Abstract

It is increasingly recognised that the majority of the world's refugees reside not in camps, but dispersed amongst the community in the countries where they have sought asylum. This is the case for Syrian refugees in Jordan, of which 84% live outside official refugee camps in urban and rural areas across the country. Understanding the needs, vulnerabilities and capacities of this dispersed refugee population is vital to ensuring their protection and access to services. The purpose of this dataset is to examine the situation of Syrian refugees living outside camps in Jordan, based on data collected through UNHCR's Home Visits programme. Under this programme, interviews are conducted with every refugee household registering with UNHCR outside camps. This provides an unparalleled source of information about the situation of Syrian refugees in non-camp settings.

Geographic coverage

Urban areas at National Coverage

Analysis unit

Household and individual

Kind of data

Sample survey data [ssd]

Sampling procedure

This study is not based on random sampling. The Home Visits survey is an ongoing assessment which aims to interview every refugee household registered with UNHCR outside camps. The survey went through many different versions which included major changes in variables and sections content, for this reason it is not possible to fully compare the different versions with each other. Whenever possible, though, the various dataset versions have been harmonized so that variables containing the same kind of information were renamed with the same name. You can see all the versions available in the Microdata Library in the “Related studies” tab.

Mode of data collection

Face-to-face [f2f]

this graph was created in R:

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Hundreds of millions of people live in a country that is different from the one in which they were born. In some countries, the majority of the population are immigrants.

Migration has played a crucial role in economic development, education and mobility. The transfer of money from migrants working overseas to family or friends in their home country – remittances – can be an important source of income in many countries.

On this page you can find all our data and visualizations relating to migration.

The estimates of the number (or “stock”) of international migrants disaggregated by age, sex and country or area of origin are based on national statistics, in most cases obtained from population censuses. Additionally, population registers and nationally representative surveys provided information on the number and composition of international migrants.

The dataset presents estimates of international migrant by age, sex and origin. Estimates are presented for 1990, 1995, 2000, 2005, 2010, 2015 and 2020 and are available for 232 countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.

Abstract

It is increasingly recognised that the majority of the world's refugees reside not in camps, but dispersed amongst the community in the countries where they have sought asylum. This is the case for Syrian refugees in Jordan, of which 84% live outside official refugee camps in urban and rural areas across the country. Understanding the needs, vulnerabilities and capacities of this dispersed refugee population is vital to ensuring their protection and access to services. The purpose of this dataset is to examine the situation of Syrian refugees living outside camps in Jordan, based on data collected through UNHCR's Home Visits programme. Under this programme, interviews are conducted with every refugee household registering with UNHCR outside camps. This provides an unparalleled source of information about the situation of Syrian refugees in non-camp settings.

Geographic coverage

Urban areas at National Coverage

Analysis unit

Household and individual

Kind of data

Sample survey data [ssd]

Sampling procedure

This study is not based on random sampling. The Home Visits survey is an ongoing assessment which aims to interview every refugee household registered with UNHCR outside camps. The survey went through many different versions which included major changes in variables and sections content, for this reason it is not possible to fully compare the different versions with each other. Whenever possible, though, the various dataset versions have been harmonized so that variables containing the same kind of information were renamed with the same name. You can see all the versions available in the Microdata Library in the “Related studies” tab.

Mode of data collection

Face-to-face [f2f]

Objectives: Housing as a social determinant of health should provide not only shelter, but also a feeling of home. We explored psychosocial pathways creating a sense of home and influencing the relationship between housing and health among asylum seekers and refugees (ASR) in high-income countries.Methods: We performed a systematic review. To be included, studies had to be peer-reviewed, published between 1995 and 2022, and focus on housing and health of ASR in high-income countries. We conducted a narrative synthesis.Results: 32 studies met the inclusion criteria. The psychosocial attributes influencing health most often identified were control, followed by expressing status, satisfaction, and demand. Most attributes overlap with material/physical attributes and have an impact on ASR’s mental health. They are closely interconnected with each other.Conclusion: Psychosocial attributes of housing play an essential role in the health of ASR; they are closely associated with material/physical attributes. Therefore, future research on housing and health of ASR should routinely study psychosocial attributes, but always in association with physical ones. The connections between these attributes are complex and need to be further explored.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42021239495.

