Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable. Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

The household income index of the different cluster numbers in Ethiopia from the Demographic Health Survey , 2016 - from Low income per household to High household income per cluster - at a resolution of about 5000 metres

Ethiopia ET: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 12.400 % in 2015. This records an increase from the previous number of 9.400 % for 2010. Ethiopia ET: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 9.400 % from Dec 1995 (Median) to 2015, with 5 observations. The data reached an all-time high of 12.400 % in 2015 and a record low of 5.200 % in 2004. Ethiopia ET: Proportion of People Living Below 50 Percent Of Median Income: % 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: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Abstract

In recent years, the need for comprehensive economic statistics has been growing rapidly in most developing countries in view of the use of such statistics in formulating socio-economic development plans in general, and to assess the socio-economic situation at the micro level, in particular. Thus, reliable and timely economic statistics data at the household level such as the ones obtained from Household Income, Consumption and Expenditure Surveys, on a regular basis are the major sources of socio-economic information. These surveys provide valuable data, especially for assessment of the impact of policies on the conditions and levels of living of households. In this survey, data were collected on basic population characteristics; consumption of food, drinks and tobacco; expenditure of the household on various consumption and non-consumption items; and household income and receipts. The data collection exercise took into account the two major seasons of the country, i.e., the slack/wet season and the peak/dry (harvest) season. It is a well known fact that surveys of Household Income, Consumption and Expenditure usually have the major goal of providing basic data needed for policy making purposes as well as other related issues that might arise at the micro level.

The major objectives of the survey are to: - Provide data on the levels, distribution and pattern of household income, consumption and expenditure that will be used for analysis of changes in the levels of living standards of households over time in various socio-economic groups and geographical areas. - Obtained information for the formulation of socio-economic plans and policies. - Furnish bench mark data for assessing the impact of existing or proposed socio-economic programs on household living conditions. - Provide data for compiling household accounts in the system of national accounts, especially in the estimation of private consumption expenditure. - Obtain weights and other useful information for the construction of consumer price indices at various levels.

Geographic coverage

The 1995-1996 Household Income, Consumption and Expenditure Survey covered all parts of the country on sample basis except the non sedentary population in Afar and Somali regions.

Analysis unit

• Household
• Individual/person
• Consumption expenditure item/ product/ service

Universe

The survey covered all households in the selected sample areas excluding residents of collective quarters, homeless persons and foreigner.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN: The 1995-1996 Household Income, Consumption and Expenditure Survey covered both urban and rural parts of the country, except six zones in Somalie region and two zones in Afar region. For the purpose of the survey, the country was divided into four categories. Urban areas were divided into twp broad categories taking into account sizes of their population. Rural areas were also grouped into two categories.

Category I: Rural parts of eight regions were grouped in this category each of which was the survey domain (reporting level). These regions are Tigray, Afar, Somali, Benishangul-Gumz, Gambela, Harari, Addis Ababa and Dire Dawa.

Category II: In this category thirteen survey domains were defined by grouping contiguous rural parts of the zones or special weredas in Amhara, Oromiya, and SNNP Regions respectively. These were: a) Amhara I) North Gonder, South Gonder II) East Gojam, West Gojam and Agew Awi III) North Welo and Wag Himra, and IV) South Welo, Oromiya and North Shoa

b) Oromiya I) East Wellega, and Wellega II) Ilubabor and Jimma III) North Shoa, West Shoa IV) East Shoa, Arsi, Bale and Borena, and V) East and West Hararge

c) SNNP I) Keficho-Shekicho, Bench-Maji and Yem, II) North Omo, South Omo, Derashe and Konso, III) Gurage, Hadiya and Kembata-Alaba-Timbaro, and IV) Sidama, Gedio, Amaro and Burji. Other than the 13 domains (reporting levels) defined in Category II, three additional domains could be constructed by combining basic domains from the two rural categories. These domains are: a) Rural Amhara b) Rural Oromiya and c) Rural SNNP

Category III: Ten large urban centers of the country were grouped in this category. Each of the ten urban centers in this category was the survey domain (reporting level), for which separate survey results for major survey characteristics were reported.

Category IV: Urban centers in the country other than the ten urban centers in category III were grouped in this category and formed a single reporting level.

Other than the eleven domains (reporting levels) defined in Category III and Category IV, one additional domain, namely total urban (country level) can be constructed by combining the basic domains defined in the two categories.

All in all twenty four basic rural domains (reporting levels) including total rural (country level) were defined for the survey.

