Abstract

The North East Zone Multiple Indicator Cluster Survey (MICS) is a household survey programme conducted in 2011 by the Puntland State of Somalia Ministry Planning and International Cooperation with technical and financial support from UNICEF.

MICS was conducted as part of the fourth global round of MICS surveys (MICS4). It provides up-to-date information on the situation of children and women and measures key indicators that allow countries to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

The Northeast Zone Multiple Indicator Survey is a representative sample survey of 4,954 households, out of which 4,785 were successfully interviewed including 5,492 women age 15 – 49 years and 4,714 mothers and caretakers of children less than five years old. The primary purpose of MICS is to provide policy makers and planners with reliable and detailed information needed to monitor the situation of women and children. Information on nutrition, child health, water and sanitation, reproductive health, child development, literacy and education, child protection, HIV/AIDS and orphan hood and access to mass media and use of information/communication technology is included.

Geographic coverage

Northeast Zone

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Northeast Zone Multiple Indicator Cluster Survey was to produce statistically reliable estimates of most indicators for the whole Northeast Zone, for urban and rural areas, and for the three regions (Bari, Nugal and Mudug) of the Zone. There were two main sampling strata: urban and rural areas.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

The target sample size for the Northeast Zone MICS was calculated as 5,179 households. For the calculation of the sample size, the key indicator used was the polio immunization coverage for children aged 12 – 23 months.

The sampling frame was the list of settlements obtained from the 2005/2006 UNDP settlement census and which was updated in preparation for the Somalia population estimation survey. For each settlement, this list contained an estimated number of households and the classification by urban and rural.

Stratification consisted of separating urban and rural settlements within each region. Settlements were then used as primary sampling units and were selected with probability proportional to size, the size being the estimated number of households. Very large settlements were selected with certainty as selfrepresenting units (that is with probability equal to 1).

In rural areas and small towns, settlements with more than 200 households were divided into segments of which one was randomly selected. All households in the selected segment were listed to create a frame for the selection of 18 households at the second stage using systematic sampling.

For very large settlements, the list of villages and sections that comprised each settlement served as frame for the second stage selection (secondary sampling units). Each selected village and section was segmented if it contained more 200 households. One of the newly created segments was then randomly selected and all of the households it contained were listed. In the final stage, 18 households were selected from the household listing. In villages and sections containing 200 households or less, a complete household listing was carried out and 18 households were directly selected from the list of households.

The sampling procedures are more fully described in "Multiple Indicator Cluster Survey 2011 - Final Report" pp.123-124.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes Household Listing Form, Education, Non Formal Education, Water and Sanitation, Household Characteristics, Insecticide Treated Nets, Indoor Residual Spraying, Child Labour, Child Discipline and Handwashing.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes Women's Background, Access to Mass Media and Use of Information/Communication Technology, Child Mortality with Birth History, Desire for Last Birth, Maternal and Newborn Health, Post-natal Health Checks, Illness Symptoms, Contraception, Unmet Need, Female Genital Mutilation/Cutting, Attitudes Towards Domestic Violence, Marriage/Union, and HIV/AIDS.

The children's questionnaire includes Child's age, Early childhood development, Breastfeeding, Care of illness, Malaria and Immunization.

The questionnaires are based on the MICS4 model questionnaire. From the MICS4 model English version, the questionnaires were translated into Somali and were pre-tested in Gabilely, Hargeisa during February 2011. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires. In addition to the administration of questionnaires, fieldwork teams observed the place for hand washing.

The following modules were removed from the three sets of questionnaires each for the given reason. In the household questionnaire; - Salt iodisation module was removed because there is more recent data from the Micronutrient Survey of 2009.

In the questionnaire for women 15- 49 years; - Sexual behaviour module was not included as it was considered culturally sensitive in Somalia. Furthermore, it was not included in the 2006 MICS

In the questionnaire for children under five years; - Birth registration was omitted based on observations in MICS3 that there are very few births registered in Somaliland as most women gave birth at home. - The anthropometry module was excluded as there was more recent data in the micronutrient survey of 2009.

