Chart and table of Somalia population from 1950 to 2025. United Nations projections are also included through the year 2100.

Author: K Mayberry, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8, high schoolResource type: lessonSubject topic(s): migration, maps, historyRegion: united statesStandards: Minnesota Social Studies Standards

Standard 2. Geographic inquiry is a process in which people ask geographic questions and gather, organize and analyze information to solve problems and plan for the future.

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).

Standard 7. The characteristics, distribution and complexity of the earth’s cultures influence human systems (social, economic and political systems).

Standard 14. Globalization, the spread of capitalism and the end of the Cold War have shaped a contemporary world still characterized by rapid technological change, dramatic increases in global population and economic growth coupled with persistent economic and social disparities and cultural conflict. (The New Global Era: 1989 to Present)

Standard 8. Processes of cooperation and conflict among people influence the division and control of the earth’s surface. Objectives: Students will be able to:

1. Read and analyze maps.
2. Use evidence, including maps and readings, to explain the background to and causes of the Somalia civil war.
3. Use evidence, including oral interviews and readings, to explain why many Somalis migrated to Minnesota in a reflection essay.Summary: Minnesota currently has the highest percentage of Somali people in the U.S., and the vast majority of the students that I teach are of Somali descent. It is important for Somali-Americans to know their own history. With this in mind, students will complete a guided inquiry lesson using maps, primary sources, and secondary sources to answer the question: Why did so many people migrate from Somalia to Minnesota? This question is multifaceted. First, students need to understand the background to the Somalia conflict. Next, they need to understand why many Somalis chose Minnesota as their newest home.

Somalia SO: International Migrant Stock: % of Population data was reported at 0.234 % in 2015. This records a decrease from the previous number of 0.250 % for 2010. Somalia SO: International Migrant Stock: % of Population data is updated yearly, averaging 0.261 % from Dec 1990 (Median) to 2015, with 6 observations. The data reached an all-time high of 7.566 % in 1990 and a record low of 0.234 % in 2015. Somalia SO: International Migrant Stock: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Somalia – Table SO.World Bank.WDI: Population and Urbanization Statistics. International migrant stock is the number of people born in a country other than that in which they live. It also includes refugees. The data used to estimate the international migrant stock at a particular time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population--people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population--that is, people who are citizens of a country other than the country in which they reside--are used as estimates. After the breakup of the Soviet Union in 1991 people living in one of the newly independent countries who were born in another were classified as international migrants. Estimates of migrant stock in the newly independent states from 1990 on are based on the 1989 census of the Soviet Union. For countries with information on the international migrant stock for at least two points in time, interpolation or extrapolation was used to estimate the international migrant stock on July 1 of the reference years. For countries with only one observation, estimates for the reference years were derived using rates of change in the migrant stock in the years preceding or following the single observation available. A model was used to estimate migrants for countries that had no data.; ; United Nations Population Division, Trends in Total Migrant Stock: 2008 Revision.; Weighted average;

Abstract

In December 2017, the World Bank, in collaboration with Somali statistical authorities conducted the second wave of the Somali High Frequency Survey to monitor welfare and perceptions of citizens in all accessible areas of 17 regions within Somalia’s pre-war borders including Somaliland which self-declared independence in 1991. The survey interviewed 4,011 urban households, 1,106 rural households, 468 households in Internally Displaced People (IDP) settlements and 507 nomadic households. The sample was drawn randomly based on a multi-level clustered design. This dataset contains information on economic conditions, education, employment, access to services, security, perceptions and details before displacement for displaced households. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology as detailed in Pape and Mistiaen (2014).

Geographic coverage

The following pre-war regions: Awdal, Bakool, Banadir, Bari, Bay, Galgaduug, Gedo, Hiran, Lower Juba, Mudug, Nugaal, Sanaag, Middle and lower Shabelle, Sool, Togdheer and Woqooyi Galbeed (Somaliland self-declared independence in 1991).

