The United States hosted, by far, the highest number of immigrants in the world in 2020. That year, there were over 50 million people born outside of the States residing in the country. Germany and Saudi Arabia followed behind at around 16 and 13 million, respectively. There are varying reasons for people to emigrate from their country of origin, from poverty and unemployment to war and persecution. American Migration People migrate to the United States for a variety of reasons, from job and educational opportunities to family reunification. Overall, in 2021, most people that became legal residents of the United States did so for family reunification purposes, totaling 385,396 people that year. An additional 193,338 people became legal residents through employment opportunities. In terms of naturalized citizenship, 113,269 people from Mexico became naturalized American citizens in 2021, followed by people from India, the Philippines, Cuba, and China. German Migration Behind the United States, Germany also has a significant migrant population. Migration to Germany increased during the mid-2010's, in light of the Syrian Civil War and refugee crisis, and during the 2020’s, in light of conflict in Afghanistan and Ukraine. Moreover, as German society continues to age, there are less workers in the labor market. In a low-migration scenario, Germany will have 37.2 million skilled workers by 2040, compared to 39.1 million by 2040 in a high-migration scenario. In both scenarios, this is still a decrease from 43.5 skilled workers in 2020.

As of 2023, 27.3 percent of California's population were born in a country other than the United States. New Jersey, New York, Florida, and Nevada rounded out the top five states with the largest population of foreign born residents in that year. For the country as a whole, 14.3 percent of residents were foreign born.

The majority of immigrants moving to Sweden in 2023 were Swedes returning to Sweden. Nearly 10,600 Swedes returned to their home country in 2023. The remaining top five countries of origin were India, Poland, Germany, and Syria. In total, 95,000 people immigrated to Sweden in 2023.

Syrians largest immigrant group

Of Sweden's foreign-born population, Syrians made up the largest group. Following the outbreak of the Syrian Civil War in 2011, many people left the country in search of a better life in Europe, some of which landed in Sweden. In 2022, Sweden hosted the world's 7th largest group of Syrian refugees.

Immigration drives population increase in Sweden

Over the past decade, Sweden has seen a positive migration rate, with more people immigrating to the country than people leaving. This is one of the main reasons why the country's population has been increasing steadily over recent years.

National Immigrant Survey: Households with immigrants, by joint geographical typology and year of arrival in Spain of the pioneer. National.

People tend to overestimate the number of third country nationals as a proportion of the population of their country (68%). Only 38% of Europeans consider themselves well informed about migration and integration. More than half of respondents (56%) receive information on these topics through traditional media (TV, radio, newspapers), while the second largest information source (15%) is social media and networks. At the same time, a strong majority of Europeans (70%) view integration as a two-way process, in which both host societies and immigrants play an important role. Half of Europeans agree that integration of migrants is successful in their city or local area, while slightly less (42%) think the same about integration in their country. Just over half of Europeans (53%) agree that their national government is doing enough to promote the integration of migrants into society. A clear majority (69%) of respondents agree that it is necessary for their country to invest in integrating migrants. Moreover, three out of four Europeans (75%) believe that the integration needs of migrants should be taken into account when designing measures to fight the effects of the COVID-19 pandemic.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Trends" The file compares to previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Migrant Worker: Whole Family Goes Out data was reported at 35.780 Person mn in Dec 2014. This records an increase from the previous number of 35.250 Person mn for Dec 2013. Migrant Worker: Whole Family Goes Out data is updated quarterly, averaging 31.705 Person mn from Dec 2008 (Median) to Dec 2014, with 10 observations. The data reached an all-time high of 35.780 Person mn in Dec 2014 and a record low of 28.590 Person mn in Dec 2008. Migrant Worker: Whole Family Goes Out data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Labour Market – Table CN.GB: Migrant Worker.