In addition to the above urban rural domains, survey results are to be reported at regional and country levels by aggregating the survey results for the corresponding urban and rural area.

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.

The sample selection scheme and sample size issues are discussed as follows: a) Category I and Category II: A 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 form 1994 population and housing census. A total of 620 EAs were selected from the rural part. Within each sample EA a fresh list of household was prepared at the beginning of the survey's filed work and for the administration of the survey questionnaire 12 households per sample EA were systematically selected.

b) Category III: Stratified two-stage sample design was used to select the sample in which the PSUs were EAs. Sample EAs from each domain were selected using systematic probability proportional to size; size being number of household obtained form the 1994 population and housing census. In this category, a total of 220 EAs were selected. Within each sample EA, fresh list of households was prepared at the beginning of the survey's field work and for the administration of the survey questionnaire 15 households per sample EA were systematically selected.

c) Category IV: Three-stage stratified sample design was adopted to select the sample from domains in category IV. The PSUs were urban centers selected using systematic probability proportional to size; size being number of households obtained form the 1994 population and housing census. The secondary sampling units (SSUs) were EAs which were selected using systematic probability proportion to size; size being number of households obtained form the 1994 population and housing census. Number of sample SSUs selected from each of the the sample urban centers was determined by proportional allocation to their household population from the census. Ultimately, 15 households within each of the sample EAs were selected systematically from a fresh list of households prepared at the beginning of the survey's field work the administration of the survey questionnaire.

Note: Distribution of sample units by domain (reporting levels) is given in Summary Tables A and B (first round) and Summary Tables C and D (second round) of 1995 Household Income, Consumption and Expenditure Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey used structured questionnaire that consisted of the following forms: - Form 1: Household characteristics (list of members, sex, age, marital status, etc) - Form 2A: Quantity and value of weekly consumption of food, drinks and tobacco for the first and second week - Form 2B: Quantity and value of weekly consumption of food, drinks and tobacco for the third and fourth week - Form 3: Consumption expenditure of the household on clothing, headwear, footwear and the like - Form 4A: Consumption expenditure on housing: House rent and repairs, energy, water for first and second week - Form 4B: Consumption expenditure on housing: House rent and repairs, energy, water for third and fourth week - Form 5: Consumption expenditure on household operation and domestic service/ domestic utensils, cleaning items, domestic services, etc - Form 6A: Household consumption expenditure on services: Health, education, transport and communications, entertainment, etc for the first and second week - Form 6B: Household consumption expenditure on services: Health, education, transport and communications, entertainment, etc for the third and fourth week - Form 7A: Household consumption expenditure on personal care and effects and other expenditure for first and second week - Form 7B: Household consumption expenditure on personal care and effects and other expenditure for third and fourth week - Form 8: Non-consumption expenditure of households: 'Ekub', 'Edir' payments, remittance given out, purchases of lottery tickets, gambling expenses, etc - Form 9A: Income received by the household in cash and/or in kind for first and second week - Form 9B: Income received by the household in cash and/or in Kind for third and fourth week

Note: The survey questionnaire is provided as external

Ethiopia ET: Income Share Held by Highest 10% data was reported at 31.400 % in 2015. This records an increase from the previous number of 27.400 % for 2010. Ethiopia ET: Income Share Held by Highest 10% data is updated yearly, averaging 27.400 % from Dec 1995 (Median) to 2015, with 5 observations. The data reached an all-time high of 38.000 % in 1995 and a record low of 25.500 % in 1999. Ethiopia ET: Income Share Held by Highest 10% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Ethiopia ET: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data was reported at 1.820 Intl $/Day in 2015. This records an increase from the previous number of 1.750 Intl $/Day for 2010. Ethiopia ET: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day data is updated yearly, averaging 1.785 Intl $/Day from Dec 2010 (Median) to 2015, with 2 observations. The data reached an all-time high of 1.820 Intl $/Day in 2015 and a record low of 1.750 Intl $/Day in 2010. Ethiopia ET: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: 2017 PPP per day 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: Social: Poverty and Inequality. Mean consumption or income per capita (2017 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.

Abstract

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

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

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

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

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

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

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

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

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

Cleaning operations

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

Response rate

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

Ethiopia ET: Income Share Held by Highest 20% data was reported at 46.700 % in 2015. This records an increase from the previous number of 41.700 % for 2010. Ethiopia ET: Income Share Held by Highest 20% data is updated yearly, averaging 41.700 % from Dec 1995 (Median) to 2015, with 5 observations. The data reached an all-time high of 51.600 % in 1995 and a record low of 39.300 % in 2004. Ethiopia ET: Income Share Held by Highest 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

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

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

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

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

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

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

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

Cleaning operations

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

Response rate

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

More detailed information is available in the BID.