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Wave 2 of the SHFS employed a multi-stage stratified random sample, ensuring a sample representative of all subpopulations of interest. Strata were defined along two dimensions - administrative location (pre-war regions and emerging states) and population type (urban areas, rural settlements, IDP settlements, and nomadic population). Households were clustered into enumeration areas (EAs), with 12 interviews was expected for each selected EA. Primary sampling units (PSUs) were generated using a variety of techniques depending on the population type. The primary sampling unit (PSU) in urban as well as rural strata was the enumeration area (EA). For IDP strata, primary sampling units were IDP settlements as defined by UNCHR’s Shelter Cluster. Across all strata, PSUs were selected using a systematic random sampling approach with selection probability proportional to size (PPS). In IDP strata, PPS sampling is applied at the IDP settlement level. In second- and final-stage sample selection, a microlisting approach was used, such that EAs were divided into 12 smaller enumeration blocks, which were selected with equal probability. Every block was selected as 12 interviews per EA were required. A similar second-stage sampling strategy was employed for IDP strata. Each IDP settlement was segmented manually into enumeration blocks. Finally, one household per block was interviewed in all selected blocks within the enumeration area.The household was selected randomly with equal probability in two stages, following the micro-listing protocol. The strategy for sampling nomadic households relied on lists of water points. The list of water points was divided up by stratum at the federated member state level and they served as primary sampling units. Water points were selected in the first stage with equal probability, with 12 interviews to be conducted at each selected water point. The selection of nomadic households to interview relied on a listing process at each water point whose aim was to compile an exhaustive list of all nomadic households at the water point. For more details, see accompanying documents, available under the related materials tab.

Sampling deviation

EAs were replaced if security rendered field work unfeasible. Replacements were approved by the project manager. Replacement of households were approved by the supervisor after a total of three unsuccessful visits of the household.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household questionnaire is in English. It includes the following modules: - Introduction - Module A: Administrative Information - Module B: Interview Information and Filters - Module C: Household Roster - Module D: Household Characteristics - Module E: Food Consumption - Module F: Non-Food Consumption - Module G: Livestock - Module H: Durable Goods - Module I: Perceptions and Social Services - Module J: Displacement - Module K: Fishing - Module L: Catastrophic Events and Disasters - Module M: Enumerator Conclusions - Appendix A - Enabling Conditions - Appendix B - Validation Conditions and Messages - Appendix C - Instructions - Appendix D - Options - Appendix E - Variables - Appendix F - Option Filters

The household questionnaire is provided under the Related Materials tab.

The goal of this project is to strengthen resilience to shocks and stresses for vulnerable IDPs and host community households in Baidoa during the project period (November 1, 2021, to October 31, 2024). The project follows a path that starts off with participant identification and consumption support, both unconditional and conditional, to stabilize ultra-poor households’ food security situation. Savings groups will be established or strengthened at the same time to cultivate the culture of saving. Savings groups will serve as the platform for skills transfer, social cohesion, and transformation, as well as economic inclusion. Income generating capacity will be supported through vocational and financial training and transfer of capital. Throughout the project cycle, participants will be coached on social capital mobilization, financial literacy, and business facilitation.

Overview

At the end of the 2018 fiscal year, the U.S. had resettled 22,491 refugees -- a small fraction of the number of people who had entered in prior years. This is the smallest annual number of refugees since Congress passed a law in 1980 creating the modern resettlement system.

It's also well below the cap of 45,000 set by the administration for 2018, and less than thirty percent of the number granted entry in the final year of Barack Obama’s presidency. It's also significantly below the cap for 2019 announced by President Trump's administration, which is 30,000.

The Associated Press is updating its data on refugees through fiscal year 2018, which ended Sept. 30, to help reporters continue coverage of this story. Previous Associated Press data on refugees can be found here.

