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.

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.

Techsalerator's News Events Data for Ethiopia: A Comprehensive Overview

Techsalerator's News Events Data for Ethiopia is an essential resource for businesses, researchers, and media organizations. This dataset compiles information on significant news events across Ethiopia from a wide range of media sources, including news outlets, online publications, and social platforms. It provides valuable insights for those looking to track trends, analyze public sentiment, or monitor industry-specific developments.

Key Data Fields

Event Date: Captures the exact date of the news event. This helps analysts monitor trends over time or allows businesses to respond quickly to market shifts.

Event Title: A brief headline describing the event. This allows users to quickly categorize and assess news content based on relevance to their interests.

Source: Identifies the news outlet or platform where the event was reported. This helps users track credible sources and assess the reach and influence of the event.

Location: Provides geographic information, indicating where the event took place within Ethiopia. This is especially valuable for regional analysis or localized marketing efforts.

Event Description: A detailed summary of the event, outlining key developments, participants, and potential impact. Researchers and businesses use this to understand the context and implications of the event.

Top 5 News Categories in Ethiopia

Politics: Major news coverage on government decisions, political movements, elections, and policy changes that shape the national landscape.

Economy: Focuses on Ethiopia’s economic indicators, inflation rates, international trade, and corporate activities influencing business and finance sectors.

Social Issues: News events covering protests, public health, education, and other societal concerns driving public discourse.

Agriculture: A critical sector for Ethiopia, with news covering developments in farming, agribusiness, and food security, which are key to the country’s economy and livelihoods.

Technology and Innovation: Reports on tech developments, startups, and innovations in Ethiopia’s growing tech ecosystem, especially in sectors like fintech and digital services.

Top 5 News Sources in Ethiopia

Addis Standard: A leading publication covering political developments, social issues, and economic trends in Ethiopia.

The Reporter Ethiopia: One of Ethiopia’s most popular news outlets, offering coverage on a wide range of national and regional events.

Fana Broadcasting Corporation: A state-owned broadcaster providing updates on politics, economic developments, and social issues.

Walta Information Center: A major media platform with in-depth reporting on Ethiopia’s political and economic landscapes.

ENA (Ethiopian News Agency): Ethiopia's official news agency, offering timely updates on major government announcements and public events.

Accessing Techsalerator’s News Events Data for Ethiopia

To access Techsalerator’s News Events Data for Ethiopia, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Event Date
• Event Title
• Source
• Location
• Event Description
• Event Category (Politics, Economy, Sports, etc.)
• Participants (if applicable)
• Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is an invaluable tool for keeping track of significant events in Ethiopia. It aids in making informed decisions, whether for business strategy, market analysis, or academic research, providing a clear picture of the country’s news landscape.

The gross domestic product (GDP) in current prices in Ethiopia stood at 142.07 billion U.S. dollars in 2024. From 1980 to 2024, the GDP rose by 134.68 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. Between 2024 and 2030, the GDP will rise by 75.86 billion U.S. dollars, showing an overall upward trend with periodic ups and downs.This indicator describes the gross domestic product at current prices. The values are based upon the GDP in national currency converted to U.S. dollars using market exchange rates (yearly average). The GDP represents the total value of final goods and services produced during a year.

Abstract

Statistical information on all aspects of the population is vital for the design, implementation and evaluation of economic and social development plan and policy issues. Labour force surveys are one of the important sources of data for assessing the role of the population of a country in the economic and social development process. These surveys provide data on the main characteristics of the work force engaged or available to be engaged in productive activities during a given period and also its distribution in the various sectors of the economy. They are also useful to indicate the extent of available and unutilized human resource that must be absorbed by the national economy to ensure full employment and economic well being of the population. Furthermore, the information obtained from such surveys is useful for the purpose of macro-economic monitoring and human resource development planning. The other broad objective of statistics on the labour force is for the measurement of the relationship between employment, income and other social and economic characteristics of the economically active population for the purpose of formulating and monitoring employment policies and programs, income-generating and maintenance schemes, vocational training and other similar programs. Seasonal and other variations in the size and characteristics of the labour force can also be monitored using up-to-date information from labour force surveys.

In order to further fill the gap in data requirement for the socio-economic development planning, monitoring and evaluation, the Central Statistical Authority (CSA) has conducted Rural Labour Force Survey (RLFS) as a part of the National Integrated Household Survey Program (NIHSP) at the end of 1980. To maintain the continuity and to update the Rural Labour Force Survey of 1981/82 results, another Rural Labour Force Survey was conducted in 1987/88. Also the CSA has conducted the 1976 Addis Ababa Manpower and Housing Sample Survey and the 1978 Manpower and Housing survey in Seventeen Major Towns. Moreover, some data on the labour force were also collected as a part of other surveys such as the 1990 Family and Fertility Survey, 1996 Urban Informal Sector Sample Survey and in the country wide deccennial Population and Housing Censuses of Ethiopia conducted in 1984 and 1994.

The labour force surveys that were conducted earlier were limited in areal coverage and content of the questionnaires. In this respect, both the 1981/82 and 1987/88 surveys covered only the rural part of the country. Till the current survey was conducted, there hasn't been a comprehensive national labour force survey representing both the urban and the rural areas of the country. Moreover, the information that should have been provided through labour force surveys could be said relatively out-dated, as the sector is dynamic and sensitive to economic and social changes. To fill this data gap, a series of current and comprehensive labour force surveys need to be undertaken.

