The first wave of ESS began as ERSS (Ethiopia Rural Socioeconomic Survey) in 2011/12 and it covered only rural and small-town areas. The second wave, which was carried out in 2012/2014, was expanded to include all urban areas. The urban supplement was done in such a way to ensure that the ESS data can provide nationally representative estimates. The third wave was carried out in 2015/2016. ESS1, ESS2, and ESS3 together represent a panel of households and individuals for rural and all urban areas, including large, medium and small towns. The ESS panel from ESS1, ESS2 and ESS3 is referred as Panel I and the subsequent rounds as Panel II.
The 2018/19 ESS (ESS4) is a new panel. It is not a follow-up of the previous ESS waves. It is a baseline survey for the next ESS waves. It covers all nine regional states and two administrative cities, Addis Ababa and Dire Dawa. ESS4 is conducted in 565 EAs and of which 316 are rural and 219 are urban. The difference between ESS4 and previous ESS waves is that its coverage; ESS4 is representative at regional level in addition to rural and urban level. In the new round of ESS, the previous survey instruments are revised in collaboration with key data users and development partners. The revisions concentrated on updating existed modules, aiming the survey data being consistence with the countries policy and proclamations(e.g. tax and transfer laws, land policy, and financial sector proclamations), consistent with monitoring of international development indicators (SDG indicators), new economic concepts (e.g. new definition on Labor statistics and SDG WASH targets), and consistent with official government existing surveys (e.g. Household Consumption Expenditure Survey-HCES). Moreover, the ESS4 in partnership with the LSMS plus (+) project added new modules on individual-level disaggregated which is aimed to improve the availability and quality of individual-disaggregated household data to track progress of SDG indicators on ownership, use right and decision of selected physical and financial assets.
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.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Version 02: Anonymous dataset for public distribution
The following datasets has been added to version 02 study documentation:
The scope of the study includes:
- Household: Household characteristics; household roster; 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.
- Community: Informant roster; basic information; access to basic services; economic activities; agriculture (only for rural EAs); infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.
- Post Harvest: Household roster; crop roster; crop harvest by field; unit and size codes; harvest labour; crop roster; crop disposition.
- Post planting: Parcel Roster; field roster; crop roster; seeds roster; miscellaneous questions for the holder; agriculture capital; irrigation; land use and agriculture income tax; crop cut by field.
- Livestock: ownership; change in stock; breeding; house, water and feed; animal health; milk production; egg production; animal power and dung; household roster.
Urban and Rural
The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.
Producers and sponsors
Central Statistics Agency of Ethiopia
Government of Ethiopia
The World Bank
Government of Ethiopia
Bill and Melinda Gates Foundation
Foreign Commonwealth and Development Office
The World Bank
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.
ESS4 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). A total of 6770 households from 535 EAs were interviewed for both the agriculture and household modules. The household module was not implemented in 30 EAs due to security reasons (See the Basic Information Document for additional information on survey implementation).
The ESS4 data needs to be weighted to represent the national-level population of rural and urban areas as well as the regional population. A sample weight with post-stratification adjustments was calculated for the households and this weight variable is included in all the datasets. It reflects the adjusted probability of selecting the household into the sample. The inverse of this weight can be considered an expansion factor that sums to the total population of households in the nation. When this weight is used in a household-level file, it sums to the population of households. When this weight is used in an individual-level file, it sums to the population of individuals. If the data user wishes to produce an estimate for the population of individuals in a household-level file, an approximate expansion factor is the sample weight times the household size of each household.
The ESS4 sample rural EAs are selected with equal probability from the AgSS sample EAs within each zone in the first stage. At the second stage the 10 sample agricultural households for the ESS4 are selected from the 20 AgSS sample households with equal probability. In addition to the10 sample agricultural households selected in each sample rural EA for the ESS4, 2 non-agricultural households will be selected from the all the non-agricultural households identified in the listing of each rural EA.