Data obtained from the State Department's Bureau of Population, Refugees and Migration show the mix of refugees also has changed substantially:

• The numbers of Iraqi, Somali and Syrian refugees -- who made up more than a third of all resettlements in the U.S. in the prior five years -- have almost entirely disappeared. Refugees from those three countries comprise about two percent of the 2018 resettlements.
• In 2018, Christians have made up more than sixty percent of the refugee population, while the share of Muslims has dropped from roughly 45 percent of refugees in fiscal year 2016 to about 15 percent. (This data is not available at the city or state level.)
• Of the states that usually average at least 100 resettlements, Maine, Louisiana, Michigan, Florida, California, Oklahoma and Texas have seen the largest percentage decreases in refugees. All have had their refugee caseloads drop more than 75% when comparing 2018 to the average over the previous five years (2013-2017).

The past fiscal year marks a dramatic change in the refugee program, with only a fraction as many people entering. That affects refugees currently in the U.S., who may be waiting on relatives to arrive. It affects refugees in other countries, hoping to get to the United States for safety or other reasons. And it affects the organizations that work to house and resettle these refugees, who only a few years ago were dealing with record numbers of people. Several agencies have already closed their doors; others have laid off workers and cut back their programs.

Because there is wide geographic variations on resettlement depending on refugees' country of origin, some U.S. cities have been more affected by this than others. For instance, in past years, Iraqis have resettled most often in San Diego, Calif., or Houston. Now, with only a handful of Iraqis being admitted in 2018, those cities have seen some of the biggest drop-offs in resettlement numbers.

About This Data

Datasheets include:

• Annual_refugee_data: This provides the rawest form of the data from Oct. 1, 2008 – Sept. 30, 2018, where each record is a combination of fiscal year, city for refugee arrivals to a specific city and state and from a specific origin. Also provides annual totals for the state.
• City_refugees: This provides data grouped by city for refugee arrivals to a specific city and state and from a specific origin, showing totals for each year next to each other in different columns, so you can quickly see trends over time. Data is from Oct. 1, 2008 – Sept. 30, 2018, grouped by fiscal year. It also compares 2018 numbers to a five-year average from 2013-2017.
• City_refugees_and_foreign_born_proportions: This provides the data in City_refugees along with data that gives context to the origins of the foreign born populations living in each city. There are regional columns, sub-regional columns and a column specific to the origin listed in the refugee data. Data is from the American Community Survey 5-year 2013-2017 Table B05006: PLACE OF BIRTH FOR THE FOREIGN-BORN POPULATION. ### Caveats According to the State Department: "This data tracks the movement of refugees from various countries around the world to the U.S. for resettlement under the U.S. Refugee Admissions Program." The data does not include other types of immigration or visits to the U.S.

The data tracks the refugees' stated destination in the United States. In many cases, this is where the refugees first lived, although many may have since moved.

Be aware that some cities with particularly high totals may be the locations of refugee resettlement programs -- for instance, Glendale, Calif., is home to both Catholic Charities of Los Angeles and the International Rescue Committee of Los Angeles, which work at resettling refugees.

About Refugee Resettlement

The data for refugees from other countries - or for any particular timeframe since 2002 - can be accessed through the State Department's Refugee Processing Center's site by clicking on "Arrivals by Destination and Nationality."

The Refugee Processing Center used to publish a state-by-state list of affiliate refugee organizations -- the groups that help refugees settle in the U.S. That list was last updated in January 2017, so it may now be out of date. It can be found here.

For general information about the U.S. refugee resettlement program, see this State Department description. For more detailed information about the program and proposed 2018 caps and changes, see the FY 2018 Report to Congress.

Queries

The Associated Press has set up a number of pre-written queries to help you filter this data and find local stories. Queries can be accessed by clicking on their names in the upper right hand bar.

