an additional 50 control households were interviewed from a newly selected Kebeles. The control communities were therefore (1) Hanikesen and (2) Aredo; (3) Gudmaydil; and (4) Gayder).
Computer Assisted Personal Interview [capi]
Population as a producer and consumer is closely related with agriculture. On the one hand, population affects production in general and agricultural outputs in particular by furnishing the required labour. On the other hand, the size of a population and its anticipated growth is the main factor determining food consumption requirements. Regarding the balance between population and consumption, if more people are to be fed than the food or services produced, saving and capital investments will be negatively affected. Moreover, population growth also negatively influences agriculture by putting pressure on the environment, such as water, fertility of land, etc. Population size further influence productivity mainly through the diversification and specialization of the economy, the size of the market, and the importance of foreign trade.
Not only the size, but also the socio-economic characteristics of the population of the agricultural households are important to the agricultural production. Study of the nature of the agricultural sector of a country will not be complete without proper understanding of the socio-economic characteristics of the population engaged in it. The population statistics of the agricultural households can be used to describe the characteristics and distribution of the population in space, its density and degree of concentration, the fluctuation in its rate of growth and the movement from one area to another. Data on population and agriculture will also help in finding out what percentage of resources will be needed at a particular time for the meeting of basic needs of the people and what amount of socially useful and productive labour is available in the country, regardless of whether labour or capital intensive techniques will suit the nation's economy.
Generally, an analysis of statistical data on population residing in agricultural households is important to assess the size, structure and characteristics of the human resources involved in and supported by the sector. Such kind of information will provide the human background for planners and policy makers in their attempt to formulate policies that helps to improve the sector's output as well as the living conditions of the rural population.
Cognizant of this fact, the 2001-2002 Ethiopian Agricultural Sample Enumeration has collected basic social and economic characteristics of the population in agricultural households in October 2001.
The 2001-2002 (1994 E.C) Agricultural Sample Enumeration was designed to cover the rural and urban parts of all districts (Weredas) in the country on a large-scale sample basis excluding the pastoralist areas of the Afar and Somali regional states.
Agricultural households from the nationally sampled area. The population in agricultural households comprises of all persons residing in households with at least one agricultural holder, where a holder is defined as a person who exercises management and control over the operation of the agricultural holding such as land and livestock and makes the major decision regarding the utilization of the available resources.
Census/enumeration data [cen]
Sampling Frame The list of enumeration areas for each wereda was compiled from the 1994 Ethiopian Population and Housing Census cartographic work and was used a frame for the selection of the Primary Sampling Units (PSU). The 1994 Population and Housing Census enumeration area maps of the region for the selected sample EA's were updated, and the EA boundaries and descriptions were further clarified to reflect the current physical situation. The sampling frame used for the selection of ultimate sampling units (agricultural households) was a fresh list of households, which was prepared by the enumerator assigned in the sampled EA's using a prescribed listing instruction at the beginning of the launching of the census enumeration.
Sample Design In order to meet the objectives and requirements of the EASE, a stratified two-stage cluster sample design was used for the selection of ultimate sampling units. Thus, in the regions each wereda was treated as stratum for which major findings of the sample census are reported. The primary sampling units are the enumeration areas and the agricultural households are secondary (ultimate) sampling units. Finally, after the selection of the sample agricultural households, the various census forms were administered to all agricultural holders within the sampled agricultural households.
For the private peasant holdings in the rural areas a fixed number (25) of sample EA's in each wereda and 30 agricultural households in each EA were randomly selected (determined). In urban areas, weredas with urban EA's of less than or equal to 25, all the EA's were covered. However, for weredas with greater than 25 urban EA's, sample size of 25 EA's was selected. In each sampled urban EA, 30 agricultural households were randomly selected for the census. The sampled size determination in each wereda and thereby in each EA was based upon the required precision level of the major estimates and the cost consideration. The pilot survey and the previous year annual agricultural sample survey results were used to determine the required sample sizes per wereda.
Sample Selection of Primary Sampling Units Within each wereda (stratum) in the region, the selection of EAs was carried out using probability proportional to size systematic sampling. In this case, size being total number of agricultural households in each EA obtained from the listing exercise undertaken in the 1994 Ethiopian Population and Housing Census of the region.
Listing of Households and Selection of Agricultural Households In each sampled enumeration area of the region, a complete and fresh listing of households was carried out by canvassing the households in the EA. After a complete listing of the households and screening of the agricultural households during the listing operation in the selected EA, the agricultural households were serially numbered. From this list, a total of 30 agricultural households were selected systematically using a random start from the pre-assigned column table of random numbers. The sampling interval for each EA was determined by dividing the total number of agricultural households by 30. For crop cutting exercise purposes (rural domain) a total of 20 agricultural households were randomly selected from the 30 sampled agricultural households. The systematical random sampling technique was employed in this case, because its application is simple and flexible, and it can easily yield a proportionate sample.
Face-to-face [f2f]
The rural census questionnaires/forms included:- - Forms 94/0 and 94/1 that are used to record all households in the enumeration area, identify the agricultural households and select the units to be covered by the census. - Form 94/2 is developed to list all the members of the sampled agricultural households and record the demographic and economic characteristics of each of the members. - Forms 94/3A, 94/3B, 94/3C and 94/3D are prepared to enumerate crop data through interview and objective measurement. - Form 94/5 is designed to record crop area data via the physical or objective measurement of crop fields. - Form 94/6 is used to list all the fields under crop and select a crop field for each type of crop randomly for crop cutting exercise. - Forms 94/7A, 94/7B, and 94/7C are developed for recording yield data on cereals, oil seeds, pulses, vegetables root crops and permanent crops by weighing their yields obtained from sub-plots and/or trees selected for crop-cuttings. - Form 94/8 is prepared to enumerate livestock, poultry and beehives data by type, age, sex and purpose including products through interview (subjective approach). - Forms 94/9, 94/10 and 94/11 are used to collect data on crop and livestock product usage; miscellaneous items and farm tools, implements, draught animals and storage facilities, in that order, by interviewing the sample holders. - The last but not least forms are the "Belg" season questionnaires identified as: - 94/12A and 94/12B that are used to record data on farm management practices of the "Belg" season. - Form 94/4 was the questionnaire used for collecting data on crop production forecast for 2001-2002 and the data collected using this form was published in December 2001 subjectively, while 94/12C is for recording "Belg" season crop area through objective measurement and volume of production through interview approach.
On the other hand, the census questionnaires/forms used in the urban areas include:- - Form U-94/1 which used to record all households in the EA, identify the agricultural households and select the units to be covered by the census. - Form U-94/2 is developed to list all the members of the sampled agricultural household and record the demographic and economic characteristics of each of the members. - From U-94/3 is prepared to enumerate crop data through interview method. - Form U-94/4 is prepared to enumerate livestock, poultry and beehives data by type, sex, age and purpose including products through interview (subjective approach). - Form U-94/5 is used to collect data on crop and livestock usage.
Editing, Coding and Verification: In the 2001-2002 Ethiopian Agricultural Sample Enumeration (EASE), the filled-in forms that were retrieved from 47 Branch Statistical Offices were primarily received and systematically registered at
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The Gross Domestic Product (GDP) in Ethiopia was worth 163.70 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Ethiopia represents 0.15 percent of the world economy. This dataset provides the latest reported value for - Ethiopia GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.