Europe recently experienced a large influx of refugees, spurring much public debate about the admission and integration of refugees and migrants into society. Previous research based on cross-sectional data found that European citizens generally favour asylum seekers with high employability, severe vulnerabilities, and Christians over Muslims. These preferences and attitudes were found to be homogeneous across countries and socio-demographic groups. Here, we do not study the general acceptance of asylum seekers, but the acceptance of refugee and migrant homes in citizens’ vicinity and how it changes over time. Based on a repeated stated choice experiment on preferences for refugee and migrant homes, we show that the initially promoted “welcome culture†towards refugees in Germany was not reflected in the views of a majority of a sample of German citizens who rather disapproved refugee homes in their vicinity. Their preferences have not changed between November 2015, the peak of “welcome...

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

The main purpose of the regional survey on abandoned housing and housing at risk of abandonment in Venezuela was to identify the quality of tenure, current situation of the abandoned properties as well as the access to HLP (housing, land and property) rights in host countries with the aim of understanding the magnitude of the impact on their rights to adequate housing. It also aimed to identify most affected population groups.

The survey aimed to characterize the dwellings abandoned by this population along with the most frequent circumstances and typologies of this abandonment. The survey also sought to characterize the housing situation faced by this population in the host countries using differential approaches.

Context

The Russian invasion of Ukraine started on February 24, 2022. Attacks by Russian forces were reported in major cities across Ukraine, including Berdyansk, Chernihiv, Kharkiv, Odesa, Sumy, and the capital Kyiv. Western officials claimed that by scope, the war could be the largest in Europe since 1945. The Office of the United Nations High Commissioner for Human Rights (OHCHR) verified over 5.7 thousand deaths of civilians in Ukraine during the war as of September 2022.

The invasion caused Europe's largest refugee crisis since World War II, with over 7.2 million Ukrainians fleeing the country and a third of the population displaced. The refugees of the war mostly fled to the neighboring countries of Ukraine located in Central and Eastern Europe, prominently the nations of Poland, Hungary, Romania, Slovakia, Belarus, Republic of Moldova and Russia as well. With the situation in the regions of Ukraine changing, it is important to keep a general record regarding where the refugees are located, to provide better assistance to them and the concerned authorities.

About the dataset

This dataset contains information about the number of Ukrainian refugees that a neighboring country is housing at different points in time, starting from early March. The countries that mostly feature in the data are obviously the ones mentioned before that share borders with the nation of Ukraine. Each record mentions the country, the date of recording, the number of refugees in that country, and geospatial data of the particular region which could help in some useful geographical analysis. The consecutive entries for one country seem to be not more than a week apart at any given time. United Nations High Commissioner for Refugees (UNHCR) and local governments are the main sources.

This file was extracted using an API about war data from RapidAPI. I will also provide regular updates to this dataset whenever I find any. I am still new to this technique of extraction so any feedback would be highly appreciated.

Inspiration

The war has inflicted large scale damage on many different communities and I believe the data science community has the knowledge and resources of providing help. I believe all data enthusiasts learn about data science to help in solving real world problems that society faces and providing aid during times of humanitarian crises would be influential work of the highest order.

Visit this link if you wish to donate or provide other support to the efforts in Ukraine: https://stand-with-ukraine.pp.ua/

If you were to leave your home country, how far would you go, and for what reason? Just over the nearest border? Across an ocean? Or to the other side of the world?

People often equate international migration with long journeys. But most migrants actually travel shorter distances, as you might expect if you put yourself into their situation.

Understanding migration patterns helps governments around the world plan for population and economic changes.

This article addresses a simple but important question: how far do international migrants usually move from their home countries?

But before we look at how far migrants travel, it’s useful to keep in mind that most people don’t move to a different country. 96% of the world’s population lives in the country where they were born. That means the people we’ll focus on here are a small fraction of the global population.

Two examples: Syria and Venezuela Syria and Venezuela are two recent examples of countries with large-scale emigration, but for very different reasons — one caused by war, the other by economic collapse and political instability.

Since the start of its civil war in 2011, Syria has become a well-known case of large-scale emigration. By 2020, nearly half (48%) of all Syrian-born people — about 8.5 million — had left the country.