Recognizing this fact, the Central Statistical Authority (CSA) has conducted a national labour force survey in March 1999. The survey is the first of its kind in that it covers the rural and the urban areas and it contains detailed information on the subject. The results of this survey have been already released to users in a publication entitled "Statistical Report on the 1999 National Labour Force Survey (NLFS)" and this presented the data in a former of detailed statistical tables including the concepts and definitions on the major technical terms used in the survey. The CSA hopes that users have benefited a lot from this publication. To increase the utility of the result of the survey, the CSA taught that it necessary to make further analysis on the data. The analytical presentation of this report will be based on the tables that have been presented in the statistical report (Report on Statistical Tables of the 1999 Labour Force Survey, CSA, 1999) and some additional tables produced and included in this report. This chapter presents an overview to the survey background. The 1999 National Labour Force survey was designed to provide statistical data on the size and characteristics of the employed, unemployed, underemployed and the non-active population of the country. In general, the data obtained from the survey is useful for policy makers, planners, researchers and other institutions and individuals engaged in the design and implementation of human resource development projects and programs.

The specific objectives of the 1999 National Labour Force Survey are to :- - collect statistical data on the potential manpower who are available to take part in various socio-economic activities - determine the size and distribution of the labour force; and the status and rates of economic activity and also to study the socio-economic and demographic characteristics of these groups - identify those who contributed to economic development and those who are partially employed, without work and economically inactive - to estimate and assess the levels and characteristics of the unemployed population - generate data on the status and type of professional and vocational training - assess the size and characteristics of children aged between 5 - 14 years that were engaged in economic activities - assess the situation of women's employment or the participation of women in the labour force

Geographic coverage

The survey covered both urban and rural parts of the country, except six zones in Somali Region and two zones in Affar Region

Analysis unit

• Household
• Household member
• Household members aged 10 years and over

Universe

The survey covered all households in selected sample areas except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1999 National Labor Force Survey covered both urban and rural parts of the country, except six zones in Somali Region and two zones in Affar Region. In addition the residents of collective quarters, homeless persons and foreigners were not covered in the survey. For the purpose of the survey, the survey population in the country was divided into urban and rural categories.

Category I: Urban parts of 26 zones, that is 4 zones in Tigray, 10 zones in Amhara, and 12 zones in Oromiya regions; and 9 zones and 5 special weredas in SNNP Region; and urban parts of Affar, Somali, Benishangul-Gumuz, Gambela and Harari regions and Addis Ababa and Dire Dawa Administration were grouped in this category. Each of the above mentioned urban parts of the zones, except the 5 special weredas in SNNP Region were the survey domains (reporting levels). All in all 47 basic urban domains (Reporting levels) including total urban (regional and country level) were defined for the survey.

Category II: Rural parts of 26 Zones that is 4 zones in Tigray, 10 zones in Amhara, 12 zones in Oromiya regions and 9 zones and 5 special weredas in SNNP regions; and rural parts of Affar, Somali, Benishangul-Gumuz, Gambela and Harari regions, Addis Ababa and Dire Dawa Administration were grouped in this category. Each of the above mentioned rural parts of zones and special weredas, except Addis Ababa rural, were the survey domains (reporting levels). All in all 51 basic rural domains (reporting levels) including total rural (regional and country level) were defined for the survey. In addition to the above urban and rural domains, survey results can be reported at regional and country levels by aggregating the survey results for the corresponding urban and rural areas. Definition of the survey domains was based on both technical and resource considerations. More specifically, sample sizes for the domains were determined to enable provision of major indicators with reasonable precision subject to the resources that were available for the survey.

Selection Scheme and Sample Size: In both categories stratified two-stage sample design was used to select the sample in which the Primary Sampling Units (PSUs) were enumeration areas (EAs). Sample EAs from each domain were selected using systematic probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. From category I, a total of 913 EAs and from category II, a total of 1428 EAs were selected. Within each sample EA, fresh list of households was prepared at the beginning of the survey's fieldwork for urban sites and at the beginning of the 1991 E.C. Agricultural Sample Survey's fieldwork for rural sites. The survey questionnaire was administered to 35 systematically selected households within each of the sampled EAs.

Note: Distributions of sample units by domain (reporting levels) and category are presented in Table 2.1 and Table 2.2 of the 1999 National Labour Force Survey report which is provided in this documentation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey has used a structured questionnaire to solicit the required data. Before taking its final shape, the draft questionnaire was tested by undertaking a Pilot Study. Based on the result of the pilot study the content, layout and presentation of the questionnaire was amended. The content of the questionnaire has been further revised on the basis of the discussion made on the user - producer forum organized by the CSA. The questionnaire used in the field was prepared in Amharic language and most questions have pre-coded answers and column numbers were assigned for each question.

