This statistic shows the biggest cities in Ethiopia in 2022. In 2022, approximately 3.86 million people lived in Adis Abeba, making it the biggest city in Ethiopia.

Geographic Coordinate System: GCS_WGS_1984 Datum: D_WGS_1984 Source: Ethiopian Road Authority (ERA)

Population in largest city in Ethiopia was reported at 5703628 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ethiopia - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Ethiopia ET: Population in Largest City data was reported at 4,215,965.000 Person in 2017. This records an increase from the previous number of 4,039,927.000 Person for 2016. Ethiopia ET: Population in Largest City data is updated yearly, averaging 1,690,413.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 4,215,965.000 Person in 2017 and a record low of 519,177.000 Person in 1960. Ethiopia ET: Population in Largest City 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. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Population in the largest city (% of urban population) in Ethiopia was reported at 18.25 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ethiopia - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Ethiopia ET: Population in Largest City: as % of Urban Population data was reported at 15.931 % in 2017. This records a decrease from the previous number of 16.255 % for 2016. Ethiopia ET: Population in Largest City: as % of Urban Population data is updated yearly, averaging 29.736 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 36.434 % in 1960 and a record low of 15.931 % in 2017. Ethiopia ET: Population in Largest City: as % of Urban Population 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: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted Average;

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the largest cities (>50k habitants) in the country. This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

Lagos, in Nigeria, ranked as the most populated city in Africa as of 2024, with an estimated population of roughly nine million inhabitants living in the city proper. Kinshasa, in Congo, and Cairo, in Egypt, followed with some 7.8 million and 7.7 million dwellers. Among the 15 largest cities in the continent, another two, Kano, and Ibadan, were located in Nigeria, the most populated country in Africa. Population density trends in Africa As of 2022, Africa exhibited a population density of 48.3 individuals per square kilometer. At the beginning of 2000, the population density across the continent has experienced a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 54 by the year 2027. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 640 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 44 percent in 2021. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating 35 percent of the total population. This trajectory is projected to continue its ascent in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2021, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. In 2023, Africa's population was estimated to expand by 2.35 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.45 percent, reaching its pinnacle at 2.59 percent between 2012 and 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

Points represent major cities in Ethiopia

This horizontal bar chart displays GDP (current US$) by capital city using the aggregation sum in Ethiopia. The data is filtered where the date is 2021. The data is about countries per year.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

Accessibility to regional cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities surrounding the country. The following cities are included: City - Population Addis Ababa, Ethiopia - 5 153 002 Asmara, Eritrea - 1 258 001 Sohag, Egypt - 979 800 Wau, South Sudan - 328 651 Abeche, Chad - 83 155 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

This horizontal bar chart displays carbon dioxide emissions (CO2) (Mt of CO2 equivalent) by capital city using the aggregation sum in Ethiopia. The data is filtered where the date is 2021. The data is about countries per year.

The metropolitan area of Lagos in Nigeria counted over 14 million middle-class people as of 2018. This was the highest number in Africa. Addis Ababa in Ethiopia followed with 2.7 million individuals belonging to the middle class. The middle-class population included people who had a disposable income of over 75 percent of their salary, were employed, had a business activity, or were in education, and had at least a secondary school degree.

Abstract

The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) is a nationwide survey with a nationally representative sample of 9,150 selected households. All women age 15-49 who were usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed in the survey. In the selected households, all children under age 5 were eligible for height and weight measurements. The survey was designed to produce reliable estimates of key indicators at the national level as well as for urban and rural areas and each of the 11 regions in Ethiopia.

The primary objective of the 2019 EMDHS is to provide up-to-date estimates of key demographic and health indicators. Specifically, the main objectives of the survey are: ▪ To collect high-quality data on contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; child nutrition; and other health issues relevant to achievement of the Sustainable Development Goals (SDGs) ▪ To collect information on health-related matters such as breastfeeding, maternal and child care (antenatal, delivery, and postnatal), children’s immunizations, and childhood diseases ▪ To assess the nutritional status of children under age 5 by measuring weight and height

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Health facility

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2019 EMDHS is a frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (EPHC) and conducted by the Central Statistical Agency (CSA). The census frame is a complete list of the 149,093 EAs created for the 2019 EPHC. An EA is a geographic area covering an average of 131 households. The sampling frame contains information about EA location, type of residence (urban or rural), and estimated number of residential households.