Ontario was the province with the most immigrants in 2024, with 197,657 immigrants. Nunavut, Canada’s northernmost territory, had 56 immigrants arrive in the same period. Immigration to Canada Over the past 20 years, the number of immigrants to Canada has held steady and is just about evenly split between men and women. Asian countries dominate the list of leading countries of birth for foreign-born residents of Canada, although the United Kingdom, the United States, and Italy all make the list as well. Unemployment among immigrants In 2023, the unemployment rate for immigrants in Canada was highest among those who had been in the country for five years or less. The unemployment rate decreased the longer someone had been in Canada, and unemployment was lowest among those who had been in the country for more than ten years, coming more into line with the average unemployment rate for the whole of Canada.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

18 of the 37 states in Nigeria were selected using procedures described in the methodology report

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sampling Frame The sampling frame was the 2006 National Population Census. For administrative purposes, Nigeria has 36 states and the Federal Capital Territory. These states are grouped into six geopolitical zones - the North Central, North East, North West, South East, South South and South West. The states in turn are divided into 776 Local Governments. The demographic and political characteristics of the states vary considerably. For example, the number of component local government areas in the states ranges from 8 in Bayelsa State (in the South South) to 44 in Kano State (in the North West). Likewise state populations vary widely from 1.41 million in the Abuja Federal Capital Territory to 9.38 million in Kano State. The National Bureau of Statistics splits the country further into 23, 070 enumeration areas (EAs). While the enumeration areas are equally distributed across the local government areas, with each local government area having 30 enumeration areas, the differences in the number of local government areas across states implies that there are also huge differences in the number of enumeration areas across states. Appendix table 1 summarizes the population according to the 2006 population census (in absolute and proportionate numbers), number of local government areas, and number of enumeration areas in each state .

Given the above, a stratified random sampling technique was thought to be needed to select areas according to population and the expected prevalence of migrants. The National Bureau of Statistics (NBS) provided a randomly selected set of enumeration areas and households spread across all states in the Federation from the 2006 sampling frame. Every state in Nigeria has three senatorial zones (often referred to as North, Central and South or East, Central and West). The NBS sample enumeration areas were distributed such that within each state, local government areas from each senatorial zones were included in the sample, with Local Governments in each state nearly evenly distributed between rural and urban areas. In all, a total of 3188 enumeration areas were selected. These enumeration areas were unevenly spread across States; some states in the North West (Kano, Katsina, and Jigawa), and a few in the South South (Akwa Ibom and Delta) had over 100 enumeration areas selected while others such as Imo and Abia in the South East, and Borno, Gombe and Taraba in the North East, had as few as 20 enumeration areas selected. This selection partially reflected the relative population distribution and number of Local Government Areas in the component states. Annex Table B shows details of the states and geopolitical regions, their shares in population of the country, the number of Local Government Areas and enumeration areas in each state and the number of enumeration areas given in the NBS list that formed the frame for the study.

B. The Sample for the Migration Survey

a. Sample Selection of States, Local Governments and Enumeration Areas Originally, the intention was to have proportionate allocation across all states, using the population of each state in the 2006 Census to select the number of households to be included in the sample. But it was later recognized that this would not yield enough migrant households, particularly those with international migrants, especially as the total number of households that could likely be covered in the sample to was limited to 2000. Consequently, a disproportionate sampling approach was adopted, with the aim of oversampling areas of the country with more migrants. According to Bilsborrow (2006), this approach becomes necessary because migrants are rare populations for which a distinct disproportionate sampling procedure is needed to ensure they are adequately captured. Given the relative rareness of households with out-migrants to international destinations within the 10 year reference period (selected by the World Bank for all countries) prior to the planned survey, sampling methods appropriate for sampling rare elements were desirable, specifically, stratified sampling with two-phase sampling at the last stage.

Establishing the strata would require that there be previous work, say from the most recent Census, to determine migration incidence among the states. However, the needed census data could not be obtained from either the National Bureau of Statistics or the National Population Commission. Therefore, the stratification procedure had to rely on available literature, particularly Hernandez-Coss and Bun (2007), Agu (2009) and a few other recent, smaller studies on migration and remittances in Nigeria. Information from this literature was supplemented by expert judgement about migration from team members who had worked on economic surveys in Nigeria in the past. Information from the literature and the expert assessment indicated that migration from households is considerably higher in the South than in the North. Following this understanding, the states were formed into two strata- those with high and those with low incidence of migration. In all, 18 States (16 in the South and 2 in the North) were put into the high migration incidence stratum while 19 states (18 in the North and 1 in the South) were classified l into the low migration incidence stratum (column C of Appendix Table 1).