• Find Cities Impacted - Most Change -- Use this query to see the cities that have seen the largest drop-offs in refugee resettlements. Creates a five-year average of how many refugees of a certain origin have come in the past, and then measures 2018 by that. Be wary of small raw numbers when considering the percentages!
• Total Refugees for Each City in Your State -- Use this query to get the number of total refugees who've resettled in your state's cities by year.
• Total Refugees in Your State -- Use this query to get the number of total refugees who've resettled in your state by year.
• Changes in Origin over Time -- Use this query to track how many refugees are coming from each origin by year. The initial query provides national numbers, but can be filtered for state or even for city.
• Extract Raw Data for Your State -- Use this query to type in your state name to extract and download just the data in your state. This is the raw data from the State Department, so it may be slightly more difficult to see changes over time. ###### Contact AP Data Journalist Michelle Minkoff with questions, mminkoff@ap.org

GDP per capita based on PPP of Somalia grew by 3.65% from 1,926 international dollars in 2022 to 1,996 international dollars in 2023. Since the 3.96% dip in 2020, GDP per capita based on PPP soared by 16.19% in 2023. GDP per capita (PPP based) is gross domestic product converted to international dollars using purchasing power parity rates and divided by total population. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States. A purchasing power parity (PPP) between two countries, A and B, is the ratio of the number of units of country A’s currency needed to purchase in country A the same quantity of a specific good or service as one unit of country B’s currency will purchase in country B. PPPs can be expressed in the currency of either of the countries. In practice, they are usually computed among large numbers of countries and expressed in terms of a single currency, with the U.S. dollar (US$) most commonly used as the base or “numeraire” currency.

Abstract

In December 2017, the World Bank, in collaboration with Somali statistical authorities conducted the second wave of the Somali High Frequency Survey to monitor welfare and perceptions of citizens in all accessible areas of 17 regions within Somalia’s pre-war borders including Somaliland which self-declared independence in 1991. The survey interviewed 4,011 urban households, 1,106 rural households, 468 households in Internally Displaced People (IDP) settlements and 507 nomadic households. The sample was drawn randomly based on a multi-level clustered design. This dataset contains information on economic conditions, education, employment, access to services, security, perceptions and details before displacement for displaced households. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology as detailed in Pape and Mistiaen (2014).

Geographic coverage

The following pre-war regions: Awdal, Bakool, Banadir, Bari, Bay, Galgaduug, Gedo, Hiran, Lower Juba, Mudug, Nugaal, Sanaag, Middle and lower Shabelle, Sool, Togdheer and Woqooyi Galbeed (Somaliland self-declared independence in 1991).

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Wave 2 of the SHFS employed a multi-stage stratified random sample, ensuring a sample representative of all subpopulations of interest. Strata were defined along two dimensions - administrative location (pre-war regions and emerging states) and population type (urban areas, rural settlements, IDP settlements, and nomadic population). Households were clustered into enumeration areas (EAs), with 12 interviews was expected for each selected EA. Primary sampling units (PSUs) were generated using a variety of techniques depending on the population type. The primary sampling unit (PSU) in urban as well as rural strata was the enumeration area (EA). For IDP strata, primary sampling units were IDP settlements as defined by UNCHR’s Shelter Cluster. Across all strata, PSUs were selected using a systematic random sampling approach with selection probability proportional to size (PPS). In IDP strata, PPS sampling is applied at the IDP settlement level. In second- and final-stage sample selection, a microlisting approach was used, such that EAs were divided into 12 smaller enumeration blocks, which were selected with equal probability. Every block was selected as 12 interviews per EA were required. A similar second-stage sampling strategy was employed for IDP strata. Each IDP settlement was segmented manually into enumeration blocks. Finally, one household per block was interviewed in all selected blocks within the enumeration area.The household was selected randomly with equal probability in two stages, following the micro-listing protocol. The strategy for sampling nomadic households relied on lists of water points. The list of water points was divided up by stratum at the federated member state level and they served as primary sampling units. Water points were selected in the first stage with equal probability, with 12 interviews to be conducted at each selected water point. The selection of nomadic households to interview relied on a listing process at each water point whose aim was to compile an exhaustive list of all nomadic households at the water point. For more details, see accompanying documents, available under the related materials tab.

