This statistic shows the total population of Somalia from 2013 to 2023 by gender. In 2023, Somalia's female population amounted to approximately 9.16 million, while the male population amounted to approximately 9.2 million inhabitants.

Abstract

High Frequency Phone Survey for Displaced Population in Somalia helps to fill the important data and knowledge gaps on displaced populations and host communities to support timely and evidence-informed decisions that can improve the lives of one of the most vulnerable groups in Somalia. Displaced population including IDPs, refugees and returnees are recognized as among the most vulnerable groups in the Somalia National Development Plan, but the paucity of data makes it difficult to adequately prescribe policy recommendations that will improve their lives. Humanitarian partners, including UNHCR and the International Organization for Migration, benefit from the information generated to better target their responses in times of crisis. It will also be used by the World Bank to support country dialogue, inform operations, and expand the knowledge base on displacement in Somalia. The time-series nature of the survey will enable the tracking of the impact of shocks on specific socio-economic indicators to allow for better timing of interventions.

Two survey rounds conducted from November 2021 to August 2022 yield samples for five population groups: host communities for IDPs, IDPs in and out of settlements, refugees and asylum seekers and refugee returnees. Implemented by the World Bank in collaboration with the United Nations High Commissioner for Refugees (UNHCR) and the National Bureau of Statistics (NBS) in Somalia, this cost-effective phone-based survey aimed to follow the same respondents over a period of time.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

Households with access to phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample consists of five strata: (i) host communities; (ii) IDPs living in settlements; (iii) IDPs living outside settlements; (iv) refugees; and (v) refugee returnees. Each stratum consisted of about 500 households, making up the total sample of around 2,500 respondents.

Samples for the host communities and IDPs living outside settlements were selected from the previous national phone survey (Somalia high frequency phone survey - SHFPS) conducted by the World Bank in Somalia from June 2020 until October 2021. The sample for host communities was selected on the basis of frequency of interaction with IDP populations, with households that reported that they had had interacted with the IDPs at least once a month collected for the sample. For IDPs living in the settlements, phone numbers were collected by UNHCR from the settlements in Bay and Banadir, while those for refugees and refugee returnees were provided from the UNHCR database.

Except for IDPs in settlements, the majority of the displacement-affected households surveyed live in urban areas. The majority of the refugees in Somalia are either from Ethiopia (54 percent) and Yemen (41 percent). Therefore, this survey focused on these two refugee groups. The refugee households mostly live in Somaliland (53 percent) with a considerable number in Puntland (28 percent) and Banadir (15 percent). In the case of refugee returnees, about 11,606 households were registered in the UNHCR database at the time of sample selection, mostly coming from Kenya (97 percent) and Yemen (2 percent). Both these groups were included in the sample proportionally to their population share. The majority of the sampled refugee returnees live in Jubaland (78 percent). As for settlement-based IDPs, two main regions—Banadir and Bay—which host almost 50 percent of the settlement-based IDPs in Somalia were focused.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Only households that consented to being interviewed were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset.

Somalia is on the path to political and security stabilization after more than two decades of civil war and conflict. Opportunities to ensure a development trajectory face many challenges since the country remains a fragile state subject to multiple shocks. Widespread poverty and food insecurity is a recurring developmental issue. Displacement is a key feature of modern Somali history linked to multiple drivers, including recurrent exposure to internal conflict and environmental hazards. Somalia is urbanizing rapidly due to large-scale forced displacement and economic migration that have driven large numbers of Somalis toward the urban areas. Remittances are central to the Somali economy and provide a lifeline to some segments of the population but not the most vulnerable. The World Bank implemented the second wave of the Somali high frequency survey (SHFS) in 2017-2018. This report is based on the most recent and first extensive household survey, wave 2 of the SHFS. The report is organized into six chapters. The first chapter presents an updated profile of monetary and nonmonetary dimensions of poverty for the Somali population, including the nomadic population. The second chapter explores in more detail spatial variation, with a focus on urbanization. The third chapter examines the impact of the 2016-2017 drought on livelihoods to identify the populations at risk and the factors that protected households against its negative effects. The fourth chapter provides an in-depth analysis of the internally displaced populations to identify displacement-related needs and to inform durable solutions. As a reaction to the analysis of poverty and vulnerabilities, the fifth chapter focuses on social protection as a means of promoting equity and building resilience against the effect of shocks on livelihoods. Similarly, the sixth chapter examines remittances and their role for livelihoods and resilience.