Ethiopia ET: Income Share Held by Lowest 20% data was reported at 6.600 % in 2015. This records a decrease from the previous number of 8.000 % for 2010. Ethiopia ET: Income Share Held by Lowest 20% data is updated yearly, averaging 8.000 % from Dec 1995 (Median) to 2015, with 5 observations. The data reached an all-time high of 9.400 % in 2004 and a record low of 6.300 % in 1995. Ethiopia ET: Income Share Held by Lowest 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The Ethiopian Rural Socioeconomic Survey (ERSS) is a collaborative project between the Central Statistics Agency (CSA) of Ethiopia 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 panel household level data with a special focus on improving agriculture statistics and 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.

The specific objectives of the ERSS are: - Development of an innovative model for collecting agricultural data in conjunction with household data; - Strengthening the capacity to generate a sustainable system for producing accurate and timely information on agricultural households in Ethiopia; - Development of a model of inter-institutional collaboration between the CSA and relevant federal and local government agencies as well as national and international research and development partners; and - Comprehensive analysis of household income, well-being, and socio-economic characteristics of households in rural areas and small towns.

Geographic coverage

Regional Coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The ERSS sample is designed to be representative of rural and small town areas of Ethiopia. The ERSS rural sample is a sub-sample of the AgSS while the small town sample comes from the universe of small town EAs. The ERSS sample size provides estimates at the national level for rural and small town households. At the regional level, it provides estimates for four regions including Amhara, Oromiya, SNNP, and Tigray.

The sample is a two-stage probability sample. The first stage of sampling entailed selecting primary sampling units , which are a sample of the CSA enumeration areas (EAs). For the rural sample, 290 EAs were selected from the AgSS EAs. The AgSS EAs were selected based on probability proportional to size of the total EAs in each region. For small town EAs, a total of 43 EAs were selected. In order to ensure sufficient sample in the most populous regions (Amhara, Oromiya, SNNP, and Tigray), quotas were set for the number of EAs in each region. The sample is not representative for each of the small regions including Afar, Benshangul Gumuz, Dire Dawa, Gambella, Harari, and Somalie regions. However, estimates can be produced for a combination of all smaller regions as one "other region" category.

The second stage of sampling was the selection of households to be interviewed in each EA. For rural EAs, a total of 12 households are sampled in each EA. Of these, 10 households were randomly selected from the sample of 30 AgSS households. The AgSS households are households which are involved in farming or livestock activities. Another 2 households were randomly selected from all other households in the rural EA (those not involved in agriculture or livestock). In some EAs, there is only one or no such households, in which case, less than two non-agricultural households were surveyed and more agricultural households were interviewed instead so that the total number of households per EA remains the same.

In the small town EAs, 12 households are selected randomly from the listing of each EA, with no stratification as to whether the household is engaged in agriculture/livestock. Households were not selected using replacement. Thus, the final number of household interviewed was slightly less than the 3,996 as planned in the design.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Most of the interviews were carried out using paper and pen interviewing method. The completed paper questionnaires were sent to the CSA headquarters in Addis Ababa. The questionnaires were first checked by editors for completeness and consistency. The editors checked completeness (taking inventory) and cross-checked the questionnaires with the EA codebook. Questionnaires with inconsistent responses or with errors were corrected by contacting the branch offices or, in some cases, by sending the questionnaires back to the field. Checked questionnaires were keyed by data entry clerks at the head office using CSPro data entry application software.

Computer assisted personal interviewing (CAPI) was implemented, as a pilot, in 33 of the 333 EAs using SurveyBe data collection software.

The data cleaning process was done in two stages. The first step was at the CSA head office using the CSA's data cleaning staff. The CSA data cleaning staff used the CSpro data cleaning application to capture out of range values, outliers, and skip inconsistencies from the batch error reports. Once the errors were flagged in the batch error report the hard copy of the original questionnaire was retrieved and checked if the errors were at the data collection, editing, or entry level. Editing and entry level errors were corrected at the head office. Field level errors were communicated with the branch offices in the regions. The second level of data cleaning was done using Stata program to check for inconsistencies.

Response rate

A total of 3,969 households were interviewed with a response rate of 99.3 percent.