Administratively, Ethiopia is divided into nine geographical regions and two administrative cities. The sample for the 2019 EMDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the nine regions and the two administrative cities.

The 2019 EMDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

To ensure that survey precision was comparable across regions, sample allocation was done through an equal allocation wherein 25 EAs were selected from eight regions. However, 35 EAs were selected from each of the three larger regions: Amhara, Oromia, and the Southern Nations, Nationalities, and Peoples’ Region (SNNPR).

In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 EPHC frame) and with independent selection in each sampling stratum. A household listing operation was carried out in all selected EAs from January through April 2019. The resulting lists of households served as a sampling frame for the selection of households in the second stage. Some of the selected EAs for the 2019 EMDHS were large, with more than 300 households. To minimise the task of household listing, each large EA selected for the 2019 EMDHS was segmented. Only one segment was selected for the survey, with probability proportional to segment size. Household listing was conducted only in the selected segment; that is, a 2019 EMDHS cluster is either an EA or a segment of an EA.

In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. All women age 15-49 who were either permanent residents of the selected households or visitors who slept in the household the night before the survey were eligible to be interviewed. In all selected households, height and weight measurements were collected from children age 0-59 months, and women age 15-49 were interviewed using the Woman’s Questionnaire.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2019 EMDHS: (1) the Household Questionnaire, (2) the Woman’s Questionnaire, (3) the Anthropometry Questionnaire, (4) the Health Facility Questionnaire, and (5) the Fieldworker’s Questionnaire. These questionnaires, based on The DHS Program’s standard questionnaires, were adapted to reflect the population and health issues relevant to Ethiopia. They were shortened substantially to collect data on indicators of particular relevance to Ethiopia and donors to child health programmes.

Cleaning operations

All electronic data files were transferred via the secure internet file streaming system (IFSS) to the EPHI central office in Addis Ababa, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by EPHI staff members and an ICF consultant who took part in the main fieldwork training. They were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro System software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing, double data entry from both the anthropometry and health facility questionnaires, and data processing were initiated in April 2019 and completed in July 2019.

Response rate

A total of 9,150 households were selected for the sample, of which 8,794 were occupied. Of the occupied households, 8,663 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 9,012 eligible women were identified for individual interviews; interviews were completed with 8,885 women, yielding a response rate of 99%. Overall, there was little variation in response rates according to residence; however, rates were slightly higher in rural than in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 EMDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019 EMDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution

- Age distribution of eligible and interviewed women

Abstract

The DPHS in Addis Ababa and Dire Dawa was conducted in May and June 2017, with the objective to assess the role of poverty in disaster risk, focusing primarily on urban flooding but also other hazards.

This project was a collaborative effort between Global Facility for Disaster Reduction and Recovery (GFDRR), the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL). Data collection was carried out by UDA Consulting under the supervision of the World Bank.

Geographic coverage

Cities of Addis Ababa and Dire Dawa, Ethiopia.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Satellite images of Addis Adaba and Dire Dawa were used to divide both cities into 100m x 100m grids and among those, 173 and 81 grids in Addis Ababa and Dire Dawa respectively were randomly selected. In each selected grid, a 10 x 10 meters secondary dot grids were created. Then, in each secondary grid, 5 households were randomly assessed for inclusion. If the house corresponded to the characteristics of a residential and “low-income/slum” dwelling, it was included in the sample. While the sampling was carried out in a manner to assure representativeness at the city level, caution should be taken before generalizing results generating from this data for the entire city population. This is because the sample intended to sample slum dwellers and low-income households (based on factors that are detectable in high-resolution satellite imagery and visible from above, such as quality of roofing and dwelling size, size of plot, etc.).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire consists of 13 sections that were used to collect the survey data. See the attached questionnaire.

Cleaning operations

The following data editing was done for anonymization purposes: • Precise location data, such as GPS coordinates, and 10 x 10 meters grids were dropped • Personal information, such as names and phone numbers were dropped • The number of religions reported was reduced from 6 to 3 categories, the number of ethnicities from 14 to 4 categories, marital status from 6 to 4 categories • Household size exceeding seven household members was categorized as “above 7 members” • Household member information for 7th member and above was dropped to avoid reconstruction of the household size variable.

For more information on the anonymization process, see the Technical Document.