The questionnaire is organized into six sections: Section-1 Area identification of the selected household: this section has

Ethiopia ET: CPIA: Financial Sector Rating: 1=Low To 6=High data was reported at 3.000 NA in 2017. This stayed constant from the previous number of 3.000 NA for 2016. Ethiopia ET: CPIA: Financial Sector Rating: 1=Low To 6=High data is updated yearly, averaging 3.000 NA from Jul 2005 (Median) to 2017, with 13 observations. The data reached an all-time high of 3.000 NA in 2017 and a record low of 3.000 NA in 2017. Ethiopia ET: CPIA: Financial Sector Rating: 1=Low To 6=High data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Policy and Institutions. Financial sector assesses the structure of the financial sector and the policies and regulations that affect it.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Techsalerator’s Business Funding Data for Ethiopia

Techsalerator’s Business Funding Data for Ethiopia provides a comprehensive and detailed collection of information vital for businesses, investors, and financial analysts. This dataset offers an in-depth analysis of funding activities across various sectors in Ethiopia, capturing and categorizing data related to funding rounds, investment sources, and key financial milestones.

For access to the full dataset, please reach out to us at info@techsalerator.com or visit https://www.techsalerator.com/contact-us.

Techsalerator’s Business Funding Data for Ethiopia

Techsalerator’s Business Funding Data for Ethiopia delivers an extensive overview of critical information for businesses, investors, and financial analysts. This dataset provides a thorough examination of funding activities across different sectors in Ethiopia, detailing data on funding rounds, investment sources, and significant financial milestones.

Top 5 Key Data Fields

• Company Name: Identifies the company receiving funding, aiding investors in spotting potential opportunities and allowing analysts to track funding trends within specific industries.

• Funding Amount: Indicates the total funding a company has received, providing insights into the financial health and growth potential of businesses and the scale of investment activities.

• Funding Round: Specifies the stage of funding, such as seed, Series A, Series B, or later stages. This information helps investors gauge a business’s maturity and growth trajectory.

• Investor Name: Lists the investors or investment firms involved, helping assess the credibility of the funding source and their strategic interests.

• Investment Date: Records when the funding was completed, which can reflect market trends, investor confidence, and potential impacts on a company’s future.

Top 5 Funding Trends in Ethiopia

• Infrastructure Development: Significant investments are being made in infrastructure projects, including roads, bridges, and energy. These investments are essential for Ethiopia’s economic growth and stability.

• Agriculture and Agritech: With agriculture being a cornerstone of Ethiopia’s economy, funding is directed toward modernizing agricultural practices through agritech, focusing on sustainability and productivity.

• Telecommunications and Digital Connectivity: The telecom sector in Ethiopia is receiving investment to enhance digital connectivity and access to information, which is crucial for economic development and social inclusion.

• Healthcare and Pharmaceuticals: Increased funding is flowing into healthcare infrastructure, pharmaceuticals, and health tech to address the healthcare needs of the population and support medical research and innovation.

• Education and Vocational Training: Funding is being allocated to educational initiatives and vocational training programs aimed at improving literacy rates, enhancing skills, and creating employment opportunities.

Top 5 Companies with Notable Funding Data in Ethiopia

• Ethio Telecom: Ethiopia’s leading telecommunications provider, Ethio Telecom, has received significant funding to expand its network coverage, enhance digital services, and support community initiatives.

• Commercial Bank of Ethiopia (CBE): This major financial institution has attracted substantial investment to improve its banking services, expand its reach, and promote financial inclusion.

• Addis Ababa University: Funding has been secured to support educational initiatives, research, and infrastructure improvements at Ethiopia’s largest university.

• EthioChicken: Known for its impact on agricultural productivity, EthioChicken has received funding to enhance poultry farming practices and expand its operations.

• Dabiré Group: This company has garnered investment to advance its activities in agriculture and agritech, focusing on sustainable farming practices and productivity improvements.

Accessing Techsalerator’s Business Funding Data

To obtain Techsalerator’s Business Funding Data for Ethiopia, contact info@techsalerator.com with your specific needs. Techsalerator will provide a customized quote based on the required data fields and records, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Company Name
• Funding Amount
• Funding Round
• Investor Name
• Investment Date
• Funding Type (Equity, Debt, Grants, etc.)
• Sector Focus
• Deal Structure
• Investment Stage
• Contact Information

For detailed insights into funding activities and financial trends in Ethiopia, Techsalerator’s dataset is an invaluable resource for investors, business analysts, and financial professionals seeking informed, strategic decisions.

Abstract

The survey was conducted in Ethiopia between July 2011 and July 2012 as part of the Africa Enterprise Survey 2011 rollout, an initiative of the World Bank. Data from 644 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Ethiopia was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry and one service as defined in the sampling manual. The manufacturing industry had a target of 340 interviews and service industry had a target of 240 interviews.

Size stratification was defined following the standardized definition for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in five regions (city and the surrounding business area): Addis Ababa, Oromya, SNNPR, Amhara, and Tigray.

For the Ethiopia ES, three sample frames were used. The first sample frame was produced by Ethiopia Ministry of Trade and Industry. A copy of that frame was sent to the TNS statistical team in London to select the establishments for interview. However, the quality of the sample frames was not optimal and additional sample frames were acquired during the implementation of the survey in order to reach the target number of interviews. The second sample frame used was the Dun & Bradstreet (D&B) database and the third sample frame was the Ethiopia Yellow Pages 2011.