The Aggregate population of the 18 states in the high migration incidence stratum was 67.04 million, spread across 10,850 Enumeration areas. Thus, the mean population of an EA in the high migration stratum was 6179. In turn, the aggregate population of the 19 states in the low migration incidence stratum was 72.95 million spread across 12,110 EAs yielding a mean EA population of 6024. These numbers were close enough to assume the mean population of EAs was essentially the same. To oversample states in the high stratum, it was decided to select twice as high a proportion of the states as in the low stratum. To further concentrate the sample and make field work more efficient in being oriented to EAs more likely to have international migrants, we decided to select randomly twice as many LGAs in each state in the high stratum states as in the low stratum states.

Thus, 12 states were randomly selected with probabilities of selection proportionate to the population size of each state (so states with larger populations were accordingly more likely to fall in the sample) from the high stratum states. Then two LGAs were randomly selected from each sample state and 2 EAs per sample LGA (one urban, one rural) to yield a total of 12 x 2 x 2 or 48 EAs in the high stratum states. For the low stratum, 6 states were randomly selected. From each of these, 1 LGA was randomly picked and 2 EAs were selected per sample LGA to give a total of 6 x 1 x 2 or 12 EAs in the low stratum. This yielded a total of 60 EAs for both strata. Given the expected range of 2000 households to be sampled, approximately 67 households were to be sampled from each local government area or 34 households from each enumeration area.

So far, the discussion has assumed two groups of households - migrant and non-migrant households. However, the study was interested in not just lumping all migrants together, but rather in classifying migrants according to whether their destination was within or outside the country. Migrant households were thus subdivided into those with former household members who were international migrants and those with former household members who were internal migrants. Three strata of households were therefore required, namely:

1. Households with an international migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live in an international destination and has remained abroad;
2. Households with an internal migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live elsewhere in Nigeria (outside the sample LGA) and has not returned to the LGA; and
3. Households with no migrant: No member of the household has left to live elsewhere either within or outside the country since Jan. 1, 2000.

The selection of states to be included in the sample from both strata was based on Probabilities of Selection Proportional to (Estimated) Size or PPES. The population in each stratum was cumulated and systematic sampling was performed, with an interval of 12.16 million for the low stratum (72.95 million divided by 6 States), and 5.59 million for the high stratum (67.04 million divided by 12 States). This yields approximately double the rate of sampling in the high migration stratum, as earlier explained. Using a random start between 0 and 12.16, the following states were sampled in the low stratum: Niger, Bauchi, Yobe, Kano, Katsina, and Zamfara. In the high stratum, states sampled were Abia, Ebonyi, Imo, Akwa Ibom, Delta, Edo, Rivers, Lagos, Ondo, Osun and Oyo. Given its large population size, Lagos fell into the sample twice. The final sample, with LGAs and EAs moving from North to South (i.e. from the low to the high stratum states) is presented in Table 1 below.

The sample was concentrated in the South since that is where it was expected that more households have international migrants. It was expected that the survey would still also be reasonably representative of the whole country and of both internal migrant and non-migrant households through weighting the data. To this effect, field teams were asked to keep careful track at all stages of the numbers of people and households listed compared to the number in the

UK residents by broad country of birth and citizenship groups, broken down by UK country, local authority, unitary authority, metropolitan and London boroughs, and counties. Estimates from the Annual Population Survey.

Immigration Consulting Services Market Outlook

The global immigration consulting services market size was valued at approximately USD 5.6 billion in 2023 and is anticipated to reach USD 11.2 billion by 2032, growing at a CAGR of 8.1% during the forecast period. The market growth can be attributed to the rising demand for skilled labor in various countries, increasing cross-border travel for education and employment, and more stringent immigration regulations that necessitate professional consulting services.

One of the primary growth factors for this market is the increasing globalization and the subsequent need for skilled labor across different countries. As countries compete to attract top talent to boost their economies, the demand for professional immigration consulting services has surged. These services help individuals navigate complex immigration laws and secure the necessary visas and permits. Additionally, many countries have been tightening their immigration policies, making it more challenging for individuals to handle the process independently. This increased complexity drives the demand for specialized consulting services to simplify and expedite the process.

Another significant factor contributing to the market growth is the rise in international students seeking higher education abroad. Countries like the United States, Canada, Australia, and the UK have become popular destinations for students due to their world-class educational institutions and promising career opportunities. Immigration consultants play a crucial role in guiding students through the entire process, from university applications to obtaining student visas. This trend is expected to continue, bolstering the demand for consulting services in the coming years.