Socio-demographic characteristics of students of Jimma high school, Jimma town southwest Ethiopia, 2021 (N = 388).

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.

The 2005 National Labor Force Survey was designed to provide statistical data on the size and characteristics of the economically active and the non-active population of the country, both in urban and rural areas. The data will be useful for policy makers, planners, researchers, and other institutionsand individuals engaged in the design, implementation and monitoring of human resource development plans, programs and projects. 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

Like the National Labour Force Survey of 1999, it covered both the urban and rural areas of all regions. Exceptions are Gambella Region, where only the urban parts of the region are covered, Affar Region with only zone one and zone three were covered and Somali Region where only Shinile, Jijiga and Liben zones were covered.

Analysis unit

• HouseHold (HH)
• Household Member

Universe

The survey is mainly aimed at providing information on the economic characteristics of the population aged 10 years and over,

Kind of data

Données échantillonées [ssd]

Sampling procedure

2.1 COVERAGE The 2005 (1997 E.C) Labour Force Sample 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. In the rural parts of the country it was planned to cover 830 Enumeration Areas (EAs) and 24,900 households. All planned EAs were actually covered by the survey; however, due to various reasons it was not possible to conduct the survey in 39 sample households. Ultimately 100.00 % EAs and 99.84% household were covered by the survey. Regarding urban parts of the country it was initially planned to cover 995 EAs and 29,850 households. Eventually 100% of the EAs and 99.24% of the households were successfully covered by the survey.

2.2 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.

2.3 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 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. Households from each EA were finely selected at the third stage and the survey questionnaires administered for all of them.

To have more informations on th sampling view the report (Page 8)

Mode of data collection

Interview face à face [f2f]

Research instrument

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 gainedfrom non-agricultural sector.

The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre-coded answers.

Cleaning operations

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 urban questionnaires were subjected to complete manual editing, while most of rural questionnaires were partially edited. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry. This system of data processing was followed on the assumption that, there is less complication of activities in rural areas than urban centers.

After the data was entered, it was again verified 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).

Addis Ababa, in Ethiopia, ranked as the most expensive city to live in Africa as of 2024, considering consumer goods prices. The Ethiopian capital obtained an index score of ****, followed by Harare, in Zimbabwe, with ****. Morocco and South Africa were the countries with the most representatives among the ** cities with the highest cost of living in Africa.

Abstract

The Household Consumption and Expenditure (HCE) survey is administered by the Central Statistical Agency every five years, most recently in 2010/11. The core objective of the HCE survey is to provide data that enable to understand the income dimension 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. • Obtain weights and other useful information for the construction and /or rebasing of consumer price indices at various levels and geographical areas.

Geographic coverage

The 2010/11 HCE survey covered all rural and urban areas of the country except the non-sedentary populations in Afar (three zones) and Somali (six zones).

Analysis unit

• Household
• Individual
• Consumption expenditure items

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The 2007 Population and Housing Census served as the sampling frame from which the rural and urban EAs were selected. A fresh list of households for each selected EA was collected at the beginning of the survey period. Households were then selected for inclusion in the survey by choosing a random number as the starting point in the list and selecting every nth household (n being the necessary number to achieve the desired number of households in each EA).

Sample Design & Selection In order to produce a representative sample, the country was stratified into the following four categories: rural, major urban centers, medium towns, and small towns.

a. Category I - Rural This category consists of the rural areas of 68 zones and special weredas, which are considered zones, in 9 regions of the country. This category also includes the rural areas of the Dire Dawa City Administration. A stratified two-stage cluster sample design was used, with the primary sampling unit being the EAs. Sample EAs were selected using Probability Proportional to Size, with size being the number of households identified in the 2007 Population and Housing Census. Twelve households were randomly selected from each sample rural EA for survey administration. The total sample for this category is 864 EAs and 10,368 households.

b. Category II - Major Urban Centers This category includes all regional capitals as well as five additional major urban centers with large populations, for a total of 15 major urban centers. These 15 urban centers were broken down into the 14 regional capitals and the 10 sub-cities of Addis Ababa City Administration resulting in a total of 24 represented urban domains. A stratified two-stage sample design was also used for this category as in the rural sample with EAs as the primary sampling unit. For this category, however, 16 households were randomly selected in each EA. In total, 576 EAs and 9,216 households were selected for this category.

c. Categories III & IV - Other Urban Centers These two categories capture other urban areas not included in Category II. A domain of other urban centers was formed from 8 regions (all except Harari, Addis Ababa, and Dire Dawa where all urban centers are included in Category II). Unlike the other categories, a three-stage sample design was used. However, sampling was still conducted using probability proportionate to size. The urban centers were the primary sampling units and the EAs were secondary sampling units. Sixteen households were randomly selected from each of the selected EAs. A total sample of 112 urban centers, 528 EAs, and 8,448 households were selected for these two categories.