The following additions were made to the modules for specific questionnaires; In the questionnaire for children under five years - In the immunisation module treatment of diarrhoea using ORS distributed in the most recent Child Health Days i.e. December 2010 was added - In the same module the type of card in which child immunisation was recorded included additional type of cards from the 2009 and 2010 child health days.

In the household questionnaire - The Non Formal Education module was added. It was considered necessary to provide information for the continued intervention and support for Non Formal Education by the government and partners.

Cleaning operations

Data were entered using the CSPro software. The data were entered on 12 computers and carried out by 12 data entry operators and one data entry supervisor and one data manager. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS4 programme and adapted to the Northeast Zone questionnaire were used throughout. Data entry began in Garowe at Puntland State University (PSU) two weeks into data collection in April 2011 but was stopped in June 2011 due to technical and logistical challenges – the university uses a generator which kept on break down and affecting data entry and some clerks were caught trying to shorten the time taken in entering data by skipping sections of the questionnaire. Following consultations between UNICEF country office, the Ministry of Planning and International Cooperation in the Northeast Zone, it was decided to ship all the questionnaires to Nairobi and have data re-entered by a new set of data entry clerks. This second round of data entry started in September 2011 and was completed in January 2012. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, Version 18, and the model syntax and tabulation plans developed by UNICEF were used for this purpose.

Response rate

Of the 4,954 households selected for the sample, excluding the households in the 13 clusters that were not surveyed, 4,904 were found to be occupied. Of these, 4,785 were successfully interviewed for a household response rate of 97.6 percent. In the interviewed households, 5,839 women (age 15-49 years) were identified. Of these, 5,492 were successfully interviewed, yielding a response rate of 94.1 percent within interviewed households. There were 4,827 children under age five listed in the household questionnaire. Questionnaires were completed for 4,714 of these children, which corresponds to a response rate of 97.7 percent within interviewed households. Overall response rates of 91.8 and 95.3 are calculated for the women’s and under-5’s interviews respectively.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each

This data set includes a range of humanitarian indicators for Somalia at the admin 1, region, level. These indicators were digitized from a range of publicly available reports for the purpose of providing data amidst a growing humanitarian crisis across Africa and in Yemen. These indicators include data concerning people in need (PIN), food security levels (IPC 2-4), internally displaced people (IDP), rates of malnutrition (SAM), cholera cases, and PIN reached and partner organizations. See field descriptions for specific source and date.Featured in Somalia in Crisis.Fields:region_id: Region ID, numeral-only two digit identifier per region (admin 1). Used for constructing P-Code.state_pcod: State PCode, two character ISO country code "SO" for Somalia.region_pcod: Region PCode, alphanumeric identifier to uniquely identify each region.state: State Name, "Somalia."region: Region Name, Somalian name for each region.region_alt: Alternative Region Name, if variation in spelling is commonly used, or English version of name (eg. "upper," "lower").totpop14: Total Population 2014, UNFPA population estimate.pin_tot: PIN Total, UNOCHA total estimated number of People in Need, March 2017.fs_tot: Food Security Total, total PIN estimated food insecure at IPC levels 2, 3, and 4. UNOCHA, June 2016.ipc2: IPC Phase 2, total PIN estimated food insecure "Stressed" level. UNOCHA, June 2016.ipc3: IPC Phase 3, total PIN estimated food insecure "Crisis" level. UNOCHA, June 2016.ipc4: IPC Phase 4, total PIN estimated food insecure "Emergency" level. UNOCHA, June 2016.idp_tot_16: IDP Population 2016, UNFPA internally displaced persons estimate, June 2016.idp_arrive: IDP Arrivals, newly displaced people arriving between November 2016 and March 2017. UNHCR.idp_depart: IDP Departures, displaced people newly departed between November 2016 and March 2017. UNHCR.sam_rate: SAM Rate, UNICEF rates of Severe Acute Malnutrition in children under age 5. February 2017.awdcase17: Cholera Cases 3-17, reported cases of Acute Watery Diarrhea/cholera as of March 2017. WHO.awddeath17: Cholera Deaths 3-17, reported deaths due to Acute Watery Diarrhea/cholera as of March 2017. WHO.pinreach17: PIN Reach 3-17, number of people in need reached by humanitarian intervention. UNOCHA, March 2017.partnerorg: Partner Organizations, number of humanitarian partner organizations delivering assistance per region. UNOCHA, March 2017.