In the case of the urban sample for the ESS4, at the first stage a new sample of urban EAs is selected systematically with PPS within each region from the updated pre-census cartographic frame. At the second stage a sample of 15 households will be selected from the listing for each sample EA.
Dates of Data Collection
Data Collection Mode
Computer Assisted Personal Interview [capi]
Routine supervision by CSA’s field supervisors entailed the field-level coordination by all CSA branch offices. Branch level statisticians and supervisors who were assigned to this project conducted the routine supervision. The branch supervisors made extended visits to the EAs between September 2018 and August 2019. One field supervisor checked the work of enumerators in selected EAs. The last visit was combined with community interviews that were conducted by the supervisors themselves. Up to two branch statisticians were also in the field to check the work of the supervisors and enumerators.
Additional supervision was conducted by CSA head office experts and Bank staff and consultants. The teams’ first visit was held between September 2018 and March 2019 when interviews with the post-planting, livestock questionnaires, crop-cut and post-harvest were being conducted. The second visit was in June-August 2019 when the household and community questionnaires were being collected.
Data Collection Notes
Seven training sessions were held for the ESS4. These included: three training of trainers (TOT) (July 2018, December 2018 and April 2019) and four field staff (enumerator and supervisor) training sessions in August 2018, October 2018, January 2019 and May 2019. The TOT and the field staff training focused on the content of the questionnaires and Survey Solutions CAPI as well as practical applications in data collection and supervision. All of the trainees had survey and CAPI experience and most of them had participated in other surveys conducted by CSA.
The ESS4 was implemented in three visits following the AgSS field schedule. For rural households, the first visit took place between September and October 2018. In this visit, the post-planting agriculture and livestock questionnaires were administered. Crop cut was conducted from September to December 2018. The second visit took place between February and March 2019 when the post-harvest agriculture questionnaires were administered. The third visit took place between June and August 2019 to administer the household and community questionnaires.
For the Urban households, the household questionnaires were administered in one visit that took place between June and August 2019.
A detailed description of the Training, Data Collection, Tracking & Monitoring process is provided in Section 4 of the Basic Information Document.
Reference Photo Album
Reference photographs were used in the collection of food consumption and crop production quantities reported in non-standard units. The photographs depict food items or crops in non-standard units (and different sizes where applicable) and were meant to ensure uniformity in the non-standard unit amounts across respondents. The photos were collected in a systematic manner during the market survey where the item-unit weights were also collected. During the market survey, interviewers were instructed to follow strict protocols when taking the photographs such as including a reference object (typically a standard sized bottle of water) to provide the respondent with a frame of reference for the size of the unit. For units with multiple sizes, all of the relevant sizes were taken in the same photo for easier comparison by the respondent. The reference photos taken during the market survey were compiled into an album that was printed and provided to all interviewers. Item-specific photos were included for non-container units (e.g. piece, medeb, bunch) while only one photo of containers (e.g. tassa, kunna, jog) were included. The reference photo album that was used by interviewers is included with the additional documentation on the website. The procedures used for collection of the reference photos as well as the conversion factors followed the guidelines laid out in a forthcoming guidebook produced by the LSMS team, The Use of Non-Standard Units for the Collection of Food Quantity: A Guidebook for Improving the Measurement of Food Consumption and Agricultural Production in Living Standards Surveys.
Central Statistics Agency
Government of Ethiopia
The survey consisted of five questionnaires, similar with the questionnaires used during the previous rounds with revisions based on the results of the previous rounds as well as on identified areas of need for new data.
The household questionnaire was administered to all households in the sample; multiple modules in the household questionnaire were administered per eligible household members in the sample.
The community questionnaire was administered to a group of community members to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.
The three agriculture questionnaires consisting of a post-planting agriculture questionnaire, post-harvest agriculture questionnaire and livestock questionnaire were administered to all household members (agriculture holders) who are engaged in agriculture activities. A holder is a person who exercises management control over the operations of the agricultural holdings and makes the major decisions regarding the utilization of the available resources. S/he has technical and economic responsibility for the holding. S/he may operate the holding directly as an owner or as a manager. Hence it is possible to have more than one holder in single sampled households. As a result we have administered more than one agriculture questionnaire in a single sampled household if the household has more than one holder.