The enumerated establishments with five or more employees were then used as the sample frame for the Ethiopia Enterprise Survey with the aim of obtaining interviews at 600 establishments.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of noneligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone or fax numbers so the local contractor had to screen the contacts by visiting them. Due to response rate and ineligibility issues, additional sample had to be extracted by the World Bank in order to obtain enough eligible contacts and meet the sample targets.

Given the impact that non-eligible units included in the sample universe may have on results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 21% (392 out of 1,873 establishments) and 12% (37 out of 310 establishments) for the ES firms for the Ministry of Trade and D&B sample frames respectively. The non-eligibility rate for the Yellow Pages sample frame was 16% (98 out of 607 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire [ISIC Rev.3.1: 15-37] - Services Module Questionnaire [ISIC Rev.3.1: 45, 50, 51, 52, 60, 61, 62, 63, 64 & 72] - Screener Questionnaire.

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times, days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.16, 0.38, and 0.36 for formal ES firms using the sample frames from the Ministry of Industry and Trade, D&B, and Yellow Pages respectively. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.06, 0.05 and 0.007 using the sample frames from the Ministry of Industry and Trade, D&B, and Yellow Pages respectively.

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Ethiopia ES 2011 Implementation" in Technical Documents.

Ethiopia ET: Net Official Flows from UN Agencies: WHO data was reported at 2.970 USD mn in 2016. This records a decrease from the previous number of 3.700 USD mn for 2015. Ethiopia ET: Net Official Flows from UN Agencies: WHO data is updated yearly, averaging 2.925 USD mn from Dec 2011 (Median) to 2016, with 6 observations. The data reached an all-time high of 3.700 USD mn in 2015 and a record low of 1.910 USD mn in 2012. Ethiopia ET: Net Official Flows from UN Agencies: WHO data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank: Defense and Official Development Assistance. Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor).). UN agencies are United Nations includes the United Nations Children’s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), , United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), Wolrd Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), and International Labour Organization (ILO). Data are in current U.S. dollars.; ; Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows to Developing Countries, Development Co-operation Report, and International Development Statistics database. Data are available online at: www.oecd.org/dac/stats/idsonline.; Sum; Data for net official flows from WHO at present are reported at the regional level only. A more detailed breakdown by recipient country will be available in the future.

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.

GDP from Construction in Ethiopia increased to 546.46 ETB Billion in 2023 from 501.49 ETB Billion in 2022. This dataset includes a chart with historical data for Ethiopia Gdp From Construction.

Exports in Ethiopia increased to 1761.20 USD Million in the fourth quarter of 2024 from 1515.90 USD Million in the third quarter of 2024. This dataset provides - Ethiopia Exports - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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).

Abstract

The Central Statistical Agency (CSA) has been providing labour force and related data at different levels and with varying details in their content. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, the 1984 and 1994 Population and Housing Census, and 2003 and 2004 Urban Bi-annual Employment Unemployment Survey. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys undertaken by the Agency also provide limited information on the area. Still pieces of information in relation to that of employment can also be derived from small, large and medium scale establishment surveys.

Till the 1999 Labour Force Survey (LFS) there hasn't been a comprehensive national labour force survey representing both urban and rural areas. This 2005 LFS is the second in the series. Like the National Labour Force Survey of 1999, it covered both the urban and rural areas of all regions.

The specific objectives of this survey are to: - generate data on the size of work force that is available to participate in production process; - determine the status and rate of economic participation of different sub-groups of the population; - identify those who are actually contributing to the economic development (i.e., employed) and those out of the sphere; - determine the size and rate of unemployed population; - provide data on the structure of the working population; - obtain information about earnings from paid employment; - identify the distribution of employed population working in the formal/informal enterprises; and - provide time series data and trace changes over time.

Geographic coverage

The survey covered all rural and urban parts of the country except all zones of Gambella region excluding Gambella town, and the non-sedentary population of three zones of Afar & six zones of Somali regions.

Analysis unit

Household Individual

Universe

The survey covered all households in selected sample areas except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME: The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) is used to select EAs from the rural part of the country. For urban sample EAs 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. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. The list was then used as a frame for selecting sample households of each 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 8 regions and two city administrations found in the country. Regarding the survey domains, each region or city administration was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category totally 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. Households per sample EA were selected as a second Stage Sampling Unit (SSU) and the survey questionnaire finally administered to all members of sample households.

Category II:- Major urban centers:- In this category all regional capitals and 15 other major urban centers that had a population size of 40,000 or more in 2004 were included. Each urban center in this category was considered as a reporting level. The category has totally 26 reporting levels. In this category too, in order to select the samples, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs. Households from each sample EA were then selected as a Second Stage Unit.

Category III: - Other urban centers: Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella a domain of other urban centers is formed for each region. Consequently seven 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. Households from each EA were finely selected at the third stage and the survey questionnaires administered for all of them.

SAMPLE SIZE AND SELECTION SCHEME: Category I: - Totally 830 EAs and 24,900 households were selected from this category. Sample EAs of each reporting level were selected using Probability Proportional to Size (PPS) systematic sampling technique; size being number of household obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and surveyed.

Category II: - In this category 720 EAs and 21,600 households were selected. Sample EAs from each reporting level in this category were also selected using probability proportional to size systematic sampling; size being number of households obtained from the 2004 EUEEC. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and covered by the study.