While we don’t have precise data on how far each migrant traveled, we do have reliable estimates of the countries they moved to. This data is published by the United Nations Department of Economic and Social Affairs.

As you can see on the chart, most Syrian emigrants have stayed close to home. The chart below shows Turkey, Lebanon, and Saudi Arabia as the top destinations, with Turkey alone hosting nearly 40% of them. Overall, a large majority of Syrian emigrants — 80% — have remained within Asia.

this graph was created in unhcr :

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The war in Ukraine, which began with the Russian Federation's large-scale invasion in February 2022, has created Europe’s largest and fastest-growing displacement crisis since World War II. This conflict has forced millions of Ukrainians to flee their homes, either escaping into nearby countries or relocating within Ukraine itself. In response, there has been an impressive, collective effort from national and local authorities, civil society groups, volunteers, and even the refugees themselves to meet the needs of those fleeing violence, ensuring protection and essential services.

Now, nearly two years into the crisis, these refugee support efforts remain crucial in host countries and show signs of long-term support. Host governments are increasingly committed to integrating refugees into their communities, with a focus on socio-economic inclusion to help Ukrainians rebuild their lives.

While the largest wave of displacement happened in the initial months of the invasion, Ukrainians continue to move back and forth across borders. Some are still fleeing the ongoing conflict, while others make short visits to Ukraine or even return more permanently. This mobility creates a complex pattern of movement that governments and aid organizations track closely to adjust their support strategies.

In the third year of this crisis, host countries face the challenge of balancing short-term and longer-term refugee needs. To help, the United Nations High Commissioner for Refugees (UNHCR) has recommended that host countries remain flexible with refugees who visit Ukraine for short periods, encouraging states to allow them to retain their legal status abroad. For those staying in Ukraine longer, UNHCR suggests a temporary pause in refugee status rather than permanent withdrawal, so they can easily regain protection if they need to leave Ukraine again.

This approach helps refugees make informed choices about returning when the situation allows and reduces unnecessary administrative hurdles, providing a more supportive environment for Ukrainians as they face an uncertain future.

Context

Missing Migrants Project tracks deaths of migrants, including refugees and asylum-seekers, who have died or gone missing in the process of migration towards an international destination. Please note that these data represent minimum estimates, as many deaths during migration go unrecorded

What is included in Missing Migrants Project data?

Missing Migrants Project counts migrants who have died at the external borders of states, or in the process of migration towards an international destination, regardless of their legal status. The Project records only those migrants who die during their journey to a country different from their country of residence. Missing Migrants Project data include the deaths of migrants who die in transportation accidents, shipwrecks, violent attacks, or due to medical complications during their journeys. It also includes the number of corpses found at border crossings that are categorized as the bodies of migrants, on the basis of belongings and/or the characteristics of the death. For instance, a death of an unidentified person might be included if the decedent is found without any identifying documentation in an area known to be on a migration route. Deaths during migration may also be identified based on the cause of death, especially if is related to trafficking, smuggling, or means of travel such as on top of a train, in the back of a cargo truck, as a stowaway on a plane, in unseaworthy boats, or crossing a border fence. While the location and cause of death can provide strong evidence that an unidentified decedent should be included in Missing Migrants Project data, this should always be evaluated in conjunction with migration history and trends.

What is excluded?

The count excludes deaths that occur in immigration detention facilities or after deportation to a migrant’s homeland, as well as deaths more loosely connected with migrants´ irregular status, such as those resulting from labour exploitation. Migrants who die or go missing after they are established in a new home are also not included in the data, so deaths in refugee camps or housing are excluded. The deaths of internally displaced persons who die within their country of origin are also excluded. There remains a significant gap in knowledge and data on such deaths. Data and knowledge of the risks and vulnerabilities faced by migrants in destination countries, including death, should not be neglected, but rather tracked as a distinct category.

What sources of information are used in the Missing Migrants Project database?