Somalia SO: Population: Female: Ages 20-24: % of Female Population data was reported at 9.036 % in 2017. This records an increase from the previous number of 8.951 % for 2016. Somalia SO: Population: Female: Ages 20-24: % of Female Population data is updated yearly, averaging 8.565 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9.036 % in 2017 and a record low of 7.932 % in 2004. Somalia SO: Population: Female: Ages 20-24: % of Female 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: Population and Urbanization Statistics. Female population between the ages 20 to 24 as a percentage of the total female population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

Abstract

The coronavirus disease 2019 (COVID-19) pandemic and its effects on households create an urgent need for timely data and evidence to help monitor and mitigate the social and economic impacts of the crisis on the Somali people, especially the poor and most vulnerable. To monitor the socioeconomic impacts of the COVID-19 pandemic and inform policy responses and interventions, the World Bank as part of a global initiative designed and conducted a nationally representative COVID-19 Somali High-Frequency Phone Survey (SHFPS) of households. The survey covers important and relevant topics, including knowledge of COVID-19 and adoption of preventative behavior, economic activity and income sources, access to basic goods and services, exposure to shocks and coping mechanisms, and access to social assistance.

Geographic coverage

National. Jubaland, South West, HirShabelle, Galmudug, Puntland, and Somaliland (self-declared independence in 1991), and Banadir.

Analysis unit

• Households

Universe

Households with access to phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample allocation for the COVID-19 SHFPS has been developed to provide representative and reliable estimates nationally, and at the level of Jubaland, South West, HirShabelle, Galmudug, Puntland, Somaliland, Banadir Regional Administration and by population type (i.e. urban, rural, nomads, and IDPs populations). The sampling procedure had two steps. The sample was stratified according to the 18 pre-war regions—which are the country’s first-level administrative divisions—and population types. This resulted in 57 strata, of which 7 are IDP, 17 are nomadic, 16 are exclusively urban strata, 15 exclusively rural, and 2 are combined urban-rural strata. The sample size in some strata was too small, thus urban and rural areas were merged into one single strata; this was the case for Sool and Sanaag.

Round 1 of the COVID-19 SHFPS was implemented between June and July 2020. The survey interviewed 2,811 households (1,735 urban households, 611 rural households, 435 nomadic households, and 30 IDP households in settlements). The sample of 2,811 households was contacted using a random digit dialing protocol. The sampling frame was the SHFPS Round 1 data - the same households from Round 1 are tracked over time, allowing for the monitoring of the well-being of households in near-real time and enabling an evidence-based response to the COVID-19 crisis.

Round 2 of the COVID-19 SHFPS was implemented in January 2021. A total of 1,756 households were surveyed (738 urban households, 647 rural households, 309 nomadic households, and 62 IDP households in settlements). Of the 1,756 households, 91 percent were successfully re-contacted from Round 1, with the remainder reached via random digit dialing. Administration of the questionnaire took on average 30 minutes.

Sampling deviation

The target sample for Round 1 was 3,000 households. The realized sample consists of 2,811 households. Reaching rural and nomadic-lifestyle respondents proved to be difficult in a phone survey setting due to lifestyle considerations and relatively lower phone penetration compared to urban settings. To overcome this challenge, the following were performed: - Lowering the sample size of the rural stratum - Reducing the number of interviews in the oversampled urban strata of Kismayo (Jubaland – Lower Juba/Urban) and Baidoa (South West State – Bay/Urban) - Utilizing snowball sampling methodology (i.e. referrals) to increase the sample for hard-to-reach population types, namely the nomadic households.

In Round 2, initially, a sample size of 1,800 households was targeted. However, due to implementation challenges in reaching specific population groups via phone, the sample size was slightly reduced. At the end of the data collection, 1,756 households had been interviewed.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire of the COVID-19 Somali High-Frequency Phone Survey (SHFPS) of households consists of the following sections:

• Interview information (R1, R2)
• Household roster (R1, R2)
• Knowledge regarding the spread of COVID-19 (R1, R2)
• Behavior and social distancing (R1, R2)
• Concerns related to the COVID-19 pandemic (R1, R2)
• COVID-19 vaccine (R2)
• Access to basic goods and services (R1, R2)
• Employment (R1, R2)
• Income loss (R1, R2)
• Remittances (R1, R2)
• Mortality (R2)
• Shocks and coping mechanisms (R1, R2)
• Food insecurity (R1, R2)
• Social assistance and safety nets (R1, R2)
• Interaction with internally displaced persons (R2)

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Only households that consented to being interviewed were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset.