Category III:-127 urban centers, 275 EAs and 8,250 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size systematic selection method; size being number of households obtained from the 2004 EUEEC. From the fresh listing of each EA 30 households were systematically selected and the study carried out on the 30 households ultimately selected.

Note: Distribution of number of samples planned and covered from each domain are given in the Summary Table 2.1, Table 2.2 and Table 2.3 of the 2005 National Labour Force Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey has used a structured questionnaire to produce the required data. Before taking its final shape, the draft questionnaire was tested by undertaking a pre-test. The pre-test was conducted in Addis Ababa, Sendoffs, Teji and their vicinity. Based on the findings of the pre-test, the content, layout and presentation of the questionnaire was amended comments and inputs on the draft contents of the survey questionnaire obtained from user-producer forum were also incorporated in the final questionnaire.

The contents of the questionnaire and methods used in this survey were further improved based on comment of international consultant. The consultancy was obtained as part of a joint World Bank/IMF project to improve statistics of countries in Anglo-phone Africa participating in the General Data Dissemination System (GDDS).

The questionnaire was organized in to five sections; Section 1 - Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section 2 - Socio- demographic characteristics of households: it consisted of the general sociodemographic characteristics of the population such as age, sex, education, status and type of disability, status and types of training, marital status and fertility questions.

Section 3 - Productive activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, employment status, and earnings from employment. Also questions included are hours spent on fetching water, collection of firewood, and domestic chores and place of work.

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

Section 5 - Economic activities during the last twelve months: this section covered the usual economic activity status (refereeing to the long reference period), number of weeks of employment /unemployment/inactive, reasons for inactivity, employment status, whether working in the agricultural sector or not and the proportion of income gained from non-agricultural sector. The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre-coded answers. A copy of the questionnaire translated to English is provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the enumerator, the field supervisors, Statisticians and the heads of branch statistical offices have done some editing. However, the major editing operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry.

Response rate

Ultimately 100.00 % EAs and 99.84% household were covered

Abstract

Statistical information on all aspects of socio-economic activities is essential for the designing, monitoring evaluation of development plans and policies for gagging the growth of investment. Labour force surveys are one of the important sources of data for assessing the role of the population of the country in the economic and social development process. These surveys provide data on the main characteristics of the work force engaged or available to be engaged in productive activities during a given period and also its distribution in the various sectors of the economy. It is also useful to indicate the extent of available and unutilized human recourses that must be absorbed by the national economy to ensure full employment and economic well being of the population. Furthermore, the information obtained from such surveys is useful for the purpose of macro-economic monitoring and evaluation human resource development planning. The other broad objective of statistics on the labour force is for the measurement of relationship between employment, income and other social and economic characteristics of the economically active population for the purpose of formulating, monitoring and evaluation of employment policy and programs. Seasonal and other variations and changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force survey.

CSA has been providing labour force and related data at different levels and with varying details in their content. These include the 1976 Addis Ababa Man Power and Housing Sample Survey the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, and the 1984 & 1994 Population and Housing Census. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys also provide limited data on the area. Some information can also be derived from small, large and medium scale establishment surveys. Till the 1999 survey there hasn't been a comprehensive national labour force survey representing both urban and rural areas.

The latest data in the subject had been collected before four years and can be considered relatively outdated as the sector is dynamic and sensitive to economic and social changes. Moreover, it lacks data for trend and comparable analysis. Thus, to fill-in the data gap in this area, a series of current and continuous labour force survey need to be undertaken. Recognizing this fact and in response to request from different data users, the CSA has launched a biannual employment-unemployment survey program starting October, 2003 G.C

This survey is the first in the series and will serve as a baseline data for tracing changes. This program covers only urban areas of all regions. Rural areas will be included in the future as necessary. The survey is planned to be conducted twice every year, one in October and another in April. October and April in Ethiopia represent peak and slack agricultural periods.

Objectives of the survey: The bi-annual employment and unemployment survey program was designed to provide statistical data on the size and characteristics of the economically active and the non-active population of the country on continuous basis. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development projects and the performance of the economy.

The specific objectives of the survey are to: - Generate data on the size of work force that is available to participate in production process; - Determine the status and rate of economic participation of different sub-groups of the population; - Identify those who are actually contributing to the economic development (employed) and those out of the sphere; - Determine the size and rate of unemployed population; - Provide data on the structure of the working population; - Obtain information about earnings from paid employment; - Identify the distribution of employed population in the formal/informal sector of the economy; - Generate baseline data to trace changes over time in the future.

Geographic coverage

The 2003 Urban Bi Annual Employment and Unemployment survey covered only urban parts of the country. Except three zones of Afar and six zones of Somali regions, where the residents are pastoralists, all urban centers of the country were considered in this survey.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

All households in the selected samples, except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Sample Size: Information from the listing of the 1994 Population and Housing Census was utilized to develop the sampling frame for the 2003 Urban Bi Annual Employment and Unemployment Survey. It was by taking into account of cost and precision of major variables that determination of sample size was achieved. Moreover, in order to judge precisions of major variables, the 1999 Labor Force Survey result was the main source of information that was taken into consideration.