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.

UNICEF's country profile for Somalia, including under-five mortality rates, child health, education and sanitation data.

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

UHouseholds (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

TThe 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 target sample size for the Somali MICS was calculated as 6000 households. Within each zone a predetermined number of clusters were selected. In the North East and North West Zones 60 clusters were selected in each2. In the Central South Zone 130 clusters were selected making a total of 250 clusters with 24 households in each cluster. Within each region of each zone districts were selected using probability proportional to size (pps); in total 57 districts, out of 114 districts in Somalia were selected. The number of clusters in each district was also allocated according to estimated population size of district.The proportion of urban to non-urban clusters was determined according to the estimated populations falling within each category within each district. The non-urban population includes both the settled population in rural areas as well as the nomadic population. Within the selected districts permanent and temporary settlements were randomly selected also using probability proportional to size sampling3. In order to ensure than nomads were included in the sample, efforts were made to include temporary settlements near to known water points where nomads would most likely to be found. The third stage of sampling then involved the selection of the cluster(s) within the settlements. For settlements over the estimated size of 150 households some form of segmentation was necessary. Sketch maps were prepared to divide the settlements into roughly equal sizes of estimated households. Each segment was considered as an enumeration area making it possible to randomly select the required number of clusters. Once the final clusters had been identified, households were selected randomly using a modified expanded programme for immunisation (EPI) method. The sample was stratified by urban and non-urban and is not self-weighting. For reporting national level results, sample weights are used.

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

Three sets of questionnaires were used in the survey: 1) a household questionnaire which was used to collect information on all de jure household members, the household, and the dwelling; 2) a women's questionnaire administered in each household to all women aged 15-49 years; and 3) an under-5 questionnaire normally administered to mothers of under-5 children; in cases when the mother was not listed in the household roster, a primary caretaker for the child was identified and interviewed. Each questionnaire comprised several modules: The Household Questionnaire included the following: Household listingo Educationo Water and Sanitationo Household characteristicso Child Labouro Insecticide Treated Netso Maternal Mortalityo Salt Iodizationo The Questionnaire for Individual Women included the following: Child Mortalityo Birth Historyo Tetanus Toxoido Maternal and Newborn Healtho Marriage/Uniono Contraceptiono Female Genital Mutilationo HIV/AIDSo The Questionnaire for Children Under Five included the following: Birth Registration and Early Learningo Vitamin Ao Breastfeedingo Care of Illnesso Malariao Immunizationo Anthropometryo The questionnaires are based on the MICS model questionnaire4 with some additional questions included to reflect PAPFAM's interests as well as some country specific questions. From the MICS English version, the questionnaires were translated into Somali and were pre-tested in urban and rural areas in each zone during June and July 2006, efforts were made to ensure that nomadic households were included in the pre-testing. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires. A copy of the Somali MICS questionnaires is provided in Appendix F.

Cleaning operations

Multiple Indicator Cluster Survey daData editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files ta had been editied by field supervisors in the collection stage, then subsequently

Response rate

Of the 6000 households selected for the sample 5969 were successfully interviewed for a household response rate of 99.5 percent. In the interviewed households, 7277 women (age 15-49) were identified. Of these, 6764 were successfully interviewed, yielding a response rate of 93 percent. In addition, 6373 children under age five were listed in the household questionnaire. Of these, questionnaires were completed for 6305 which corresponds to a response rate of 98.9 percent. Overall response rates of 92.5 percent and 98.4 are calculated for the women's and under-5's interviews respectively (Table HH.1).