The Missing Migrants Project currently gathers information from diverse sources such as official records – including from coast guards and medical examiners – and other sources such as media reports, NGOs, and surveys and interviews of migrants. In the Mediterranean region, data are relayed from relevant national authorities to IOM field missions, who then share it with the Missing Migrants Project team. Data are also obtained by IOM and other organizations that receive survivors at landing points in Italy and Greece. IOM and UNHCR also regularly coordinate to validate data on missing migrants in the Mediterranean. Data on the United States/Mexico border are compiled based on data from U.S. county medical examiners, coroners, and sheriff’s offices, as well as media reports for deaths occurring on the Mexican side of the border. In Africa, data are obtained from media and NGOs, including the Regional Mixed Migration Secretariat and the International Red Cross/Red Crescent. The quality of the data source(s) for each incident is assessed through the ‘Source quality’ variable, which can be viewed in the data. Across the world, the Missing Migrants Project uses social and traditional media reports to find data, which are then verified by local IOM staff whenever possible. In all cases, new entries are checked against existing records to ensure that no deaths are double-counted. In all regions, Missing Migrants Project data represent a minimum estimate of the number of migrant deaths. To learn more about data sources, visit the thematic page on migrant deaths and disappearances in the Global Migration Data Portal.

Content

What are the variables used in the Missing Migrants Project database?

This section presents the list of variables that constitute the Missing Migrants Project database. While ideally, all incidents recorded would include entries for each of these variables, the challenges described above mean that this is not always possible. The minimum information necessary to register an incident is the date of the incident, the number of dead and/or the number of missing, and the location of death. If the information is unavailable, the cell is left blank or “unknown” is recorded, as indicated in below.

1. Web ID - An automaticall...

Global matrices of bilateral migrant stocks spanning the period 1960-2000, disaggregated by gender and based primarily on the foreign-born concept are presented. Over one thousand census and population register records are combined to construct decennial matrices corresponding to the last five completed census rounds. For the first time, a comprehensive picture of bilateral global migration over the last half of the twentieth century emerges. The data reveal that the global migrant stock increased from 92 to 165 million between 1960 and 2000. South-North migration is the fastest growing component of international migration in both absolute and relative terms. The United States remains the most important migrant destination in the world, home to one fifth of the world’s migrants and the top destination for migrants from no less than sixty sending countries. Migration to Western Europe remains largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East, North Africa and South and South-East Asia. Finally, although the global migrant stock is still predominantly male, the proportion of women increased noticeably between 1960 and 2000.

Context

In 2013 alone, international migrants sent $413 billion home to families and friends. This money is known as "remittance money", and the total is more than three times that afforded by total global foreign aid ($135 billion). Remittances are traditionally associated with poor migrants moving outside of their home country to find work, supporting their families back home on their foreign wages; as a result, they make up a significant part of the economic picture for many developing countries in the world.

This dataset, published by the World Bank, provides estimates of 2016 remittance movements between various countries. It also provides historical data on the flow of such money going back to 1970.

For a look at how remittances play into the global economy, watch "The hidden force in global economics: sending money home".

Content

This dataset contains three files:

• bilateral-remittance.csv --- Estimated remittances between world countries in the year 2016.
• remittance-inflow.csv --- Historical remittance money inflow into world countries since 1970. Typically high in developing nations.
• remittance-outflow.csv --- Historical remittance money outflow from world countries since 1970. Typically high in more developed nations.

All monetary values are in terms of millions of US dollars.

For more information on how this data was generated and calculated, refer to the World Bank Remittance Data FAQ.

Acknowledgements

This dataset is a republished version of three of the tables published by the World Bank which has been slightly cleaned up for use on Kaggle. For the original source, and other complimentary materials, check out the dataset home page.

Inspiration

• What is the historical trend in remittance inflows and outflows for various countries? How does this relate to the developmental character of the countries in question?
• What countries send to most money abroad? What countries receive the most money from abroad? Try combining this dataset with a demographics dataset to see what countries are most and least reliant on income from abroad.
• How far do workers migrate for a job? Are they staying near home, or going half the world away? Are there any surprising facts about who send money to who?