Except Harari, Addis Ababa and Dire Dawa, where all urban centers of the domain were incorporated in the survey, in other domains a three stage stratified cluster sample design was adopted to select the samples from each domain. The primary sampling units (PSU's) were urban centers selected systematically using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. From each selected urban centers enumeration areas (EA's) were selected as a second stage sampling unit (SSU). The selection of the SSU's was also done using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. For each sampled EA a fresh list of households was prepared at the beginning of the survey. Thirty households from each sample EA were selected at the third stage. The survey questionnaire was finally administered to those thirty households selected at the last stage. The selection scheme for Harari, Addis Ababa and Dire Dawa was similar to the case explained above. However, in these three domains instead of a three-stage design a two-stage stratified cluster sample design with enumeration areas as PSU and households (from the fresh list) as secondary sampling unit was used.

Note: Distribution of sampling units (planned and covered) by domain (reporting level) is given in Summary Table 2.1 of the 2003 Urban Bi-annual Employment Unemployment Survey Round 1 report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey has used a structured questionnaire to solicit the required data. Before taking its final shape, the draft questionnaire was tested by undertaking a pre-test. The pre-test was conducted in Addis Ababa, Debreziet and Sendafa. Based on the findings of the pre-test, the content, layout and presentation of the questionnaire was amended. Comments and inputs on the draft contents of the survey questionnaire obtained from user-producer forum were also incorporated in the final questionnaire.

The questionnaire is organized in to five sections; Section - 1: Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc., Section - 2: Demographic characteristics of household: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, states & types of training and marital status. Section - 3: Economic activity during the last six months: this section covered the usual economic activity status, number of weeks of Employment /Unemployment and reasons for not usually working. Section - 4: Productive activities during the last seven days: this section dealt with the status and characteristics of employed persons such as hours of work occupation, industry, employment status, and Earnings from employment. Section - 5: Unemployment and characteristics of unemployed persons: the section focused on the size and characteristics of the unemployed population.

Note: The questionnaires are provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors, Statisticians and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry. After the data was entered, it was again verified using the computer.

Data Entry, Cleaning and Tabulation: Using the computer edit specification prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on tabulation results. Computer programs was developed by data processing department for data entry, data cleaning and tabulation using Integrated Microcomputer Processing System (IMPS) software.

Response

Abstract

Labour force surveys are one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. These surveys provide data on the main characteristics of the work force engaged or available to be engaged in productive activities during a given period and its distribution in the various sectors of the economy. It is also useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. Moreover, it further provides an input for assessing the meeting of the Millennium Development Goals (MDGs) and the country's poverty reduction strategy framework (PASDEP-Plan for Accelerated and Sustained Development to End Poverty). The other broad objective of statistics on the labour force is for the measurement of relationship between employment, income and other social and economic characteristics of the economically active population for the purpose of formulating, monitoring and evaluation of employment policy and programs. Seasonal and other variations and changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys.

The Central Statistical Agency (CSA) has been providing labour force and related data at different levels and with varying content details. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, and the 1984 & 1994 Population and Housing Census. A comprehensive national labour force data representing both urban and rural areas was also provided based on the 1999 and 2005 Labour Force Surveys. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys also provide limited data on the area. Moreover, some information can be derived from small, large and medium scale establishment surveys.

Considering the dynamic and sensitive nature of the sector and also in response to the demands of different data users, the CSA had launched a Bi-annual Employment Unemployment Survey program starting from October 2003 GC. In this way, the Agency had conducted two rounds in October 2003 and April 2004 and the results were published in Statistical Bulletin 301 and 319. The 2005 Labour Force Survey (LFS) had been conducted to update the 1999 National Labour force survey. Here after, based on data need assessment it was decided to undertake the continuous survey annually instead of bi-annually.

Objectives of the survey The Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the non-active population of the country on continuous basis. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development projects and the performance of the economy.

The specific objectives of this survey were to: - Up date data on the size of work force that is available to participate in production process; - Determine the status and rate of economic participation of different sub-groups of the population; - Identify those who are actually contributing to the economic development (employed) and those out of the sphere; - Determine the size and rate of unemployed population; - Provide data on the structure of the working population; - Obtain information about earnings from paid employment; - Identify the distribution of employed population in the formal/informal sector of the economy; and - Generate time series data to trace changes over time.

Geographic coverage

The 2006 Urban Annual Employment and Unemployment Survey covered only urban parts of the country. Except three zones of Afar and six zones of Somali regions, where the residents are pastoralists, all urban centers of the country were considered in this survey.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

All households in the selected samples, except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Sample Size: Information from the listing of the 2004 Urban Economic Establishment Census was utilized to develop the sampling frame for the 2006 Urban Annual Employment and Unemployment Survey. It was by taking into account of cost and precision of major variables that determination of sample size was achieved. Moreover, in order to judge precisions of major variables, the 1999 Labor Force Survey result was the main source of information that was taken into consideration.

Except Harari, Addis Ababa and Dire Dawa, where all urban centers of the domain were incorporated in the survey, in other domains a three stage stratified cluster sample design was adopted to select the samples from each domain. The primary sampling units (PSU's) were urban centers selected systematically using probability proportional to size; size being number of households obtained from the 2004 Urban Economic Establishment Census. From each selected urban centers enumeration areas (EA's) were selected as a second-stage sampling unit (SSU). The selection of the SSU's was also done using probability proportional to size; size being number of households obtained from the 2004 Urban Economic Establishment Census. For each sampled EA a fresh list of households was prepared at the beginning of the survey. Thirty households from each sample EA were selected at the third stage. The survey questionnaire was finally administered to those thirty households selected at the last stage.