Household questionnaire: The household questionnaire provides information on education; health (including anthropometric measurement for children); labor and time use; financial inclusion; assets ownership and user right; food and non-food expenditure; household nonfarm activities and entrepreneurship; food security and shocks; safety nets; housing conditions; physical and financial assets; credit; tax and transfer; and other sources of household income. Household location is geo-referenced in order to be able to later link the ESS data to other available geographic data sets (See Appendix 1 for discussion of the geo-data provided with the ESS).
Community questionnaire: The community questionnaire solicits information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.
Agriculture questionnaire: The post-planting and post-harvest agriculture questionnaires focus on crop farming activities and solicit information on land ownership and use; land use and agriculture income tax; farm labor; inputs use; GPS land area measurement and coordinates of household fields; agriculture capital; irrigation; and crop harvest and utilization. The livestock questionnaire collects information on animal holdings and costs; and production, cost and sales of livestock by products.
Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.
LSMS Database Manager
Development Economics Data Group (The World Bank)
World Bank Microdata Library
Development Economics Data Group (The World Bank)
The electronic datasets are organized by questionnaire with the following labels on file names in parentheses: household (hh), community (com), post-planting agriculture (pp), post-harvest agriculture (ph), and livestock (ls). The data within each questionnaire do not contain any constructed variables. For example, the ESS data provide most all variables needed to construct an estimate of total household consumption, but the data set does not contain an estimated value of total consumption. The only compiled data that are included with the ESS files are the geo-spatial variables The ESS collects confidential information on respondents. The confidential variables pertain to (i) names of the respondents to the household and community questionnaires, (ii) village and constituency names, (iii) descriptions of household dwelling and agricultural field locations, (iv) phone numbers of household members and their reference contacts, (v) GPS-based dwelling and agricultural field locations, (vi) names of the children of the head/spouse living elsewhere, (vii) names of the deceased household members, (viii) names of individuals listed in the network roster, and (ix) names of field staff. To maintain confidentiality, this information is not included in the ESS public use data. To partially satisfy user interest in geo-referenced location, while preserving the confidentiality of sample household and communities, modified EA-level coordinates are provided as part of the household geovariable table. Modified coordinates are generated by applying a random offset within a specified range to the average EA value (following the MeasureDHS approach). For households that have moved between waves 1 and 3, and are more than 5 km from their baseline location, the offset is with respect to the new household location. More specifically, the coordinate modification strategy relies on random offset of EA center-point coordinates (or average of household GPS locations by EA in ESS) within a specified range determined by the urban and rural classification. For small towns and urban areas, an offset range of 0-2 km is used. In rural areas, where communities are more dispersed and risk of disclosure may be higher, a range of 0-5 km offset is used. Additionally, an offset range of 0-10 km is applied to 1% of EAs, effectively increasing the known range for all points to 10 km while introducing only a small amount of noise. Offset points are constrained at the zone level, so that they still fall within the correct zone for spatial joins, or point-in-polygon overlays. The result is a set of coordinates, representative at the EA level, that fall within known limits of accuracy. Users should take into account the offset range when considering different types of spatial analysis or queries with the data. Analysis of the spatial relationships between locations in close proximity would not be reliable. However, spatial queries using medium or low-resolution datasets should be minimally affected by the offsets before being granted access to the dataset, all users have to formally agree: 1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor. 2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files. 3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Public use files, accessible to all
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Central Statistical Agency of Ethiopia. Ethiopia Socioeconomic Survey (ESS4) 2018-2019. Public Use Dataset. Ref: ETH_2018_ESS_v01. Downloaded from[URL] on [Date]
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
DDI Document ID
Development Economics Data Group
The World Bank
Documentation of the DDI
Date of Metadata Production
DDI Document version
Version 02 (February 2021). The metadata has been updated with additional datasets.