BackgroundThe number of people who have been displaced from their homes due to violence, conflict, and natural disasters. The displaced persons are vulnerable to PTSD; however, being women, individuals with lower socio-economic status and intense exposure to physical assault are more vulnerable. The reviews stated that the pooled prevalence of PTSD among refugees in high-income countries was higher than the general population. However, there has been no review done on PTSD among displaced persons in Africa. Therefore, the aim of this review was to summarise the most recent data evidence on the pooled prevalence of posttraumatic stress disorder and the pooled effect of associated factors on adult displaced people in Africa.MethodsWe used an appropriate guideline for systematic reviews and meta-analyses reports, which is the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This review protocol was registered in PROSPERO (CRD42023411371). The publications were identified from PubMed/Medline, EMBASE, the Cochrane Library, Scopus databases, and other grey searches of Google Scholar and World Health Organisation (WHO) reports. The data was extracted in Microsoft Excel, and then it will be imported into STATA 11.0 for analysis.ResultsWe have included 10 studies conducted in African countries with 5287 study participants. In this meta-analysis, the pooled prevalence of PTSD among displaced people in Africa was 55.64 (95% CI: 42.76–68.41%). Further, in subgroup analysis regarding the study participants, the pooled prevalence of PTSD among internally displaced people and refugees was 56.35% and 54.04%, respectively. Among the associated factors, being female, unemployed, and depression were significantly related to PTSD among displaced people.ConclusionsIn this review, the pooled prevalence of PTSD among displaced people in Africa was high. Demographic characteristics (female, single, and unemployed), substance use disorder, and depression were risk factors for PTSD among displaced people. This finding might help the stakeholders (mental health policy makers, administrators, and mental health professionals) to address the prevention, early screening, and management of PTSD among displaced people and to give attention to more vulnerable bodies.Systematic review registrationPROSPERO, identifier CRD42023411371.

Debates about migration are often in the news. People quote numbers about how many people are entering and leaving different countries. Governments need to plan and manage public resources based on how their own populations are changing.

Informed discussions and effective policymaking rely on good migration data. But how much do we really know about migration, and where do estimates come from?

In this article, I look at how countries and international agencies define different forms of migration, how they estimate the number of people moving in and out of countries, and how accurate these estimates are.

Migrants without legal status make up a small portion of the overall immigrant population. Most high-income countries and some middle-income ones have a solid understanding of how many immigrants live there. Tracking the exact flows of people moving in and out is trickier, but governments can reliably monitor long-term trends to understand the bigger picture.

Who is considered an international migrant? In the United Nations statistics, an international migrant is defined as “a person who moves to a country other than that of his or her usual residence for at least a year, so that the country of destination effectively becomes his or her new country of usual residence”.1

For example, an Argentinian person who spends nine months studying in the United States wouldn’t count as a long-term immigrant in the US. But an Argentinian person who moves to the US for two years would. Even if someone gains citizenship in their new country, they are still considered an immigrant in migration statistics.

The same applies in reverse for emigrants: someone leaving their home country for more than a year is considered a long-term emigrant for the country they’ve left. This does not change if they acquire citizenship in another country. Some national governments may have definitions that differ from the UN recommendations.

What about illegal migration? “Illegal migration” refers to the movement of people outside the legal rules for entering or leaving a country. There isn’t a single agreed-upon definition, but it generally involves people who breach immigration laws. Some refer to this as irregular or unauthorized migration.

There are three types of migrants who don’t have a legal immigration status. First, those who cross borders without the right legal permissions. Second, those who enter a country legally but stay after their visa or permission expires. Third, some migrants have legal permission to stay but work in violation of employment restrictions — for example, students who work more hours than their visa allows.

Tracking illegal migration is difficult. In regions with free movement, like the European Union, it’s particularly challenging. For example, someone could move from Germany to France, live there without registering, and go uncounted in official migration records.2 The rise of remote work has made it easier for people to live in different countries without registering as employees or taxpayers.

The HEED project aims at understanding energy needs of refugees and displaced populations to improve access to clean energy. The focus of HEED is on the lived experiences of refugees living for protracted periods of time in three refugee camps in Rwanda (Nyabiheke, Gihembe and Kigeme) and internally displaced persons (IDPs) forced to leave their homes as a result of the 2015 earthquake in Nepal. As part of the project, an energy assessment survey of households in both countries was undertaken using quantitative and qualitative research methods with households living in different parts of the camps/settlements, entrepreneurs running small businesses, and those responsible for community facilities, such as schools and health clinics. In the first phase, a questionnaire-based survey targeting displaced populations was conducted with households living in three refugee camps in Rwanda and four displaced sites in Nepal (see tables 2.1 and 2.2 respectively). The second phase of the field research involved a series of interviews and focus group discussions with various stakeholders in Nepal and Rwanda. The surveys were designed and delivered between March and April 2018 by the project partner, Practical Action. In both countries, the enumerators for the survey received a two-day training on research methods, data collection and ethics.