The proliferation of multinational corporations and the growing trend of corporate globalization also contribute substantially to market expansion. Companies often need to relocate employees to different countries for various projects and assignments. Immigration consulting services are essential in ensuring that these transitions are smooth and comply with local immigration laws. These services range from obtaining work visas to legal advisory and documentation assistance, making them indispensable for corporates looking to expand their operations internationally.

From a regional perspective, North America remains a dominant player in the immigration consulting services market, driven by high immigration rates and complex regulatory frameworks. However, regions like the Asia Pacific and Europe are rapidly emerging as significant markets due to rising economic opportunities and educational prospects. Increasing government initiatives to attract skilled labor in countries like Australia, New Zealand, and several European nations also play a crucial role in this regional growth. As such, the market is expected to witness substantial growth across various geographies.

Service Type Analysis

The service type segment of the immigration consulting services market is categorized into visa consulting, legal advisory, documentation assistance, and others. Visa consulting services are integral for individuals and businesses seeking to navigate the complex visa application processes. These services provide comprehensive support ranging from filling out forms to ensuring compliance with visa requirements. Given the increasing complexities in visa regulations worldwide, the demand for visa consulting services is projected to remain high throughout the forecast period.

Legal advisory services form another crucial segment. These services are vital for those facing legal issues related to immigration, such as deportation, asylum cases, and other immigration disputes. Legal advisors help clients understand their rights and responsibilities, providing legal representation when necessary. As immigration laws continue to evolve, the need for specialized legal advisory services is expected to grow. This segment is particularly important for high-stakes immigration cases where expert legal guidance can make a significant difference in outcomes.

Documentation assistance services are essential for ensuring that all necessary documents are correctly prepared and submitted on time. This includes everything from personal identification documents to financial statements and employment verification. Proper documentation is a critical aspect of any immigration application, and errors can lead to delays or denials. Therefore, the role of documentation assistance services is indispensable in the immigration consultin

Until 2016, Sweden had among the most generous asylum laws within the European Union. As a result, the immigration increased for several years, reaching 163,000 immigrants in 2016. During 2016, Sweden sharpened their asylum laws, and the number of immigrants started to decline. In 2020, also as a result of the COVID-19 pandemic, the number of immigrants in Sweden fell to 82,500, before increasing slightly again in 2021 and 2022. Over the last years, there was also a decline in the number of asylum grants in Sweden.

Large inflow of refugees

The so-called refugee crisis in the European Union that started in 2015 was characterized by a large inflow of refugees from non-European countries, mainly traversing the Mediterranean Sea in order to reach the European Union. In regards to the immigration trends to Sweden, one of the biggest groups in the last years consisted of Swedes returning to Sweden. Further countries that were among the top countries of origin in the latest years, were India, Syria, Germany, and Poland.

Decline in asylum grants in the European Union

Sweden is not the only country that sharpened the rules for asylum grants in 2016, it has been observed within the whole European Union. Since the end of 2016, there has been a significant decline in the number of accepted first instance asylum applications within the European Union.

The estimated population of unauthorized immigrants in the U.S. stands at around 11 million people. Although the number has stabilized, the United States has seen a spike in migrant encounters in the last few years, with over two million cases registered by the U.S. Border Patrol in 2023. This is a slight decrease from the previous year, when there were over 2.2 million cases registered. Due to its proximity and shared border, Mexico remains the leading country of origin for most undocumented immigrants in the U.S., with California and Texas being home to the majority.

Immigration and political division

Despite the majority of the population having immigrant roots, the topic of immigration in the U.S. remains one of the country’s longest-standing political debates. Support among Republicans for restrictive immigration has grown alongside Democratic support for open immigration. This growing divide has deepened the polarization between the two major political parties, stifling constructive dialogue and impeding meaningful reform efforts and as a result, has led to dissatisfaction from all sides. In addition to general immigration policy, feelings toward illegal immigration in the U.S. also vary widely. For some, it's seen as a significant threat to national security, cultural identity, and economic stability. This perspective often aligns with support for stringent measures like Trump's proposed border wall and increased enforcement efforts. On the other hand, there are those who are more sympathetic toward undocumented immigrants, as demonstrated by support for the Deferred Action for Childhood Arrivals (DACA) program.