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors can be evaluated statistically. The sample of respondents to the 2006 MICS is only one of many possible samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differe somewhat from the results of the actual

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

BackgroundFor more than a decade, most parts of Somalia have not been under the control of any type of government. This “failure of state” is complete in the central and southern regions and most apparent in Mogadishu, which had been for a long period in the hands of warlords deploying their private militias in a battle for resources. In contrast, the northern part of Somalia has had relatively stable control under regional administrations, which are, however, not internationally recognized. The present study provides information about drug abuse among active security personnel and militia with an emphasis on regional differences in relation to the lack of central governmental control—to our knowledge the first account on this topic. Methods and FindingsTrained local interviewers conducted a total of 8,723 interviews of armed personnel in seven convenience samples in different regions of Somalia; 587 (6.3%) respondents discontinued the interview and 12 (0.001%) were excluded for other reasons. We assessed basic sociodemographic information, self-reported khat use, and how respondents perceived the use of khat, cannabis (which includes both hashish and marijuana), psychoactive tablets (e.g., benzodiazepines), alcohol, solvents, and hemp seeds in their units. The cautious interpretation of our data suggest that sociodemographic characteristics and drug use among military personnel differ substantially between northern and southern/central Somalia. In total, 36.4% (99% confidence interval [CI] 19.3%–57.7%) of respondents reported khat use in the week before the interview, whereas in some regions of southern/central Somalia khat use, especially excessive use, was reported more frequently. Self-reported khat use differed substantially from the perceived use in units. According to the perception of respondents, the most frequent form of drug use is khat chewing (on average, 70.1% in previous week, 99% CI 63.6%–76.5%), followed by smoking cannabis (10.7%, 99% CI 0%–30.4%), ingesting psychoactive tablets (8.5%, 99% CI 0%–24.4%), drinking alcohol (5.3%, 99% CI 0%–13.8%), inhaling solvents (1.8%, 99% CI 0%–5.1%), and eating hemp seeds (0.6%, 99% CI 0%–2.0%). Perceived use of khat differs little between northern and southern Somalia, but perceived use of other drugs reaches alarmingly high levels in some regions of the south, especially related to smoking cannabis and using psychoactive tablets. ConclusionsOur data suggest that drug use has quantitatively and qualitatively changed over the course of conflicts in southern Somalia, as current patterns are in contrast to traditional use. Although future studies using random sampling methods need to confirm our results, we hypothesize that drug-related problems of armed staff and other vulnerable groups in southern Somalia has reached proportions formerly unknown to the country, especially as we believe that any biases in our data would lead to an underestimation of actual drug use. We recommend that future disarmament, demobilization, and reintegration (DDR) programs need to be prepared to deal with significant drug-related problems in Somalia.