With regards to the household survey, the sample size was derived using Cochran’s formula as described by Bartlett et. al. in Organizational Research: Determining Appropriate Sample Size in Survey Research. A minimum sample size of 119 households was derived by applying a margin of error of 0.03 and an alpha of 0.5. A breakdown of the focal group and specific sites where the surveys were delivered in Rwanda and Nepal is shown in tables 2.1 and 2.2 respectively. In Rwanda, a total of 814 surveys including 622 households, 155 enterprises and 37 community facilities from across three sites were conducted. The sample distribution across camp shows 211 for Gihembe, 202 for Kigeme and 209 for Nyabiheke. In Gihembe more than half of the respondents (118, 55.9%) sampled were females with the remaining 93 (44.1%) being males. This is in contrast with Kigeme where almost equal numbers of both male (100, 49.5%) and females (102, 50.5%) were sampled. In Nyabiheke the sample covered more females (123, 58.9%) than males (86, 41.1%). In Nepal, the sample covered 181 households, 18 enterprises and 3 community facilities (see table 2.2). The household sample in Nepal covered more males (126, 69.6%) than females (55, 30.4%).

Folder Structure: Surveys:

Gihembe Community Facility Survey – Gihembe_CF.csv Gihembe Enterprise Survey – Gihembe_EN.csv Gihembe Household Survey – Gihembe_HH.csv

Kigeme Community Facility Survey – Kigeme_CF.csv Kigeme Enterprise Survey – Kigeme_EN.csv Kigeme Household Survey – Kigeme_HH.csv

Nepal Community Facility Survey – Nepal_CF.csv Nepal Enterprise Survey – Nepal_EN.csv Nepal Household Survey – Nepal_HH.csv

Nyabiheke Community Facility Survey - Nyabiheke_CF.csv Nyabiheke Enterprise Survey – Nyabiheke_EN.csv Nyabiheke Household Survey – Nyabiheke_HH.csv

Location Maps:

Gihembe Community Facility Survey Map – CF_GIS_gihembe.csv Gihembe Enterprise Survey Map – EN_GIS_gihembe.csv Gihembe Household Survey Map – HH_GIS_gihembe.csv

Kigeme Community Facility Survey Map – CF_GIS_kigeme.csv Kigeme Enterprise Survey Map – EN_GIS_kigeme.csv Kigeme Household Survey Map – HH_GIS_kigeme.csv

Nepal Community Facility Survey Map – CF_GIS_nepal.csv Nepal Enterprise Survey Map – EN_GIS_nepal.csv Nepal Household Survey Map – HH_GIS_nepal.csv

Nyabiheke Community Facility Survey Map - CF_GIS_nyabiheke.csv Nyabiheke Enterprise Survey Map – EN_GIS_nyabiheke.csv Nyabiheke Household Survey Map – HH_GIS_nyabiheke.csv

The following information was gathered from each of the surveys:

Households: The datasets contain information about household demographics, access to and use of electricity and lighting technologies, access to and use of cooking technologies and fuels, self-reported needs and priorities by the household, and ownership of energy products. Several key areas, such as solar lighting products and issues around fuel usage, are covered in more detail.

Enterprises: The datasets contain information about the enterprise, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances.

Community facility: The datasets contain information about the community facility or institution, their electrical and non-electrical lighting needs and supply, the usage of energy for ICT and entertainment, motive power, heating, and cooling applications, and their ownership of electrical appliances. Community facilities offered healthcare services were presented additional questions about specific medical devices.

The survey results together with other methodological tools including field visits, workshops - ‘Design for Displacement (D4D)’ and ‘Energy for End-Users’ (E4E) workshops have provided relevant data and contextual knowledge to inform the design of the various interventions associated with the HEED. The data sets and results have been compiled, organised and uploaded in the data portal for use by researchers, students and all both within and outside of the project consortium, during and beyond the project lifetime.