Ethiopia ET: Income Share Held by Second 20% data was reported at 11.000 % in 2015. This records a decrease from the previous number of 12.600 % for 2010. Ethiopia ET: Income Share Held by Second 20% data is updated yearly, averaging 12.600 % from Dec 1995 (Median) to 2015, with 5 observations. The data reached an all-time high of 13.200 % in 1999 and a record low of 9.900 % in 1995. Ethiopia ET: Income Share Held by Second 20% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

How do income shocks affect intra-household expenditure patterns in agricultural economies? Using rainfall data and household panel data, with responses from both spouses, from rural Ethiopia, we show that a negative household level income shock significantly reduces female expenditures relative to male expenditures (31.4% greater reduction). We specifically explore the channel of female and male labour supply as an explanation behind the observed differentiated impacts on spousal consumption. We find evidence that engaging in off-farm employment provides women with an independent income and allows them to smooth their expenditures during farm income shock. We also find evidence that the wife's involvement in managing and controlling the household farm, measured as her time spent on the farm relative to the husband, negates the shock-induced gender differential in expenditures. Together, these results highlight gender-specific impacts of household income shocks on consumption and the role female economic opportunities play in negating intra-household impacts of such household shocks.

Abstract copyright data collection owner. The individual household method (IHM) was developed by Evidence for Development as a reliable, standardised method of collecting and using household income data that is suitable for operational use. IHM work involves both in-person data collection and the use of specialised analytical software, open-IHM, which can be used to manage complex household data and produce reports, models and predictions to inform policy-making.

This data set includes anonymised data from project areas Masumbankunda, Malawi; Karonga, Malawi; Tigray, Ethiopia; Assela, Ethiopia.

Please also see related file on Qualitative Impact Assessment (QUIP) data which includes some qualitative data collected from a sub-sample of the same households included in this study (only Round 2 files - Round 1 households were not from the same sample set).

Ethiopia ET: Female Headed Households data was reported at 25.400 % in 2016. This records a decrease from the previous number of 26.100 % for 2011. Ethiopia ET: Female Headed Households data is updated yearly, averaging 24.500 % from Dec 2000 (Median) to 2016, with 4 observations. The data reached an all-time high of 26.100 % in 2011 and a record low of 22.800 % in 2005. Ethiopia ET: Female Headed Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Population and Urbanization Statistics. Female headed households shows the percentage of households with a female head.; ; Demographic and Health Surveys.; ; The composition of a household plays a role in the determining other characteristics of a household, such as how many children are sent to school and the distribution of family income.

Abstract

At the time of Appraisal of the Food Security Project, Ethiopia was a post-conflict state having just emerged from a two-year long armed conflict with Eritrea. Though the conflict resulted in a suspension of development assistance, an Interim Strategy Note (ISN) was put in place in November 2000 to guide the World Bank’s post-conflict recovery program. This ISN guided much of the strategy for the Food Security Project’s design. District governments, or woredas, were largely responsible for delivering services. Though the agricultural sector remained underemployed, it was still the largest sector of the economy, meaning there was little opportunity outside rural areas for non-farming activities. Poor rural households also lacked sufficient access to the microfinance sector. Droughts and food price escalation caused massive food insecurity for around 7-13 million people. The Food Security Project (FSP) wanted to shift assistance focus away from short term temporary fixes toward addressing long-term problems of food insecurity. The FSP was designed to comprise 5 components: (i) grants to communities and kebeles, including community-level assets building, household asset building and income generating activities, and child growth promotion; (ii) capacity building for woredas, regions, and federal ministries; (iii) food marketing initiatives, including improved management of food aid, establishment of a food market information system, development of a warehouse receipt and inventory credit system for traders, and development of a competitive and efficient market in warehousing services; (iv) communications and public education; and (v) project administration and impact evaluation. While these components were edited before the culmination of the project, they generally remained. The project development objective was to build the resource base of poorer rural households, increase their employment and incomes, and improve their nutrition levels, especially for children under five years of age, pregnant and lactating women. A major benefit of FSP participation is access to credit. Documented outcomes included: (i) small increase in the number of months FSP households were food secure and a small decrease in number of months of food consumption covered by own resources; (ii) positive effect on caregivers’ knowledge of and behavior regarding child nutrition; (iii) FSP households slightly less likely to have had at least one shock in the last five years and less likely to have used savings or a loan to buy food; and (iv) FSP households reported an increase of off-farm work.

Geographic coverage

Rural Areas

Analysis unit

Households Individuals

Universe

The Food Security Project's (FSP) primary target groups were poor rural households, children under age 5, and pregnant and lactating women.