Sampling deviation

EAs were replaced if security rendered field work unfeasible. Replacements were approved by the project manager. Replacement of households were approved by the supervisor after a total of three unsuccessful visits of the household.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household questionnaire is in English. It includes the following modules: - Introduction - Module A: Administrative Information - Module B: Interview Information and Filters - Module C: Household Roster - Module D: Household Characteristics - Module E: Food Consumption - Module F: Non-Food Consumption - Module G: Livestock - Module H: Durable Goods - Module I: Perceptions and Social Services - Module J: Displacement - Module K: Fishing - Module L: Catastrophic Events and Disasters - Module M: Enumerator Conclusions - Appendix A - Enabling Conditions - Appendix B - Validation Conditions and Messages - Appendix C - Instructions - Appendix D - Options - Appendix E - Variables - Appendix F - Option Filters

The household questionnaire is provided under the Related Materials tab.

Abstract

The Rapid Emergency Response Survey (RERS) 2017 is a pilot project that developed a rapid, low cost methodology using phone interviews to identify critical developmental binding constraints to inform a developmental response to populations in crisis. The RERS was conducted in Nigeria, Somalia, South Sudan and Yemen, where food shortage from a prolonged drought brought large portions of the populations to the brink of famine. These conditions urged a rapid humanitarian short-term response but also requires a developmental intervention to restore assets and create resilience for future shocks. The RERS collects data to inform the developmental response.

Geographic coverage

For the Somali population, pre-war regions declared to be in Emergency or worse are surveyed. This comprises of the regions Bakool, Bay, Bari, Galguduud, Gedo, Hiran, Lower Shabelle, Mudug, Nugaal, Sanaag, Sool, Toghdeer and Woqooyi Galbeed.

In South Sudan, former states declared to be in Emergency or worse are surveyed. This comprises of Central Equatoria, Jonglei, Nothern Bahr-el-Gazal, Unity, Upper Nile and Western Bahr-el-Gazal.

In Yemen, the survey covers all governorates, stratified into Emergency and non-emergency strata. Governorates classified as ‘Emergency’ are Abyan, Al Bayda, Hajjah, Lahj, Sa’ada, Sana’a, Shabwah and Taizz. Non-‘Emergency’ governorates are Aden, Al Dhale’e, Al Hudaydah, Al Mahwit, Amanat Al Asimah, Amran, Dhamar, Hadramaut, Ibb, Marib and Raymah.

Analysis unit

• Households

Universe

Households with active phone connections and charged phones in 13 (pre-war) regions classified to be under ‘Emergency’.

Kind of data

Sample survey data [ssd]

Sampling procedure

SOMALIA

• Population targeted: Households with active phone connections and charged phones in 13 (pre-war) regions classified to be under ‘Emergency’ phase as per the IPC.

• Sample structure: 2600 households, stratified by region. A random sample was drawn for each strata based on a sampling frame of phone numbers that responded to a mass text message sent for this purpose.

SOUTH SUDAN

• Population targeted: Households with active phone connections and charged phones in 6 (pre-war) states classified to be under ‘Emergency’ phase as per the IPC.

• Sample structure: 1200 households, stratified by state. A random sample was drawn for each of the strata using random digit dialing.

YEMEN

• Population targeted: Households with active phone connections and charged phones across all (21) governorates in the country and the capital City, Sana’a.

• Sample structure: 1800 households, stratified by governorate (the capital Sana’a is a separate strata in itself). A random sample -was drawn from each governorate and the capital Sana’a, using random digit dialing.