BackgroundDifferent acculturation strategies might be related to different mental health outcomes and social participation of unaccompanied refugee minors (URMs), but little is known about which factors influence this acculturation process. Therefore, the aim of this investigation was to examine the impact of individual, stress-related, and contextual factors on the acculturation process of URMs in Germany.MethodsA sample of N = 132 URMs living in child and youth welfare service facilities in Germany completed questionnaires about their acculturation orientation, traumatic experiences, daily stressors, asylum stress, and perceived social support between June 2020 and October 2021. This investigation is part of the multi-center randomized control trial BETTER CARE. Data were analyzed descriptively and via multiple hierarchical regression.ResultsIntegration (43.5%) and Assimilation (37.1%) were the most common acculturation strategies used by URMs. Multiple hierarchical regression models showed that daily stressors (e.g., the lack of money) were associated with a stronger orientation toward the home country, whereas traumatic events were associated with a weaker orientation toward their home country. No significant predictors were found for the orientation toward the host country.DiscussionOverall, URMs in Germany showed favorable acculturation strategies. Nevertheless, daily stressors and traumatic experiences might influence this process. The implications for practitioners and policymakers are discussed with a view to further improving the acculturation process of URMs in Germany.Clinical Trial Registration: German Clinical Trials Register, DRKS00017453 https://drks.de/search/de/trial/DRKS00017453. Registered on December 11, 2019.

Allgemeine politische Einstellungen und Meinungen zu aktuellen politischen Themen. Politische Aufgaben. Themen: Wichtigstes Problem in Deutschland; Parteisympathie; Politikinteresse; Angehen der langfristigen Probleme in Deutschland: Bewertung der Intensität der Aktivitäten der Bundesregierung aus CDU/CSU und SPD in Bezug auf langfristige Probleme; Ausgestaltung des Sozialsystems: Präferenz für hohe Steuern und umfangreiche Sozialleistungen vs. niedrige Steuern und geringe Sozialleistungen; Präferenz für Ausbau oder Verringerung von Sozialleistungen in Deutschland durch die Bundesregierung. Politische Aufgaben: Wichtigkeit von Verbesserungen in ausgewählten Politikfeldern (z.B. Eingliederung von Langzeitarbeitslosen und Ausländern, Vereinbarkeit von Familie und Beruf, Klimaschutz, etc.) und Bewertung der Fortschritte der Bundesregierung in diesen Bereichen; Flüchtlinge: Unterbringung von Flüchtlingen bzw. Asylbewerbern in der Wohngegend; eher positive oder eher negative Erfahrungen mit Flüchtlingen bzw. Asylbewerbern in der Wohngegend und in Deutschland; Einstellung zu Flüchtlingen in Deutschland (Anstieg der Kriminalität durch Flüchtlinge, Bedarf an qualifizierten Arbeitskräften besser zu decken, kulturelle Bereicherung, erwartete Rückkehr der meisten Flüchtlinge in ihr Heimatland, Deutschland kann sich die Flüchtlinge finanziell nicht leisten, gute Unterbringung und Versorgung, helfen gegen die Überalterung der Bevölkerung im Land). Demographie: Alter; Geschlecht; Familienstand; Zusammenleben mit einem Partner; Schulabschluss bzw. angestrebter Schulabschluss; Hochschulabschluss; Ausbildungsabschluss; Berufstätigkeit; Sicherheit des Arbeitsplatzes; berufliche Stellung; Haushaltsgröße; Anzahl Personen im Haushalt ab 18 Jahren; Gewerkschaftsmitglied im Haushalt; Konfession; Kirchgangshäufigkeit; Anzahl der Telefonnummern im Haushalt. Zusätzlich verkodet wurde: Befragten ID; Bundesland der Wahlberechtigung; Bezirk Berlin früher West/Ost; Ortsgröße; Gewichtungsfaktor. General political attitudes and opinions on current political issues. Political tasks. Topics: main problem in Germany; party sympathy; interest in politics; addressing the long-term problems in Germany: assessment of the intensity of the activities of the federal government related on long-term problems; design of the social system: preference for high taxes and extensive social benefits vs. low taxes and low social benefits; preference for the expansion or reduction of social benefits in Germany by the Federal Government. Political tasks: Importance of improvements in selected policy areas (including integration of long-term unemployed and foreigners, reconciliation of family and occupation, climate protection, etc.) and assessment of the progress made by the Federal Government in these areas. Refugees: accommodation of refugees or asylum seekers in the residential area; rather positive or rather negative experiences with refugees or asylum seekers in the residential area and in Germany; attitude towards refugees in Germany (increase in criminality by refugees, the demand for skilled workers is better to cover, cultural enrichment, expected return of most refugees to their home country, Germany cannot afford the refugees financially, good accommodation and care, help against the aging of the population). Demography: age; sex; marital status; living together with a partner; school leaving certificate or aspired school leaving certificate; graduate degree; graduation; employment; safety of job; occupational position; household size; number of persons aged 18 and over; union member in the household; denomination; church attendance; number of telephone numbers in the household. Additionally coded was: respondent ID; state of electoral authority; district of Berlin formerly west / east; city size; weighting factor. Telefonisches Interview: CATI (Computerunterstützte telefonische Befragung) Telephone Interview: CATI (Computer Assisted Telephone Interview)