The national integration barometer gives visibility to the status and development of the integration area. It consists of nine integration objectives with related indicators; which shows the development towards the objectives both in the country as a whole and in the individual municipalities. In addition, the integration barometer contains a number of statistics; key figures; evaluations and analyses; It explores different aspects of integration. Among other things, you will find a number of national and municipal key figures on refugees; figures and analyses on, inter alia, employment, training and repatriation efforts; crime; citizenship; Danish citizenship; statistics on activity in Danish education for adult foreigners and key figures on displaced persons from Ukraine in Denmark. The Integration Barometer focuses on immigrants and descendants of non-Western origin; supplemented by statistics on immigrants and descendants originating in MENAP countries and Turkey.

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.

The main objective of the SIMCUR research project is to uncover the processes underlying developmental resilience in children from immigrant families during the transitions to primary and secondary education in three European countries. These processes are examined at the levels of the individual, the family, the school, and the community. By comparing children in Germany, the Netherlands, and Norway, the study also elucidates the impact of broader societal influences. In a longitudinal cohort design based on the two school transitions, we studied 880 migrant families with origins in Turkey allowing across- country comparisons. Mastering major educational transitions is a critical indicator of social integration and is related to individual psychosocial adaptation. For the primary school transition, 364 children from Turkish migrant families (cohort 1) were assessed at ages 5, 6, and 7 in the three participating countries. For the secondary school transition, we assessed 256 children in a second cohort of children at ages 12, 13, and 14. Because this transition takes place earlier in Germany, this country had an extra cohort of 147 children assessed at ages 9, 10, and 11. At each assessment, variables from all levels of functioning are measured using multiple methods (behavioral observation, interviews, tests, and surveys), obtained from multiple sources (mothers, fathers and children).

Parents 1. Mother questionnaire:

Category 1: Background Family situation: relation to the child / father; caregiver; number of children, marital status; health; education, work and economic situation: years of schooling, ISCED; gainful employment; working hours; total income; neighborhood: managing to make it (length of residence), NICHD (National Institute of Child Health and Human Development); Collective Efficacy Scale; activities at home: literacy and media at home; language use in reading and watching TV; language: language proficiency Turkish and majority language; importance of child language use; language use in Turkish and majority language; culture: MEIM-R (Multigroup Ethnic Identity Measure - revised); questions from ICSEY study (International Comparative Study on Ethnocultural Youth; acculturation stress; discrimination; religion: religious affiliation; role of religion in parenting.

Category 2: Child Behavior: CBQ (Child Behavior Questionnaire), EATQ-R (Early Adolescent Temperament Questionnaire - revised); parenting: discipline; EMBU (Egna Minnen Beträffande Uppfostran (´My memories of upbringing´); aspirations and expectations: schooling you would lke/actually expect your child to complete; school: child preschool attendance; parent-teacher involvement, parent-teacher responsibility, confidence in school/teacher; get ready for school; transition; strengths and difficulties: SDQ (Strength and Difficulties Questionnaire); friends: number of friends; frequency of playing; language: child´s language use.

Category 3: Yourself Your life: SWLS (The Satisfaction with Life Scale); task division; social network: Oslo 3-item social support scale; relationship to neighbors; daily life: daily hassles; relationship: VGP (Vragenlijist voor Gezins Problemen); feelings: CES-D 10 (Center for Epidemiologic Studies Short Depression Scale); family: FAD (Family Assessment Device); values: perceived achievement values; familiy collectivist values.

Mother interview: Family history: family tree, reason for migration; legal status; neighborhood: NICHD (National Institute of Child Health and Human Development); managing to make it (Subscale: community services); car/driver´s license; living situation: living space; daily schedule: daily schedules; spare time; activities at home: media use at home.

1. Father questionnaire:

Category 1: Background Language: language proficiency Turkish and majority language; importance of child; language use in Turkish and majority language; culture: MEIM-R (Multigroup Ethnic Identity Measure - revised); questions from ICSEY study (International Comparative Study on Ethnocultural Youth); acculturation stress; discrimination; religion: religious affiliation; role of religion in parenting.