The sample size of these strata is low and would yield large confidence intervals for the estimates. Thus, for analysis the strata can be grouped into 'analytical strata' as follows:

1. Governorates in emergency or worse as per the IPC.

2. Governorates not in emergency as per IPC.

3. Capital city of Sana’a.

Sampling deviation

In Yemen, three governorates, Al Jawf, Al Maharah and Socotra, could not be reached over the phone, thus they were dropped and the share of planned interviews was evenly spread among other governorates.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire covers modules on income, employment, schooling, market and food access, water and health. Many questions explore changes in these areas over the previous 1 to 12 months, to understand the impacts of the current food security crisis. The questionnaire also includes the Coping Strategies Index (CSI), which measures severity of food insecurity. This index has been used as a measure of household vulnerability, which is correlated against other variables to understand the profiles of households that are most vulnerable.

All questionnaires and modules are provided as Related Materials.

1.996 (Int. Dollar (PPK) pro Kopf) in 2023. GDP per capita (PPP based) is gross domestic product converted to international dollars using purchasing power parity rates and divided by total population. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States. A purchasing power parity (PPP) between two countries, A and B, is the ratio of the number of units of country A’s currency needed to purchase in country A the same quantity of a specific good or service as one unit of country B’s currency will purchase in country B. PPPs can be expressed in the currency of either of the countries. In practice, they are usually computed among large numbers of countries and expressed in terms of a single currency, with the U.S. dollar (US$) most commonly used as the base or “numeraire” currency.

Abstract

The 2005 Ethiopia Demographic and Health Survey (2005 EDHS) is part of the worldwide MEASURE DHS project which is funded by the United States Agency for International Development (USAID).

The principal objective of the 2005 Ethiopia Demographic and Health Survey (DHS) is to provide current and reliable data on fertility and family planning behaviour, child mortality, adult and maternal mortality, children’s nutritional status, the utilization of maternal and child health services, knowledge of HIV/AIDS and prevalence of HIV/AIDS and anaemia.

The specific objectives are to: - collect data at the national level which will allow the calculation of key demographic rates; - analyze the direct and indirect factors which determine the level and trends of fertility; - measure the level of contraceptive knowledge and practice of women and men by method, urban-rural residence, and region; - collect high quality data on family health including immunization coverage among children, prevalence and treatment of diarrhoea and other diseases among children under five, and maternity care indicators including antenatal visits and assistance at delivery; - collect data on infant and child mortality and maternal and adult mortality; - obtain data on child feeding practices including breastfeeding and collect anthropometric measures to use in assessing the nutritional status of women and children; - collect data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluate patterns of recent behaviour regarding condom use; - conduct haemoglobin testing on women age 15-49 and children under age five years in a subsample of the households selected for the survey to provide information on the prevalence of anaemia among women in the reproductive ages and young children; - collect samples for anonymous HIV testing from women and men in the reproductive ages to provide information on the prevalence of HIV among the adult population.

This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general and reproductive health in particular at both the national and regional levels. A long-term objective of the survey is to strengthen the technical capacity of the Central Statistical Agency to plan, conduct, process, and analyse data from complex national population and health surveys. Moreover, the 2005 Ethiopia DHS provides national and regional estimates on population and health that are comparable to data collected in similar surveys in other developing countries. The first ever Demographic and Health Survey (DHS) in Ethiopia was conducted in the year 2000 as part of the worldwide DHS programme. Data from the 2005 Ethiopia DHS survey, the second such survey, add to the vast and growing international database on demographic and health variables.

Wherever possible, the 2005 EDHS data is compared with data from the 2000 EDHS. In addition, where applicable, the 2005 EDHS is compared with the 1990 NFFS, which also sampled women age 15-49. Husbands of currently married women were also covered in this survey. However, for security and other reasons, the NFFS excluded from its coverage Eritrea, Tigray, Asseb, and Ogaden autonomous regions. In addition, fieldwork could not be carried out for Northern Gondar, Southern Gondar, Northern Wello, and Southern Wello due to security reasons. Thus, any comparison between the EDHS and the NFFS has to be interpreted with caution.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The 2005 EDHS sample was designed to provide estimates for the health and demographic variables of interest for the following domains: Ethiopia as a whole; urban and rural areas of Ethiopia (each as a separate domain); and 11 geographic areas (9 regions and 2 city administrations), namely: Tigray; Affar; Amhara; Oromiya; Somali; Benishangul-Gumuz; Southern Nations, Nationalities and Peoples (SNNP); Gambela; Harari; Addis Ababa and Dire Dawa. In general, a DHS sample is stratified, clustered and selected in two stages. In the 2005 EDHS a representative sample of approximately 14,500 households from 540 clusters was selected. The sample was selected in two stages. In the first stage, 540 clusters (145 urban and 395 rural) were selected from the list of enumeration areas (EA) from the 1994 Population and Housing Census sample frame.