Kind of data

Sample survey data [ssd]

Sampling procedure

The dataset is a product of survey data. The questionnaire was administered by the CSA to 6,000 households in 240 kebeles of which 120 FSP kebeles were selected at random and then the nearest neighboring kebele which was not participating in FSP was also selected.

Within the non-FSP kebeles, 25 households were selected at random to participate in the survey. In FSP kebeles, a list was compiled of all FSP beneficiaries using FSP program records. From this list 17 households were selected at random to participate in the survey. In addition, among the population of non-beneficiaries, 8 households were selected at random for interviews.

This sampling structure provides two potential comparison groups to compare to FSP participants: non-beneficiaries within FSP kebeles and those residing in non-FSP kebeles.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Module 1 - Basic Household Characteristics S1A: Household demographics, current household members S1B: Characteristics of the household and the household head S1C: Former household members S2: Children's education and activities

Module 2 - Land, Crop and Forestry Production, and Disposition S1: Land characteristics and tenure S2A: 2002 Meher crop production S2B: 2002 Belg crop production S2C: 2002 Perennial crops/forestry production S3: Household level supply and disposition of crops (annual and perennial), spices, and forestry prducts S4: Use of labor in agricultural production

Module 3 - Household Assets S1: Household assets (non-land): production equipment, consumer durables S2: Housing S3: Livestock ownership S4: Income from livestock S5: Distress asset sales

Module 4 - Income Apart from Own-Agricultural Activities and Credit S1: Wage employment S2: Own business activities S3: Transfers

Module 5 - Access to WB/CIDA/Italy Food Security Project and Related Programs S1: Access to productive safety nets program - public works S2: Participation in other food security programs (OFSP) S3: Perceptions of benefits of assets created by PSNP and other public works S4: Perceptions and participation of operations of the WB/CIDA/Italy FSP S5: Access to credit

Module 6 - Consumption S1: Non-food expenditure on durables and services S2: Non-food expenditure on household consumables S3: Food consumption S4: Food availability, access and coping strategies

Module 7 - Health, Illness, Shocks and Poverty Perceptions S1: Health status S2: Illness S3: Child Growth Promotion S4: Long term shocks and coping mechanisms S5: Shocks to crops and livestock S6: Perceptions of poverty and well-being

The Ethiopia Rural Household Survey (ERHS) is a unique longitudinal household data set covering households in a number of villages in rural Ethiopia. Data collection started in 1989, when a team visited 6 farming villages in Central and Southern Ethiopia. In 1989, IFPRI conducted a survey in seven Peasant Associations located in the regions Amhara, Oromiya and the Southern Ethiopian People’s Association (SNNPR). Civil conflict prevented survey work from being undertaken in Tigray. Under extremely difficult field conditions, household data were collected in order to study the response of households to food crises. The study collected consumption, asset and income data on about 450 households. In 1994, the survey was expanded to cover 15 villages across the country. An additional round was conducted in late 1994, with further rounds in 1995, 1997, 1999, 2004, and 2009. In addition, nine new villages were selected giving a sample of 1477 households. The nine additional communities were selected to account for the diversity in the farming systems in the country, including the grain-plough areas of the Northern and Central highlands, the enset-growing areas and the sorghum-hoe areas. Topics addressed in the survey include household characteristics, agriculture and livestock information, food consumption, health, women’s activities, as well as community level data on electricity and water, sewage and toilet facilities, health services, education, NGO activity, migration, wages, and production and marketing.

Abstract

The HICE survey basically reflect the income dimension of poverty while WM survey aims at providing socioeconomic data that reflect the non-income dimension of poverty. The HICE survey provides statistics on income, consumption and expenditure of households and WM survey provides basic indicators on the various socioeconomic areas including health, education, nutrition, access to and utilization and satisfaction of basic facilities/services and related non-income aspects of poverty. The HICE survey has been conducted together with the WM survey every four-five years since 1995/96. The latest of these HICE surveys is for 2004/5 and covered a representative sample of 21,600 households. Previous HICE were similarly representative, covered 11,928 and 17,332 households for 1995/96 and 1999/00, respectively.

Unlike the previous two HICE surveys that had been conducted in 1995/96 and 1999/00, in the 2004/05 HICE survey data on Household Consumption Expenditure and Household Income were collected independently using separate modules. However, this statistical report concentrates only on the household consumption expenditure part.