Category 2: Child Parenting: EMBU (Egna Minnen Beträffande Uppfostran (´My memories of upbringing´); school: parent-teacher-responsibility; SDQ (Strength and Difficulties Questionnaire).

Category 3: Yourself Parenting: task division; relationship: VGP (Vragenlijist voor Gezins Problemen, Subscale: partner relationship/marital support); values: perceived achievement values.

Child

Category 1: Background Culture: MEIM-R (Multigroup Ethnic Identity Measure - revised); questions from ICSEY study (International Comparative Study on Ethnocultural Youth); discrimination: perceived ethnic discrimination; language: language proficiency Turkish and majority language; language use Turkish and majority language; religion: children´s practice of their beliefs; spirituality´s role in coping and everyday life; overall...

Geographic coverage

Nationally representative sample

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame

The 2002 population and housing census provided a frame for sample selection. The frame contains a list of all administrative units up to the lowest level called, 'Local Council 1', or LC1. This is usually, but not always consistent with a village in terms of area. The Enumeration Area (EA) may comprise of one village/LC1, or more than one village/LC1. The demarcation of EAs is based on total population within a given area and in many instances, may vary by locality. In addition the sampling frame also indicates the EA to which a particular LC belongs. The 2002 Uganda Sampling Frame has a total of 33,283 EAs.

Study population

The study population comprised of the entire population of Uganda. Based on the distribution of households in table 1 above, the sample was determined based on information from Uganda National Household survey 2005/06 conducted by the Uganda Bureau of Statistics. The proportion of internal migrants reported in the past 5 years has been used to estimate the required sample. Given the limited nature of the number of international migrants, the proportion of internal migrants is considered adequate to provide sufficient estimates of the indicators of interest.

Sample allocation by region

The above sample was proportionately allocated across the four statistical regions on the basis of the population in each of the regions. There was oversampling for urban population approximately by 5 times. To ease implementation, the regional sample was further disaggregated down to Enumeration Area level.

Selection of Enumeration Areas

The task was to undertake a nationally-representative survey of 2,000 households (urban and rural combined) in 2009 that would provide information on migration, remittances and their effects on development. The frame was be divided into two strata namely rural and urban. A two-stage stratified sample design was adopted. The first stage representing the primary sampling unit comprised of the selection of EAs from each of the strata while at the second and ultimate stage households were selected. EAs were selected from the list of Enumeration Areas developed after the 2002 Population and Housing Census and updated to include new districts.

The selection of EAs was proportionally done based on the number of households in the respective stratum according to the 2006 Uganda household survey. All the EAs in each domain were sorted by county, sub-county and parish. A random number was generated and an appropriate random start and sampling interval was systematically selected from the ordered list with probability proportionate to number of households. This was done separately for urban and rural areas, hence stratified sampling. The proportion of EAs sampled in urban areas is about 5 times that in rural.

Selection of households

At the second stage, a complete listing of households in each EA was done to classify the households into three groups: non migrants, internal migrants and international migrants. The number of households per EA varied from around 20 to about 1000. Most of the time, all households were listed even in the large EAs since it was difficult to establish lines of demarcation to segment the EA.

A total of 10 households were selected randomly from each of the 200 EAs. The goal was to select 4 households with an international migrant (emigrant), 3 with one or more internal migrants, and 3 with no migrant. This sampling was done from the three strata or listings of households according to migration status. Separate sampling was done from each stratum using systematic sampling. In case of a refusal or other reason for non-response, another household was selected from the same stratum to reach the desired quota. In case the number of households listed in any of the three strata was smaller than the numbers desired (4, 3, 3), then all those listed in that stratum were automatically sampled and the short fall selected from the next stratum.

For example, if there were, say, 150 households in an EA, with 3 with international migrants, 27 with internal migrants, and 120 with no migrants, the numbers selected would be, respectively, 3, 3 and 3. But to make up 10, priority would be given to the migrant stratum to add one more, randomly selected, from that stratum. As another example, suppose there were 0 international migrant households; then 7 would be selected from the internal migrant stratum and still only 3 from the list of non-migrant households.

The choice of 10 households per EA was based on experience from the various economic surveys conducted by UBOS, where 10 households provide adequate representation at EA level for most of the economic and social indicators.

