This statistic shows the median age of the population in Somalia from 1950 to 2100. The median age is the age that divides a population into two numerically equal groups; that is, half the people are younger than this age and half are older. It is a single index that summarizes the age distribution of a population. In 2020, the median age of the Somalian population was 15.3 years.

Temperature in Somalia increased to 27.28 celsius in 2024 from 27.10 celsius in 2023. This dataset includes a chart with historical data for Somalia Average Temperature.

Somalia SO: Average Precipitation in Depth data was reported at 282.000 mm/Year in 2014. This stayed constant from the previous number of 282.000 mm/Year for 2012. Somalia SO: Average Precipitation in Depth data is updated yearly, averaging 282.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 282.000 mm/Year in 2014 and a record low of 282.000 mm/Year in 2014. Somalia SO: Average Precipitation in Depth 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: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

Precipitation in Somalia increased to 287.44 mm in 2024 from 272.46 mm in 2023. This dataset includes a chart with historical data for Somalia Average Precipitation.

The average inflation rate in Somalia was estimated at approximately 5.53 percent in 2024. Between 2013 and 2024, the inflation rose by around 2.73 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The inflation is expected to drop by about 3.03 percentage points between 2024 and 2030, showing a continuous downward movement throughout the period.This indicator measures inflation based upon the year-on-year change in the average consumer price index, expressed in percent. The latter expresses a country's average level of prices based on a typical basket of consumer goods and services.

This horizontal bar chart displays median age (year) by country full name using the aggregation average, weighted by population in Somalia. The data is about countries per year.

The life expectancy experiences significant growth in all gender groups in 2023. As part of the positive trend, the life expectancy reaches the maximum value for the different genders at the end of the comparison period. Particularly noteworthy is the life expectancy of women at birth, which has the highest value of 61.39 years. Life expectancy at birth refers to the number of years that the average newborn can expect to live, providing that mortality patterns at the time of their birth do not change thereafter.Find further similar statistics for other countries or regions like Vietnam and Seychelles.

Somalia: Population size, in millions: The latest value from 2024 is 19.01 million, an increase from 18.36 million in 2023. In comparison, the world average is 41.28 million, based on data from 196 countries. Historically, the average for Somalia from 1960 to 2024 is 8.4 million. The minimum value, 2.88 million, was reached in 1960 while the maximum of 19.01 million was recorded in 2024.

Somalia: Population density, people per square km: The latest value from 2021 is 27 people per square km, an increase from 26 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for Somalia from 1961 to 2021 is 13 people per square km. The minimum value, 5 people per square km, was reached in 1961 while the maximum of 27 people per square km was recorded in 2021.

Annual average number of people affected by cyclone events at district level in Somalia. This estimation is part of the results of the Cyclone Displacement Risk Profile - Addressing Drivers and Facilitating Safe, Orderly and Regular Migration in the Contexts of Disasters and Climate Change in the IGAD Region (ICPAC (2023), Flood Displacement Risk Profile (2023)) financed by the Migration Multi-Partner Trust Fund (MPTF)

This bar chart displays median age (year) by ISO 3 country code using the aggregation average, weighted by population in Somalia. The data is about countries per year.

Somalia SO: CPIA: Structural Policies Cluster Average: 1=Low To 6=High data was reported at 1.667 NA in 2017. Somalia SO: CPIA: Structural Policies Cluster Average: 1=Low To 6=High data is updated yearly, averaging 1.667 NA from Dec 2017 (Median) to 2017, with 1 observations. Somalia SO: CPIA: Structural Policies Cluster Average: 1=Low To 6=High 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: Policy and Institutions. The structural policies cluster includes trade, financial sector, and business regulatory environment.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Somalia SO: CPIA: Policies for Social Inclusion/Equity Cluster Average: 1=Low To 6=High data was reported at 2.000 NA in 2017. Somalia SO: CPIA: Policies for Social Inclusion/Equity Cluster Average: 1=Low To 6=High data is updated yearly, averaging 2.000 NA from Dec 2017 (Median) to 2017, with 1 observations. Somalia SO: CPIA: Policies for Social Inclusion/Equity Cluster Average: 1=Low To 6=High 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: Policy and Institutions. The policies for social inclusion and equity cluster includes gender equality, equity of public resource use, building human resources, social protection and labor, and policies and institutions for environmental sustainability.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Somalia: Livestock production index (2004-2006 = 100): The latest value from 2022 is 98.6 index points, a decline from 99.7 index points in 2021. In comparison, the world average is 112.3 index points, based on data from 188 countries. Historically, the average for Somalia from 1961 to 2022 is 86.5 index points. The minimum value, 47.8 index points, was reached in 1961 while the maximum of 110.2 index points was recorded in 2013.

This statistic displays the average daily number of calories consumed per capita in selected countries as of 2014. The average daily calorie intake per capita in the United States is 3,641 kilocalories (kcal) per person. The prevalence of obesity has increased in countries like the United States and the United Kingdom in the last decade, with the U.S. reporting the highest rates of obesity in all OECD countries. An increased daily caloric intake has been positively associated with this international problem.

Somalia SO: CPIA: Public Sector Management and Institutions Cluster Average: 1=Low To 6=High data was reported at 1.800 NA in 2017. Somalia SO: CPIA: Public Sector Management and Institutions Cluster Average: 1=Low To 6=High data is updated yearly, averaging 1.800 NA from Dec 2017 (Median) to 2017, with 1 observations. Somalia SO: CPIA: Public Sector Management and Institutions Cluster Average: 1=Low To 6=High 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: Policy and Institutions. The public sector management and institutions cluster includes property rights and rule-based governance, quality of budgetary and financial management, efficiency of revenue mobilization, quality of public administration, and transparency, accountability, and corruption in the public sector.; ; World Bank Group, CPIA database (http://www.worldbank.org/ida).; Unweighted average;

Somalia SO: External Debt: Average Grace Period on New External Debt Commitments data was reported at 0.000 Year in 2016. This stayed constant from the previous number of 0.000 Year for 2015. Somalia SO: External Debt: Average Grace Period on New External Debt Commitments data is updated yearly, averaging 0.000 Year from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 15.611 Year in 1970 and a record low of 0.000 Year in 2016. Somalia SO: External Debt: Average Grace Period on New External Debt Commitments 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: External Debt: Terms and Undisbursed Debt. Grace period is the period from the date of signature of the loan or the issue of the financial instrument to the first repayment of principal. To obtain the average, the grace periods for all public and publicly guaranteed loans have been weighted by the amounts of the loans. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity.; ; World Bank, International Debt Statistics.; Weighted average;

This horizontal bar chart displays median age (year) by capital city using the aggregation average, weighted by population in Somalia. The data is about countries per year.

Somalia: Percent of world population: The latest value from 2023 is 0.23 percent, an increase from 0.22 percent in 2022. In comparison, the world average is 0.51 percent, based on data from 196 countries. Historically, the average for Somalia from 1960 to 2023 is 0.14 percent. The minimum value, 0.1 percent, was reached in 1960 while the maximum of 0.23 percent was recorded in 2023.

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.