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

Project Overview Pain is a major public health problem in the Global South, particularly among marginalized communities, such as Somali pastoralists. Yet, the topic of chronic pain has not yet been comprehensively studied in Sub-Saharan Africa, specifically in the Somali region of Ethiopia, formally known as Somali Regional State (SRS). Therefore, this study aims to explore the perceptions and notions of chronic pain among Somali pastoralists in this context. Data and Data Collection Overview This study used an explorative qualitative design and was conducted between July 2020 and January 2021. We performed semi-structured, face-to-face interviews with 20 purposively selected female and male Somali pastoralists with chronic pain. The study took place in primary, secondary and tertiary care facilities in the SRS, as well as in pastoralist communities. We selected the study sites based on considerations of regional diversity and rural/urban differences. In doing so, we intended to gain insights into heterogeneous perceptions of chronic pain. In addition, we were interested in differences in pain severity across study sites, organizational features and care. For instance, we anticipated that we might speak to patients with more severe pain conditions in the hospital setting. We selected semi-structured interviews for data collection to explore sociocultural aspects of pain perception, in addition to corresponding experiential and existential domains. The interview language was Somali. Interviews lasted from 20 to 36 minutes. Three members of the research team (listed as co-authors on this data deposit) conducted the interviews, including Eleonore Baum (data collector), Sied Abdi (data collector & translator) and Roda Arab Abdilahi (data collector & translator), who are fluent in Somali and familiar with the Somali pastoralist culture. For cultural reasons and due to experiences during preliminary work, female researchers conducted the interviews with female pastoralists. Interviews in the health centers took place in a quiet location outside the building. In secondary and tertiary care hospitals, interviews took place at the patient’s bedside. Within pastoralist communities, we interviewed the participants in front of their residences. After each interview, the research team discussed first impressions and noted them in a research diary. We audio-recorded the interviews, transcribed them word for word and removed identifying elements. A member of the research team fluent in both English and Somali translated the transcripts into English. Data Analysis To systematically analyze the data on a case by code basis, we applied the Framework Method according to Gale et al. (2013). This matrix-based analytic method facilitates rigorous and transparent data management. It allowed us to perform all stages of the analysis in a systematic manner. The Framework Method permitted us to move back and forth between different levels of abstraction without losing sight of the original data. Three researchers experienced in qualitative research were involved in the data analysis. We used MAXQDA 2022 software for data analysis support. Substantively, we explained patterns drawing on the enactive approach to pain proposed by Stilwell and Harman (2019). Shared Data Organization Data files include twenty de-identified excerpted interviews made shareable for secondary research only under controlled access. This abstracted presentation of the data is the form approved for sharing by the participants. Documentation files include the informed consent form and interview guide used in the study; an inventory of the participants; this data narrative and an administrative README file. References Gale, N. K., Heath, G., Cameron, E., Rashid, S., & Redwood, S. (2013) “Using the framework method for the analysis of qualitative data in multi-disciplinary health research.” BMC Medical Research Methodology (13): 117. https://doi.org/10.1186/1471-2288-13-117 Stilwell, P., Harman, K. (2019) “An enactive approach to pain: beyond the biopsychosocial model.” Phenomenology and Cognitive Science (18): 637–665. https://doi.org/10.1007/s11097-019-09624-7

Abstract (en): In this special topic poll, respondents were queried regarding United States involvement in Somalia and the former Yugoslavia. Those surveyed were asked if the United States should be sending troops to Somalia to insure that food shipments got through to the people, whether food would get to the Somalians without United States military involvement, whether troops should stay in Somalia only as long as it took to set up supply lines, and whether sending troops to Somalia was worth the possible cost of American lives. Respondents were also asked if they favored the United States using its military forces to keep Serbia from violating the United Nations ban on Serbian flights over Bosnia, whether it was more important for the United States to be involved in Somalia or the former Yugoslavia, and whether helping the homeless and hungry in the United States was more important than trying to get food to the people in Somalia. Background information on respondents includes age, race, education, family income, service in the United States armed forces or reserves, political orientation, and party preference. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. Adult population of the United States aged 18 and over having telephones at home. A variation of random-digit dialing using primary sampling units (PSUs) was employed, consisting of blocks of 100 telephone numbers identical through the eighth digit and stratified by geographic region, area code, and size of place. Within households, respondents were selected using a method developed by Leslie Kish and modified by Charles Backstrom and Gerald Hursh (see Backstrom and Hursh, SURVEY RESEARCH [Evanston, IL: Northwestern University Press, 1963]). 2010-01-28 SAS, SPSS, and Stata setups have been added to this data collection. A weight variable has been included and must be used for any analysis.