The listing operation

The survey targeted household with in-migrants or former members who have migrated away, whether to another part of the country (urban or rural) or to another country. Since the census frame does not uniquely identify who is a migrant or non migrant, and owing to the lack of an up to date list of all households in Uganda from which to draw the sample, the survey team adopted a listing exercise as stop gap measure.

The exercise involved conducting a fresh listing of all households in each of the selected EAs. During the exercise, households with migrants were identified and the migrants clearly categorized as internal-within Uganda- or international where household members had moved to another country all together. The total number of listed household numbers was 24,618. Thereafter, a sample of 10 households was selected using systematic sampling procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire consisted of seven sections namely: A Cover Sheet requiring household identification particulars including district name and code, county name and code, parish name and code, EA name, stratum, household number, names of the household head and first spouse, number of household members and a description of the location of the household.

In addition, the page captured details of the interview including the interviewer name, date, duration and the outcome of the interview. It also provided for the team leaders remarks and signature.

Section 1: Household roster This section captured the socio-demographic characteristics of all household members.

Section 2: Households housing conditions In this section, information was sought on the type of dwelling, occupancy status, the physical characteristics of the dwelling, and access to basic utilities including water, electricity and sanitation.

Section 3 Household Assets and Expenditure The section collected information on the assets and expenditures of the household. This information was used to determine the welfare status of the household.

Section 4: Household Use of Financial Services: In this section, information relating to use of financial services by household members was collected.

Section 5: Internal and International Migration And Remittances From Former Household Members This section captured information on migration, both internal and international as well as remittances received by the household from former household member migrants.

Section 6: Internal and International Migration and Remittances From Former Household Members Like section 5 above, section 6 sought information on migration, both internal and international as well as remittances received by the household from non household member migrants.

Section 7: Return Migrants Here information on Return migrants was captured. A return migrant was defined as an adult member (over 18 years old) currently living in the household, who had lived in another country or another place in Uganda for at least 3 months in the 5 years preceding the survey. The information sought in this section related to the last migration episode for each return migrant.

Cleaning operations

Data Editing: Data editing was initially done by six editors from among the enumerators.

Prior to data entry, efforts were made to manually edit and ensure that inconsistent entries in the questionnaire were corrected. Data entry was initially done using the EPIDATA software after which it was exported to SPSS for further processing and analysis. This included the creation of variable and value labels for the data.

Response rate

Three categories of non response were encountered in the survey. These include: · Household not Visisted: In this category, the survey teams were unable to visit the households due to one reason or another. This happened in Karamoja, where 2 EAs could not be visited due to insecurity; Kalangala, an island EA where residents were reported to have vacated the EA on the advice of the National Environmental Management Authority (NEMA) in a bid to conserve the environment, four years prior to the visit by the survey team and in Kampala, where an EA could not be located. This led to a loss of 40 responses. · Incomplete Information: Here households were located but enumerators were not able to conduct or complete the interviews due to various reasons. Such reasons include respondents' hostility, interruption by an unforeseen event such as death of the respondent's close relative. The total number of responses lost in this category is 79. Overall, there were 1872 valid responses received representing a response rate of 94%. Of these, 49% reported having migrants.

As of May 2024, a total of 5.4 million Indian migrants were estimated to live in the United States of America, followed by over three million in the United Arab Emirates (UAE). India has over 35 million overseas Indians living across the world.

Full edition for public use. The REMINDER Integrated Multilevel Database on Migration in the EU brings together cross-national public opinion data and statistics related to immigration and EU mobility from multiple sources. The archived material offers complete replication code and auxiliary files to assist researchers in reconstructing and expanding the database for their own analyses. Please note that replication requires users to access the original survey data separately. The integrated database consists of 184400 observations and 160 variables, 61 of which are at the country level (28 EU plus Norway and Switzerland). We harmonize existing and newly collected survey data between 2002 and 2017, matched with country level data on the welfare impacts of immigration as well as stocks and flows of immigrant populations.