In the census frame, each of the 11 administrative areas is subdivided into zones and each zone into weredas. In addition to these administrative units, each wereda was subdivided into convenient areas called census EAs. Each EA was either totally urban or rural and the EAs were grouped by administrative wereda. Demarcated cartographic maps as well as census household and population data were also available for each census EA. The 1994 Census provided an adequate frame for drawing the sample for the 2005 EDHS. As in the 2000 EDHS, the 2005 EDHS sampled three of seven zones in the Somali Region (namely, Jijiga, Shinile and Liben). In the Affar Region the incomplete frame used in 2000 was improved adding a list of villages not previously included, to improve the region's representativeness in the survey. However, despite efforts to cover the settled population, there may be some bias in the representativeness of the regional estimates for both the Somali and Affar regions, primarily because the census frame excluded some areas in these regions that had a predominantly nomadic population.

The 540 EAs selected for the EDHS are not distributed by region proportionally to the census population. Thus, the sample for the 2005 EDHS must be weighted to produce national estimates. As part of the second stage, a complete household listing was carried out in each selected cluster. The listing operation lasted for three months from November 2004 to January 2005. Between 24 and 32 households from each cluster were then systematically selected for participation in the survey.

Because of the way the sample was designed, the number of cases in some regions appear small since they are weighted to make the regional distribution nationally representative. Throughout this report, numbers in the tables reflect weighted numbers. To ensure statistical reliability, percentages based on 25 to 49 unweighted cases are shown in parentheses and percentages based on fewer than 25 unweighted cases are suppressed.

Note: See detailed sample implementation table in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

In order to adapt the standard DHS core questionnaires to the specific socio-cultural settings and needs in Ethiopia, its contents were revised through a technical committee composed of senior and experienced demographers of PHCCO. After the draft questionnaires were prepared in English, copies of the household, women’s and men’s questionnaires were distributed to relevant institutions and individual researchers for comments. A one-day workshop was organized on November 22, 2004 at the Ghion Hotel in Addis Ababa to discuss the contents of the questionnaire. Over 50 participants attended the national workshop and their comments and suggestions collected. Based on these comments, further revisions were made on the contents of the questionnaires. Some additional questions were included at the request of MOH, the Fistula Hospital, and USAID. The questionnaires were finalized in English and translated into the three main local languages: Amharic, Oromiffa and Tigrigna. In addition, the DHS core interviewer’s manual for the Women’s and Men’s Questionnaires, the supervisor’s and editor’s manual, and the HIV and anaemia field manual were modified and translated into Amharic.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight measurements of women age 15-49 and children under the age of five, households eligible for collection of blood samples, and the respondents’ consent to voluntarily give blood samples.

The Women’s Questionnaire was used to collect information from all women age 15-49 years and covered the following topics. - Household and respondent characteristics - Fertility levels and preferences - Knowledge and use of family planning - Childhood mortality - Maternity care - Childhood illness, treatment, and preventative actions - Anaemia levels among women and children - Breastfeeding practices - Nutritional status of women and young children - Malaria prevention and treatment - Marriage and sexual activity - Awareness and behaviour regarding AIDS and STIs - Harmful traditional practices - Maternal mortality

The Men’s Questionnaire was administered to all men age 15-59 years living in every second household in the sample. The Men’s Questionnaire collected similar information contained in the Women’s Questionnaire, but was shorter because it did not contain questions on reproductive