The selection scheme for Harari, Addis Ababa and Dire Dawa was similar to the case explained above. However, in these three domains instead of a three-stage design a two-stage stratified cluster sample design with enumeration areas as PSU and households (from the fresh list) as secondary sampling unit was used.

Note: Distribution of sampling units (planned and covered) by domain (reporting level) is given in Summary Table 2.1 of the 2006 Urban Employment Unemployment Survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Almost similar questionnaire that were used for the first and second rounds is administered in this survey.

The questionnaire was organized into five sections: Section - 1: Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section - 2: Demographic characteristics of household: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status & types of training and marital status.

Section - 3: Productive activities during the last seven days: this section dealt with the status and characteristics of employed persons such as hours of work, occupation, industry, employment status, and earnings from paid employment.

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

Section - 5: Economic activity during the last six months: this section covered the usual economic activity status, number of weeks of employment /unemployment and reasons for not usually working.

The questionnaire used in the field for data collection purpose was prepared in Amharic language. Both Amharic and English versions of the questionnaires are provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors, Statisticians and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry. After the data was entered, it was again verified using the computer.

Data Entry, Cleaning and Tabulation: Using the computer edit specification prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on tabulation results. Computer programs used in data entry, machine editing and tabulation were prepared using the Integrated Microcomputer Processing System (IMPS).

Response rate

As regards the response rate of the survey, a total of 99 urban centers were selected and incorporated into the survey. To be covered by the survey, 527 enumeration areas was initially selected, and the survey could successfully be carried out in 525 (99.62%) out of all the 527 of the EA’s. The total number of expected households that were to be interviewed was 15,810; however, due to different reasons 235 sample households were not interviewed. As a result only 15,575 households were actually covered by the survey, which made the ultimate response rate of the survey 98.51 %.

Sampling error estimates

Estimation procedures of total, ratio and

Abstract

The survey was conducted in Ethiopia between June 2015 and February 2016 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

In Ethiopia, data from 848 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using CAPI mode.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. The establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into four manufacturing industries (Food and Beverages (ISIC Rev. 3.1 code 15), Textile and Garments including leather (ISIC codes 17-19), Non-metallic mineral products (ISIC code 26), and other manufacturing (ISIC Codes 16, 20-25, 27-37)) and three services sectors (Transportation (ISIC codes 60-62, 64), Retail (ISIC code 52) and Other Services (ISIC codes 45, 50, 51,55 and 72)).

Size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees).

Regional stratification for the 2015 Ethiopia ES was done across six geographic regions: Addis Ababa and Dire Dawa city administrations, and Amhara, Oromia, SNNPR and Tigray regional states.

The sample frame consisted of listings of firms from two sources. First, for panel firms, the list of 644 firms covered in the 2011 Ethiopia Enterprise Survey (i.e. "panel" firms) is used. Secondly, for fresh firms (i.e., firms that were not covered in the 2011 survey), business registry data collected from the Trade and Industry Bureaus of the six administrative regions and cities, and additional list of business registry data from the Federal Ministry of Trade and Industry were used.

The enumerated establishments with 5 employees or more (fresh and panel) were then used as the sample frame for the Ethiopia Enterprise Survey with the aim of obtaining interviews of 900 establishments.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 33% (1056 out of 3447 establishments), reflecting the fact that the fresh sample frame is based on a business registry data4. In fact, most of the non-eligibility issue is the result of firms turning out to be micro (which is not part of our sampling universe) up on screening.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

Questionnaires have common questions (core module) and respectfully additional manufacturing and services specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.25. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.04.

Ethiopia's total Exports in 2023 were valued at US$2.86 Billion, according to the United Nations COMTRADE database on international trade. Ethiopia's main export partners were: Saudi Arabia, the United States and the Netherlands. The top three export commodities were: Coffee, tea, mate and spices; Edible vegetables and certain roots and tubers and Oil seed, oleagic fruits, grain, seed, fruits. Total Imports were valued at US$17.05 Billion. In 2023, Ethiopia had a trade deficit of US$14.19 Billion.

Abstract

Statistical information on all aspects of the population is vital for the design, implementation, monitoring and evaluation of economic and social development plan and policy issues. Labour force survey is among the important sources of data to assess the participation of the population in the economic and social development process of the country. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic wellbeing of the population.

The general objective of the 2013 National Labor Force Survey was designed to provide statistical data on the size, distribution and characteristics of the economically active and the distribution in the various sectors of the economy in both urban and rural areas. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development plans, programs and projects. The specific objectives of this survey are: • Generate data on the size of the potential work force that is available to participate in production process; • Determine the activity status and rate of economic participation of different sub-groups of the population; • Identify those who are actually contributing to the economic development (i.e., employed) and those who are out of the sphere of productive activities; • Identify the size, distribution and characteristics of employed population by occupation and Industry, status in employment, sector of employment and earnings from employment...etc. • Provide data on the size, distribution and characteristics of unemployed population and rate of unemployment; • Assess the situation of women's employment or the participation of women in the labour force; • Provide time series data to trace changes over time.