Response rate

The response rate is defined as the percentage of reached eligible households willing to participate in the survey. It is calculated as the number of interviewed households over the number of reached eligible households, thus excluding unreached households (i.e. invalid numbers or failure to contact the household) and households that were reached but were not eligible to participate in the survey (as determined by the minimum age requirement of the main respondent and sampling criteria).

The response rate for Round 1 was nearly 80 percent. In Round 2, 91 percent of the 1,756 households surveyed were successfully re-contacted from Round 1, with the remainder reached via random digit dialing.

Abstract

The SHDS is a national sample survey designed to provide information on population, birth spacing, reproductive health, nutrition, maternal and child health, child survival, HIV/AIDS and sexually transmitted infections (STIs), in Somalia.. The main objective of the SHDS was to provide evidence on the health and demographic characteristics of the Somali population that will guide the development of programmes and formulation of effective policies. This information would also help monitor and evaluate national, sub-national and sector development plans, including the Sustainable Development Goals (SDGs), both by the government and development partners. The target population for SHDS was the women between 15 and 49 years of age, and the children less than the age of 5 years

Geographic coverage

The SHDS 2020 was a nationally representative household survey.

Analysis unit

The unit analysis of this survey are households, women aged 15-49 and children aged 0-5

Universe

This sample survey covered Women aged 15-49 and Children aged 0-5 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample for the SHDS was designed to provide estimates of key indicators for the country as a whole, for each of the eighteen pre-war geographical regions, which are the country's first-level administrative divisions, as well as separately for urban, rural and nomadic areas. With the exception of Banadir region, which is considered fully urban, each region was stratified into urban, rural and nomadic areas, yielding a total of 55 sampling strata. All three strata of Lower Shabelle and Middle Juba regions, as well as the rural and nomadic strata of Bay region, were completely excluded from the survey due to security reasons. A final total of 47 sampling strata formed the sampling frame. Through the use of up-to-date, high-resolution satellite imagery, as well as on-the-ground knowledge of staff from the respective ministries of planning, all dwelling structures were digitized in urban and rural areas. Enumeration Areas (EAs) were formed onscreen through a spatial count of dwelling structures in a Geographic Information System (GIS) software. Thereafter, a sample ground verification of the digitized structures was carried out for large urban and rural areas and necessary adjustments made to the frame.

Each EA created had a minimum of 50 and a maximum of 149 dwelling structures. A total of 10,525 EAs were digitized: 7,488 in urban areas and 3,037 in rural areas. However, because of security and accessibility constraints, not all digitized areas were included in the final sampling frame-9,136 EAs (7,308 in urban and 1,828 in rural) formed the final frame. The nomadic frame comprised an updated list of temporary nomadic settlements (TNS) obtained from the nomadic link workers who are tied to these settlements. A total of 2,521 TNS formed the SHDS nomadic sampling frame. The SHDS followed a three-stage stratified cluster sample design in urban and rural strata with a probability proportional to size, for the sampling of Primary Sampling Units (PSU) and Secondary Sampling Units (SSU) (respectively at the first and second stage), and systematic sampling of households at the third stage. For the nomadic stratum, a two-stage stratified cluster sample design was applied with a probability proportional to size for sampling of PSUs at the first stage and systematic sampling of households at the second stage. To ensure that the survey precision is comparable across regions, PSUs were allocated equally to all regions with slight adjustments in two regions. Within each stratum, a sample of 35 EAs was selected independently, with probability proportional to the number of digitized dwelling structures. In this first stage, a total of 1,433 EAs were allocated (to urban - 770 EAs, rural - 488 EAs, and nomadic - 175 EAs) representing about 16 percent of the total frame of EAs. In the urban and rural selected EAs, all households were listed and information on births and deaths was recorded through the maternal mortality questionnaire. The data collected in this first phase was cleaned and a summary of households listed per EA formed the sampling frames for the second phase. In the second stage, 10 EAs were sampled out of the possible 35 that were listed, using probability proportional to the number of households. All households in each of these 10 EAs were serialized based on their location in the EA and 30 of these households sampled for the survey. The serialization was done to ensure distribution of the households interviewed for the survey in the EA sampled. A total of 220 EAs and 150 EAs were allocated to urban and rural strata respectively, while in the third stage, an average of 30 households were selected from the listed households in every EA to yield a total of 16,360 households from 538 EAs covered (220 EAs in urban, 147 EAs in rural and 171 EAs in nomadic) out of the sampled 545 EAs. In nomadic areas, a sample of 10 EAs (in this case TNS) were selected from each nomadic stratum, with probability proportional to the number of estimated households. A complete listing of households was carried out in the selected TNS followed by the selection of 30 households for the main survey interview. In those TNS with less than 30 households, all households were interviewed for the main survey. All eligible ever-married women aged 12 to 49 and never-married women aged 15 to 49 were interviewed in the selected households, while the household questionnaire was administered to all households selected. The maternal mortality questionnaire was administered to all households in each sampled TNS.