Mode of data collection

Face-to-face [f2f]

Research instrument

A hard copy (Paper print) booklet type questioner has been used for data collection. The design of the questionnaire has structured/organized into five main parts (forms).

The main components of the survey questionnaire are: Form 0: is used together basic household information that could help to assess the general livelihood nature of a household and its members, such as: source of household income, status and scope of agriculture engagement (diversity and specialization), safety net/asset accumulation participation, participation in micro and small scale business enterprise, accessibility and/or credit facility status from micro-finance institution, …etc.,

Form 1: has been used to collect data on demographic characteristics and economic activity of household members, such as: age, sex, marital status, education, income contribution status, economic activity and other related variables.

Form 2 (2A & 2B): is used to collect actual consumption (quantity consumed) and equivalent expenditure of food, beverages and tobacco items, that would have been actually consumed by the household (members of the household) within the reference period of the survey. Note that the first three consecutive day's consumption being collected in Form 2A and 2B is used to collect the second phase (consecutive 4 days) of the survey week.

Form 3 (3A, 3B & 3C): Household consumption and expenditure data on non-durable goods and frequent services has been collected using three segments of form 3. Of which 3A and 3B are designed to handle three and four day's data, respectively; while 3C has been used to capture a full month reference data.

Form 4 (4A-4E): Household expenditure data of durable goods and Less-Frequent services was administered in form 4. In order to facilitate a systematic way of data collection approach, these goods and services are grouped into classes and data were collected using five chapters of the main module in such a way that expenditure data on: • Clothing and footwear was collected in 4A; • Dwelling rent, water, fuel and energy, furniture's & furnishing, household equipment and operation were collected by use of form 4B; • Health, transport and communication goods and services has been collected in form 4C; • Education, recreation, entertainment, cultural and sport goods and services were collected by the use of 4D; and • Personal goods and services, financial services, and others including operational cost of production with respect to unincorporated household economic enterprises;

Dairy book: Consumption expenditure of food and beverages data are collected, at first on daily basis, by listing every consumed item by the household (every household member) in each day in a dairy book, to facilitate exhaustiveness of consumption. And, then a summary of attributes are transferred to the main questionnaire.

Measuring tools: Kitchen balance (digital type in urban and analog type in rural areas) and measuring type are used for consumption/quantity data collection.

Cleaning operations

Data Processing All data processing was undertaken at the head office. Completed questionnaires were returned to the CSA data processing department from the field periodically. Data processing activities included cleaning, coding, and verifying data as well as checking for consistency. These activities were carried out on a quarterly basis after entering three months of data. Further processing, including the estimation of sampling weights, was carried out at the close of data entry.

Data Entry and Coding Manual editing and coding of data began as early as August 2010, when the first round of completed questionnaires was received at the head office. A team of 21 editors, 5 verifiers, and 4 supervisors carried out these activities. Subject matter experts provided a 5-day intensive training for this team to equip them with the necessary skills. Additionally, a team of 12 encoders was trained to enter the data. A double-entry system was used, wherein two separate encoders manually entered each survey. Any discrepancies between the two entries were flagged automatically and the physical survey was reviewed to correct the errors. Data entry was completed in October 2011.

Data Validation and Cleaning Data validation and cleaning was carried out by subject matter experts and data programmers. Systematic validity checks were completed at the commodity, household and visit levels. Activities related to consistency, validity, and completeness included the following: a. Imputation of missing observations on consumption goods (in quantity or value) using the market price survey that was collected at the time of the HCE. b. Validity and consistency of quantity and value of consumption items was checked by comparing the figures across both household visits (using the household-provided prices and/or the market price survey). c. Estimation of the value of consumption of own production using the household-provided quantities and market survey prices. d. Comparison of household expenditure on durable goods using different recall periods (i.e., 3 and 12 months). After analyzing the annualized values using each reference period, it was decided to use whichever period resulted in the largest expenditure, which was often the