Allgemeine politische Einstellungen. Meinung zu aktuellen politischen Themen. Politische Aufgaben. Themen: Wichtigstes Problem in Deutschland; Parteisympathie; Politikinteresse; allgemeine Entwicklung im Land in die richtige Richtung; Bewertung der Intensität der Aktivitäten der Bundesregierung in Bezug auf langfristige Probleme; politische Aufgaben: Wichtigkeit von Verbesserungen in ausgewählten Politikfeldern (z.B. Eingliederung von Langzeitarbeitslosen und Ausländern, Vereinbarkeit von Familie und Beruf, Klimaschutz, etc.) und Bewertung der Fortschritte der Bundesregierung in diesen Bereichen; Flüchtlinge bzw. Asylbewerber in der Wohngegend; eher positive, eher negative oder gar keine Erfahrungen mit Flüchtlingen in der Wohngegend und in Deutschland; Einstellung zu Flüchtlingen in Deutschland (Anstieg der Kriminalität durch Flüchtlinge, Bedarf an qualifizierten Arbeitskräften besser zu decken, kulturelle Bereicherung, erwartete Rückkehr der meisten Flüchtlinge in ihr Heimatland, Deutschland kann sich die Flüchtlinge finanziell nicht leisten, gute Unterbringung und Versorgung, helfen gegen die Überalterung der Bevölkerung im Land). Demographie: Alter (kategorisiert); Geschlecht; Familienstand; Zusammenleben mit einem Partner; höchster Schulabschluss bzw. angestrebter Schulabschluss; Hochschulabschluss; Ausbildungsabschluss; Berufstätigkeit; Sicherheit des Arbeitsplatzes; berufliche Stellung; Haushaltsgröße; Anzahl Personen im Haushalt ab 18 Jahren; Gewerkschaftsmitglied im Haushalt; Konfession; Kirchgangshäufigkeit; Anzahl der Telefonnummern im Haushalt. Zusätzlich verkodet wurde: Befragten-ID; Bundesland der Wahlberechtigung; Bezirkszuordnung Berlin West/Ost; Ortsgröße; Gewichtungsfaktor. General political attitudes. Opinion on current political issues. Political tasks. Topics: the main problem in Germany; party sympathy; interest in politics; general development in the country in the right direction; average intensity of the activities of the federal government in terms of long-term problems; political tasks: importance of improvements in selected policy areas and assessment of the progress of the federal government in these areas; importance of improvements in digitization and evaluation of the progress of the federal government in these area; refugees or asylum seekers in the residential area; rather positive, rather negative or no experience with refugees in the residential area and in Germany; attitude towards refugees in Germany (increase in crime, better demand for qualified workers, cultural enrichment, expected return of most refugees to their home country, Germany financially cannot afford the refugees, good accommodation and care, help against ageing population). Demography: age (categorized); sex; marital status; living together with a partner; highest educational degree or aspired degree; academic degree; graduation; employment status; job security; occupational position; household size; number of persons in the household aged 18 and over; union member in the household; confession; frequency of church attendance; number of phone numbers in the household. Also encoded was: respondent-ID; federal state; district assignment Berlin West/East; city size; weighting factor. Telefonisches Interview: CATI (Computerunterstützte telefonische Befragung) Telephone interview: CATI (Computer Assisted Telephone Interview) Wahlberechtigte Bevölkerung ab 18 Jahren