Mode of data collection

Face-to-face [f2f]

Response rate

A total of 16,360 households were selected for the sample, of which 15,870 were occupied. Of the occupied households, 15,826 were successfully interviewed, yielding a response rate of 99.7 percent. The SHDS 2020 interviewed 16,486 women-11,876 ever-married women and 4,610 never-married women.

Sampling error estimates

Sampling errors are important data quality parameters which give measure of the precision of the survey estimates. They aid in determining the statistical reliability of survey estimates. The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling 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 Somaliland Health and Demographic Survey ( SHDS 2020) to minimise this type of error, non-sampling 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 SHDS 2020 is only one of many samples that could have been selected from the same population, using the same design and sample 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 by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the SHDS 2020 sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The variance approximation procedure that account for the complex sample design used R program was estimated sampling errors in SHDS which is Taylor series linearization. The non-linear estimates are approximated by linear ones for estimating variance. The linear approximation is derived by taking the first-order Tylor series approximation. Standard variance estimation methods for linear statistics are then used to estimate the variance of the linearized estimator. The Taylor linearisation method treats any linear statistic such as a percentage or mean as a ratio estimate, r = y/x, where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration

Data appraisal

• Household age distribution
• Age distribution of eligible and interviewed women
• Pregnancy- related mortality trends Note: See detailed data quality tables in APPENDIX C of the report.

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.

Abstract

In 2013, the World Bank, in collaboration with the Ministry of Planning and Development implemented the 2013 Somaliland Household Survey (SHS 2013). Somaliland self-declared independence in 1991. The survey interviewed 852 urban and 873 rural households. The sample was drawn randomly based on a multi-level clustered design. This dataset contains information on consumption, income and household characteristics. The sample is representative of urban Somaliland, and parts of rural Somaliland. It does not include nomadic households or those affected by the ongoing conflict. The data and code reproduce some of the results from the original submission of the SHS 2013, but also comparable poverty estimates to those obtained with the Somali High Frequency Survey.

Geographic coverage

The SHS 2013 sample is representative of urban Somaliland, and parts of rural Somaliland.

Analysis unit

Household

Universe

The sample does not include nomadic households (which recent estimates suggest comprises 36% of the population), and omits households in areas affected by ongoing conflict

Kind of data

Sample survey data [ssd]

Sampling procedure

The SHS 2013 interviewed 852 urban and 873 rural households. The frame used for selecting enumeration areas (EAs) was a mixed frame where the database of EAs for the UNFPA urban survey was used for all urban areas (in two strata: Hergaisa and Other Urban). The rural frame used the list of polling stations which was provided by the electoral commission.The sample frame used was the 2012 cartographic list of enumeration areas.

The sample employs a stratified two-staged clustered design with the Primary Sampling Unit (PSU) being the enumeration area. Within each enumeration area, 9 households were selected for interviews. Then, a listing approach was used to select these 9 households randomly for interviews. Three primary strata were defined as: rural, Hergaisa and other urban areas. The population proportion varied by stratum, and the general agreement in informal discussions was that about 50% of the population was urban and 50% were rural.

Sampling deviation

A total of 26 EAs had to be replaced, and except in the case of Hergaisa, the replacements were rural polling stations. The most prevalent problem in the rural area were in the Sool, Sannag and Sahil zones, and these were identified as problems with security. A practical approach was undertaken by using the “nearest secure neighbor”. The idea was to assure that the sample polling station in the same district had similar characteristics to those of the insecure sample PSU, in order to maintain the geographic representativeness of the sample and reduce the bias from the PSU nonresponse

Mode of data collection

Face-to-face [f2f]

Research instrument

The SHS 2013 questionnaire is available under the Related Materials tab

Cleaning operations

Accompanying Stata do-files for carrying out the household analysis using the SHS 2013 data are provided under the Related Materials tab.