The core objective of the HICE survey is to provide data that enable to understand the income aspects of poverty and the major objectives are to: - assess the level, extent and distribution of income dimension of poverty; - provide data on the levels, distribution and pattern of household expenditure that will be used for analysis of changes in the households' living standard level over time in various socio-economic groups and geographical areas; - provide basic data that enables to design, monitor and evaluate the impact of socio- economic policies and programs on households/individuals living standard; - furnish series of data for assessing poverty situations, in general, and food security, in particular; - provide data for compiling household accounts in the system of national accounts, especially in the estimation of private consumption expenditure; and - obtain weights and other useful information for the construction of consumer price indices at various levels and geographical areas.

Geographic coverage

The 2004/05 HICE Sample Survey covered all rural and urban parts of the country except all zones of Gambella Region, and the non-sedentary population of three zones of Afar and six zones of Somali regions.

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/ services

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata.

Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living.

The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were finally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of “other urban centers” is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category.

Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Sample Size and Selection Scheme Category I: - Totally 797 EAs and 9,564 households were selected from this category. Sample EAs of each reporting level were selected using Probability Proportional to Size (PPS) with systematic sampling technique; size being number of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration. From the fresh list of households prepared at the beginning of the survey 12 households per EA were systematically selected and surveyed.

Category II: - In this category 485 EAs and 7,760 households were selected. Sample EAs from each reporting level in this category were also selected using probability proportional to size with systematic sampling method; size being number of households obtained from the 2004 EUEEC. From the fresh list of households prepared at the beginning of the survey 16 households per EA were systematically selected and covered by the survey.

Category III:-127 urban centers, 275 EAs and 4,400 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size with systematic sampling method; size being number of households obtained from the 2004 EUEEC. From the listing of each EA 16 households were systematically selected and the survey was carried out on the 16 ultimately selected households.

Including region rural, region urban and country domains, totally 61 reporting levels (including the 10 sub-cities of Addis Ababa) were formed. For the overall distribution of planned and covered EAs and households see Annex I of the 2004-2005 Household Income, Consumption and Expenditure Survey (HICE).

Mode of data collection

Face-to-face [f2f]

Research instrument

The Household Income, Consumption and Expenditure Survey questionnaire contains the following forms: - Form 1: Area Identification and Household Characteristics - Form 2A: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for first and second week.
- Form 2B: Quantity and value of weekly consumption of food and drinks consumed at home and tobacco/including quantity purchased, own produced, obtained, etc for third and fourth week .
- Form 3A: All transaction (income, expenditure and consumption) for the first and second weeks except what is collected in Forms 2A and 2B - Form 3B: All transaction (income, expenditure and consumption) for the third and fourth weeks except what is collected in Forms 2A and 2B - Form 4: All transaction (expenditure and consumption) for last 6 months for Household expenditure on some selected item groups
- Form 5: Cash income and receipts received by household and type of tenure.

Cleaning operations

Data Editing, Coding and Capturing:
The first step of data processing activities was the training of 40 data editors/ coders and 20 supervisors by subject matter department staff members for the first round survey data. The data capturing (data entry) operation was carriedout using about 60 computers and as many data encoders. Similarly, the data processing activities of the second round survey were undertaken by about 60 editors/coders and 25 verifiers for about 85 days. Data entry operation took about 60 days using 125 computers and as many data encoders.

Data validation and cleaning activity was carried out by subject matter specialists and data processing programmers. The data cleaning and validity

The Food Security Simulator Ethiopia (FSS-ETH) is an innovative and easy-to-use, MS-Excel-based tool for assessing the potential short-term impacts of food price or household income shocks, along with changes in preferences, on food security and people’s diets. The Simulator is an ideal tool for first-cut forward-looking evaluations of direct, household-level outcomes of economic crises and policy responses in a timely manner. The tool allows users to enter positive and negative price or income changes in percentage terms and provides simulated changes for a diverse set of food-consumption- and diet-quality-related indicators. In addition to detailed tabular presentations of all simulation results by household income quintile and residential area, key indicator results are summarized in concise overview tables and visualized in graphs for easy export and use in reports. The underlying data include estimates from representative household survey data and rigorous, sophisticated food demand models to capture consumer behavior.

This data was collected using the Rural Household Multi-Indicator Survey (RHoMIS) tool to a conduct baseline survey among 200 households in Ethiopia. The standard RHoMIS tool was extended with a feeding and forage module. The dataset can be used to understand agricultural productivity, food security, income, gender dynamics and livestock feeding and forage practice.