Somalia administrative level 0-2 boundaries (COD-AB) dataset.Boundaries established: 1984 NOTE: In addition to administrative levels 0-2, this dataset includes ""level 1 Operational Zones"" (OPZ1) and ""level 2 Operational Zones"" (OPZ2). 17 OPZ1 features within the Banadir administrative level 1 feature are now (as of the January 2025 update) coincident with individual administrative level 2 features. Two further OPZ1 features within the Banadir administrative level 1 polygon are excluded from the administrative structure and must be named and P-coded as 'unspecified'. All Operational Zones are not administrative boundaries and are not identified as administrative level 3 features. The six Federal member states (Somaliland, Puntland, Galmudug, Hirshabelle, South West, and Jubaland) are NOT integrated into the administrative structure. (Their boundaries cannot be constructed even from administrative level 2 features.) This COD-AB was most recently reviewed for accuracy and necessary changes in December 2024. The COD-AB does not require any update. Sourced from OCHA Somalia Live geoservices (provided by Information Technology Outreach Services (ITOS) with funding from USAID) are available for this COD-AB. Please see COD_External. (For any earlier versions please see here, here, and here.) Vetting, configuration, and geoservices provision by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the Somalia COD-PS. An edge-matched (COD-EM) version of this COD-AB is available on HDX here. Please see the COD Portal. Administrative level 1 contains 18 feature(s). The normal administrative level 1 feature type is 'Region'. Administrative level 2 contains 91 feature(s). The normal administrative level 2 feature type is 'District'. Recommended cartographic projection: Africa Albers Equal Area Conic This metadata was last updated on January 23, 2025.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper Roughly 0.4947 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0029 and 0.3665 (in million kms), corressponding to 0.5881% and 74.094% respectively of the total road length in the dataset region. 0.1252 million km or 25.3179% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0 million km of information (corressponding to 0.0006% of total missing information on road surface) It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications. This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications. AI features: pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved." pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved). osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved." combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved." combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved." n_of_predictions_used: Number of predictions used for the feature length estimation. predicted_length: Predicted length based on the DL model’s estimations, in meters. DL_mean_timestamp: Mean timestamp of the predictions used, for comparison. OSM features may have these attributes(Learn what tags mean here): name: Name of the feature, if available in OSM. name:en: Name of the feature in English, if available in OSM. name:* (in local language): Name of the feature in the local official language, where available. highway: Road classification based on OSM tags (e.g., residential, motorway, footway). surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt). smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad). width: Width of the road, where available. lanes: Number of lanes on the road. oneway: Indicates if the road is one-way (yes or no). bridge: Specifies if the feature is a bridge (yes or no). layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels). source: Source of the data, indicating the origin or authority of specific attributes. Urban classification features may have these attributes: continent: The continent where the data point is located (e.g., Europe, Asia). country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States). urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban) urban_area: Name of the urban area or city where the data point is located. osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature. osm_type: Type of OSM element (e.g., node, way, relation). The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer. This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information. We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

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 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.

Larval density, adults emerged from larvae and breeding site indices for each breeding site container type in Kebridehar.

Germany has long been involved with international asylum applications, especially in recent years. The most applications came from Syrian asylum seekers. These was followed by applications from Afghanistan and Turkey. Germany as a refuge choice Statistics on the number of asylum applicants in Germany are recorded by the BAMF, or the Federal Office for Migration and Refugees (Bundesamt für Migration und Flüchtlinge). The coronavirus (COVID-19) pandemic has decreased refugee numbers in Europe, with travel bans across modes of transport and borders being closed. As the restrictions begin to lift, migration is beginning again. 2023 saw the highest number of applications for asylum since 2016. However, in 2024, numbers have decreased significanly again. Support for refugees remains a present issue, fraught with tension, legal complications and surrounded by constant debate with many believing that not enough is done to support them. Asylum decisions Not all asylum applications in Germany get accepted, due to various circumstances. Besides rejection, decisions regarding asylum may involve granting a legal status as a refugee, a grant of subsidiary protection or determining a deportation ban.