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.

Population, female (% of total population) in Somalia was reported at 49.9 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Somalia - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

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 Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 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. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. The Pan Arab Population and Family Health Project(PAPFAM) is a programme conducted to enable national health institutions in the Arab region to obtain a timely and integrated flow of reliable information suitable for formulating, implementing, monitoring and evaluating the family health and reproductive health policies and programs in a cost-effective manner.

MICS and PAPFAM are capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS/PAPFAM is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

Survey Objectives The 2006 Somali Multiple Indicator Cluster Survey (MICS)/Pan Arab Population and Family Health Project(PAPFAM) has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Somalia - To furnish data needed for monitoring progress toward goals established in the Millennium Declaration, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Somalia and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Survey Content Following the MICS global questionnaire templates, the questionnaires were designed in a modular fashion customized to the needs of Somalia. The questionnaires consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker).

Survey Implementation The Somalia MICS/PAPFAM was carried out by UNICEF with the support and assistance the Ministry of Planning and International Cooperation of the Somali Transitional Federal Government, the Ministry of National Planning and Coordination of Somaliland and the Ministry of Planning and International Cooperation of Puntland. Technical assistance and training for the survey was provided through a series of regional workshops organised by UNICEF and PAPFAM, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The Somali 2006 MICS/PAPFAM covers all regions of Somalia. For the purposes of this survey, the analysis refers to the North West Zone, the North East Zone and Central South Zone according to prewar boundaries for Somaliland and Puntland and does not imply any recognition of administrative boundaries by the United Nations or the League of Arab States.

Analysis unit

Households (defined as a group of persons who usually live and eat together)

De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household. The survey also included a full birth history module which covered all live births born to ever-married women aged 15-49.

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample design was to provide current and reliable estimates on a set of indicators covering the four major areas of the World Fit for Children declaration, including promoting healthy lives; providing quality education; protecting against abuse, exploitation and violence; and combating HIV/AIDS. The population covered by the 2006 MICS/PAPFAM is defined as the universe of all women aged 15-49 and all children aged under 5. A sample of households was selected and all women aged 15-49 identified as usual residents of these households were interviewed. In addition, the mother or the caretaker of all children aged under 5 who were usual residents of the household were also interviewed about the child.

The 2006 MICS/PAPFAM collected data from a nationally representative sample of households, women and children. The primary focus of the 2006 MICS/PAPFAM was to provide estimates of key population and health, education, child protection and HIV related indicators for the country as a whole, for the North West, North East and Central South Zones and for urban and rural areas separately. Somalia is divided into 18 regions. Each region is subdivided into districts, and each district into settlements and towns. The sample frame for this survey was based on the list of settlements developed from the 2005-2006 UNDP Settlement Survey and WHO vaccination campaign data.

The Sampling design follows a 4 stage-sample approach. The first stage is the selection of the districts in each of the 18 regions of the country selected using probability proportional to size (pps). The second stage is the selection of the secondary sampling units which are defined as permanent and temporary settlements. The third stage is the selection of the cluster(s) within the settlement and the fourth stage is the selection of the households to be interviewed.

Once the districts had been selected great efforts went into compiling a complete list of permanent and temporary settlements within these districts. The main source was the WHO immunisation campaign data, this data was later backed up by the UNDP settlement survey for at least two out of the three zones. Other sources also contributed such as FAO data on water points which could act as proxy for surrounding nomadic areas and temporary settlements. Finally lists were shown to the NGO partners implementing the survey and UNICEF staff on the ground for additional contributions to recent movement of internally displaced persons and nomads. The settlement lists were then sorted into urban and non urban. The first two stages of sampling were thus completed by selecting the required number of clusters from each of the 3 zones by urban and rural areas separately.

Mapping and Listing Activities

For settlements over the estimated size of 150 households some form of segmentation through sketch mapping was necessary. For several district capitals it was possible to use maps from UN Habitat to assist the personnel deployed in sketch mapping. However for most of the larger non-urban settlements there were no maps available. The most important aspect of the sketch mapping was to divide the settlements into roughly equal sizes by estimating the number of households and to clearly delineate the segments using identifiable boundaries.