Geographic coverage

The survey covered all rural and urban parts of the country except the non-sedentary areas of six zones of Somali region.

Analysis unit

• Households
• Individuals (household members aged 5 years and above)

Kind of data

Sample survey data [ssd]

Frequency of data collection

Every five years

Sampling procedure

Sampling Frame The list of Sampling Frame obtained from the 2007 Population and Housing Census is used to select EAs. A fresh list of households from each EA was prepared at the beginning of the survey period. The list was then used as a frame for selecting sample households of each EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories, rural (Category I), major urban center (Category II) and other urban center categories (Category III).

Sample Size and Selection Scheme Category I: Totally 842 EAs and 25260 households were selected from this category. Sample EAs of each reporting level was selected using Probability Proportional to Size (PPS) systematic sampling technique; size being number of household obtained from the 2007 Population and Housing Census. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and surveyed. For the distribution of planned and covered number of samples from each domain see

Category II: In this category 817 EAs and 24510 households were selected. Sample EAs from each reporting level in this category were also selected using probability proportional to size (PPS) systematic sampling; size being number of households obtained from the 2007 Population and Housing Census is used to select EAs. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and covered by the study. The table below (Summary Table 2.2) shows planned and covered EAs and households in each domain.

Category III: 127 urban centers, 296 EAs and 8,880 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size systematic selection method; size being number of households obtained from the 2007 Population and Housing Census is used to select EAs. From the fresh listing of each EA 30 households were systematically selected and the study carried out on the 30 households ultimately selected. Summary Table 2.3 below shows the number of planned and sampled EAs and households by domain.

For details on sampling design, see: Ethiopian Central Statistical Agency. Analytica Report on The 2013 National Labour Force Survey

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey is mainly aimed at providing information on the economic characteristics of the population aged 10 years and above, i.e., their activity status, employment, and unemployment situation during the last seven days prior to the survey date. It has also covered detailed socio-demographic background variables such as age, sex, relationship to the head of household, migration, disability, literacy status, educational level, training and marital status. The survey has used a structured questionnaire to produce the required data. Before taking its final shape, the draft questionnaire was commented by CSA senior staff member from different directorate as well as top management. Based on the comment given by professionals, the content, layout and presentation of the questionnaire were amended.

The questionnaire was organized in to six sections; Section 1: Area identification of the selected household: this section dealt with area identification of the respondents such as region, zone, wereda, etc. Section 2: Socio- demographic characteristics of households: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status and type of migration, disability, literacy status, educational Attainment, types of training and marital status. Section 3: Economic activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, status in employment, earnings from employment, job mobility, service year for paid employees employment in the formal and informal sector and time related under employment. Section 4: Unemployment and characteristics of unemployed persons: this section focused on the size, rate and characteristics of the unemployed population. Section 5: Economic activities during the last twelve months: this section consists of the usual economic activity status refereeing to the long reference period i.e. engaged in productive activities during most of the last twelve months, reason for not being active, status in employment, main occupation and industry with two digit codes. Section 6: Economic activities of children aged 5-17 years: this section comprises information on the participation of children aged 5-17 years in the economic activities, whether attending education, reason for not attending education, whether they were working during the last seven days, reason for working, for whom they are working, types of injury at work place, whether using protective wear while working and frequency of working periods, and orphan hood status.

The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre-coded answers. A copy of the questionnaire translated to English is attached as an external resource.

Cleaning operations

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

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

Response rate

• For the rural domains, the response rate was 99.60%
• For the major urban centers domains, the response rate was 99.51%
• For the other urban centers domains, the response rate was 99.62%

As of 2023, Niger registered the agricultural sector's highest contribution to the GDP in Africa, at over ** percent. Comoros and Ethiopia followed, with agriculture, forestry, and fishing accounting for approximately ** percent and ** percent of the GDP, respectively. On the other hand, Botswana, Djibouti, Libya, Zambia, and South Africa were the African countries with the lowest percentage of the GDP generated by the agricultural sector. Agriculture remains a pillar of Africa’s economy Despite the significant variations across countries, agriculture is a key sector in Africa. In 2022, it represented around ** percent of Sub-Saharan Africa’s GDP, growing by over *** percentage points compared to 2011. The agricultural industry also strongly contributes to the continent’s job market. The number of people employed in the primary sector in Africa grew from around *** million in 2011 to *** million in 2021. In proportion, agriculture employed approximately ** percent of Africa’s working population in 2021. Agricultural activities attracted a large share of the labor force in Central, East, and West Africa, which registered percentages over the regional average. On the other hand, North Africa recorded the lowest share of employment in agriculture, as the regional economy relies significantly on the industrial and service sectors. Cereals are among the most produced crops Sudan and South Africa are the African countries with the largest agricultural areas. Respectively, they devote around *** million and **** million hectares of land to growing crops. Agricultural production varies significantly across African countries in terms of products and volume. Cereals such as rice, corn, and wheat are among the main crops on the continent, also representing a staple in most countries. The leading cereal producers are Ethiopia, Nigeria, Egypt, and South Africa. Together, they recorded a cereal output of almost *** million metric tons in 2021. Additionally, rice production was concentrated in Nigeria, Egypt, Madagascar, and Tanzania.