Once sketch maps were prepared survey coordinators were then in a position to randomly select the cluster(s) where household would be selected. It must be added at this point that finding people trained in cartographic techniques is rare in Somalia. Thus the quality of the maps varied significantly across the country and resources and time also did not allow for a full household count.

Selection of Households

For the final stage of sampling, the Somali MICS/PAPFAM had no other option than to use the method used in MICS 2 of the Expanded Program for Immunization (EPI) random walk method; the expense of household/dwelling listing would simply be too considerable.

Whilst the EPI method is quick and approximately self-weighting, it is recognised that this is not a probability sample, and so cannot ensure objectivity of household selection. In order to try and avoid the subjectivity involved in selecting households some measures were put in place. For example instead of relying on an arbitrary decision regarding the central point of a cluster, supervisors selected at least three or four possible starting points and then randomly choose one of them. Moreover only supervisors were able to select and number the households, not interviewers. Significant time was spent training supervisors on how to select households in order to avoid some of the criticisms typically directed towards this method.

For clusters falling in nomadic areas (the temporary settlements) the survey teams were instructed to interview the first 24 households that they came across. Typically nomads do not move in large numbers, therefore in order to ensure representation of nomads in the sample it was necessary to assume a more purposive method of sampling for this group.

Sampling deviation

No major deviations from the original sample design were made. All clusters were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Somali MICS/PAPFAM were structured questionnaires based on the MICS3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children

Chart and table of population level and growth rate for the Mogadishu, Somalia metro area from 1950 to 2025.

Somalia SO: Fertility Rate: Total: Births per Woman data was reported at 6.267 Ratio in 2016. This records a decrease from the previous number of 6.365 Ratio for 2015. Somalia SO: Fertility Rate: Total: Births per Woman data is updated yearly, averaging 7.227 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 7.689 Ratio in 1997 and a record low of 6.267 Ratio in 2016. Somalia SO: Fertility Rate: Total: Births per Woman 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: Health Statistics. Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average; Relevance to gender indicator: it can indicate the status of women within households and a woman’s decision about the number and spacing of children.

According to the Global Hunger Index 2024, which was adopted by the International Food Policy Research Institute, Somalia was the most affected by hunger and malnutrition, with an index of 44.1. Yemen and Chad followed behind. The World Hunger Index combines three indicators: undernourishment, child underweight, and child mortality. Sub-Saharan Africa most affected The index is dominated by countries in Sub-Saharan Africa. In the region, more than one fifth of the population is undernourished . In terms of individuals, however, South Asia has the highest number of undernourished people. Globally, there are 735 million people that are considered undernourished or starving. A lack of food is increasing in over 20 countries worldwide. Undernourishment worldwide The term malnutrition includes both undernutrition and overnutrition. Undernutrition occurs when an individual cannot maintain normal bodily functions such as growth, recovering from disease, and both learning and physical work. Some conditions such as diarrhea, malaria, and HIV/AIDS can all have a negative impact on undernutrition. Rural and agricultural communities can be especially susceptible to hunger during certain seasons. The annual hunger gap occurs when a family’s food supply may run out before the next season’s harvest is available and can result in malnutrition. Nevertheless, the prevalence of people worldwide that are undernourished has decreased over the last decades, from 18.7 percent in 1990-92 to 9.2 percent in 2022, but it has slightly increased since the outbreak of COVID-19. According to the Global Hunger Index, the reduction of global hunger has stagnated over the past decade.

Somalia SO: Intentional Homicides: per 100,000 People data was reported at 5.600 Ratio in 2015. This records a decrease from the previous number of 6.600 Ratio for 2010. Somalia SO: Intentional Homicides: per 100,000 People data is updated yearly, averaging 6.100 Ratio from Dec 2005 (Median) to 2015, with 3 observations. The data reached an all-time high of 6.600 Ratio in 2010 and a record low of 5.600 Ratio in 2015. Somalia SO: Intentional Homicides: per 100,000 People 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: Health Statistics. Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; Weighted average;

Diabetes prevalence (% of population ages 20 to 79) in Somalia was reported at 5.8 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Somalia - Diabetes prevalence (% of population ages 20 to 79) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

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

Abstract

The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host communities to design a better mix of approaches which could help the government in designing livelihood opportunities for these communities. The SPS 2017 contains information on employment, barriers to labor force participation, livelihood structures of refugees before displacement, education and economic conditions as well as access to services, and perceptions. The data combines detailed household questionnaire information with displacement-specific information including drivers of displacement, access to resettlement mechanisms, and return intentions. 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 (2018).

Geographic coverage

The Skills Profile Survey 2017 covered refugees in camps, and surrounding host communities, in Ethiopia. Refugees of four nationalities were surveyed: Eritrean, Somali, South Sudanese, and Sudanese. Only the refugees living in camps were surveyed, because tracing households outside the camps was not feasible. However, 66 percent of all refugees in Ethiopia live in camps, while those that live outside camps are largely Eritrean. Host communities, defined as Ethiopian non-displaced households living within a 5km radius of a camp, were also surveyed.

Analysis unit

Household and individual.

Kind of data

Sample survey data [ssd]

Sampling procedure

The SPS 2017 is a household survey with a multi-stage stratified random sample. The sampling frame was the list of refugee camps, sites and locations as of January 2017, provided by UNHCR-Ethiopia. The sample consisted of four strata based on four regions: Tigray Afar (Eritrean refugees), Gambella (South Sudanese refugees), Benishangul Gumuz (Sudanese refugees), and Somali (Somali refugees). Each region hosts predominantly one refugee nationality leading to an implicit stratification based on nationality. In each stratum, camps were divided into Enumeration Areas (EAs) of equal size using GIS technology, and 82 EAs were selected per stratum. Within the stratum, the number of EAs per camp was selected proportional to the size of the camp. Within camps, EAs were selected with equal probability. Within each selected EA, all households were listed and then 12 were randomly selected for interview.

The host community sample consisted of the same four regional strata. Within each stratum, areas within a 5km radius of a camp, were divided into EAs of equal size. Of these EAs, those classified as 'residential' by Open Street Maps were used as the sampling frame. 42 EAs were selected per stratum. Within a stratum, EAs were selected using probability proportional to size, with the probability of selection of an EA corresponding to the area of the EA. Within each selected EA, all households were listed and then 12 were randomly selected for interview.

Sampling deviation

Due to security concerns, revisions were made to the sample during fieldwork. Enumerators in Gambella stratum (hosting South Sudanese refugees) faced repeated security threats and could survey only 439 of the intended 900 refugee households in the region. As the survey team was withdrawn from Gambella region, the host community in Gambella region was not surveyed. The remaining interviews with refugees in Gambella region were substituted by oversampling EAs in Benishangul Gumuz, as 25 percent of the refugee population in this region is South Sudanese. In September 2017, violent conflict in Oromia and Somali regions escalated, rendering some of the camps in Somali stratum inaccessible. The EAs of Jijiga sub-region were replaced by EAs in non-violent areas of Somali stratum. Further, as most refugee camps are in remote areas with sparse host populations, the final number of host households surveyed fell short of the original intended sample of 500 host households per stratum. However, despite the changes in sample, the survey captured roughly similar number of refugee households of the four main refugee nationalities.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire contains modules on Household Member Roster, Household Characteristics, Food Consumption, Non food consumption, Livestock, Durable Good, Wellbeing and Opinions and Forced Displacement, Movement and Return Intentions. The questionnaire is available for download with the dataset.

Cleaning operations

See accompanying Stata do-files, available under the related materials tab.

Response rate

NA

Global Disaster Alert and Coordination System described Cyclone Sagar as one of the strongest storms ever recorded in Somalia. This was a very rare cyclone in the Gulf of Aden that made landfall in north-western Somaliland on 19 May, 2018. This layer shows the occurence of TC Sagar in some regions of Somalia,including Awdal, Togdheer, Sool, Sanaag, Bari, Hiraan, Gedo and Lower Juba.The TC Sagar moved with wind gusts of up to 120 km/hour that delivered a year’s worth of rain to some areas in the north, that is between 150 and 200mm. TC Sagar was reported to have adverse impacts on Somalia regions. The United Nations Office for the Coordination of Humanitarian Affairs (OCHA) reported heavy rainfall, strong winds and dangerous flash floods hitting coastal areas of Puntland and Somaliland. This resulted in the loss of lives, crops and livestock as well as the destruction of property and infrastructure. Recored impacts according to regions included: Awdal, Bari, Sanaag, Sool, Togdheer Number of people affected: 228,000 Number of people displaced: 9,000 Awdal, Hiraan, Gedo, Lower Juba Number of people affected:830,000 Number of people worst hit:170,000 Number of people displaced: 290,000 Number of people death: 50 Infrusructure destroyed:(20-40)schools and 21 health facilities