Abstract

Previous surveys on labor migration from Pacific Island countries are often cross-sectional, not readily available, and focusing on one migration scheme, country, or issue and hence incompatible. Such limitation of existing data restricts analysis of a range of policy-relevant issues that present themselves over the migrants' life cycle such as those on migration pathways, long-term changes in household livelihood, and trajectory of migrants’ labor market outcomes, despite the significant impacts of labor migration on the economy of the Pacific Island countries. To address these shortfalls in the Pacific migration data landscape, the PLMS is designed to be longitudinal, spanning multiple labor sending and receiving countries and collecting omnibus information on both migrants, their households and non-migrant households. The survey allows for disaggregation and reliable comparative analysis both within and across countries and labor mobility schemes. This open-access and high-quality data will facilitate more research about the Pacific migration, help inform and improve Pacific migration policy deliberations, and engender broader positive change in the Pacific data ecosystem.

Geographic coverage

Tonga: Tongatapu, ‘Eua, Vava’u, Ha’apai, Ongo Niua. Vanuatu: Malampa, Penama, Sanma, Shefa, Tafea, Torba. Kiribati: Abaiang, Abemama, Aranuka, Arorae, Banaba, Beru, Butaritari, Kiritimati, Maiana, Makin, Marakei, Nikunau, Nonouti, North Tabiteuea, North Tarawa, Onotoa, South Tabiteuea, South Tarawa, Tabuaeran, Tamana, Teraina.

Analysis unit

• Households in Kiribati, Tonga, and Vanuatu.
• Temporary migrant workers from Kiribati, Tonga and Vanuatu who participated in the Pacific Australia Labour Mobility scheme in Australia and the Recognised Seasonal Employers scheme in New Zealand

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling frame: The PLMS sample was designed based on a Total Survey Error framework, seeking to minimize errors and bias at every stage of the process throughout preparation and implementation.

The worker sample frame is an extensive list of approximately 11,600 migrant workers from Kiribati, Tonga and Vanuatu who had participated in the RSE and PALM schemes. Due to the different modes of interviews, sampling strategies for the face-to-face segment of the household survey in Tonga was different from the rest of the surveys implemented via phone interviews. The face-to-face segment of the household survey selected households using Probability Proportional to Size sampling based on the latest population census listing and our worker sample frame, with technical inputs from the Tonga Statistics Department. The phone-based segment of the household survey used a combination of Probability Proportional to Size sampling based on the existing sample frame and random digit dialing. The design of the sample benefited from technical inputs from the Tonga Statistics Departments and the Vanuatu National Statistics Office, as well as World Bank staff from Kiribati.

As participation in the survey is voluntary, a worker might agree to participate while their household did not, and vice versa. Because of this, the survey did not achieve a complete one-to-one match between interviewed workers and sending households. Of all interviewed respondents, 418 workers in the worker survey are linked to their households in the household survey. However, after removing incomplete interviews, 341 worker-household pairs remain. They are matched by either pre-assigned serial ID numbers or contact details collected in the household and worker surveys during the post-fieldwork data cleaning process.

Sampling deviation

The survey was originally planned to be conducted face-to-face and was so for most of the collection of household data in Tonga. However, due to COVID-19, it was switched to phone-based mode and the survey instruments were adjusted accordingly to better suit the phone-based data collection while ensuring data quality. In particular, the household questionnaire was shortened, and sampling strategy changed to a combination of Probability Proportional to Size sampling based on the existing household listing and random digit dialing.

Compared to in-person data collection, the usual caveats of potential biases in phone-based survey related to disproportional phone ownership and connectivity apply here. The random digit dialing approach provides data representative of the phone-owning population. Yet due to lack of information, it is difficult to judge whether sending households in Kiribati, Tonga, and Vanuatu are more or less likely to own a phone and/or respond positively to survey request than non-sending households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• The questionnaires were jointly designed in English by the World Bank and researchers at the Development Policy Centre, Australian National University. They were translated into Bislama, Gilbertese and Tongan, scripted into CAPI/CATI programs, tested and piloted before being finalized. The design of the questionnaires and the samples benefited from technical inputs from the Tonga Statistics Departments, Pacific consultants, and academic experts specialized in Pacific labor mobility and remittances.
• Enumerators are native speakers from the labor-sending countries covered in the survey and were trained to elicit information asked in the questionnaire in local languages.
• The phone-based household questionnaire is moderately shorter than the in-person version.

Cleaning operations

The published data have been cleaned and anonymized. All incomplete interview records have been removed from the final datasets. The anonymization process followed the theory of Statistical Disclosure Control for microdata, aiming to minimize re-identification risk, i.e. the risk that the identity of an individual (or a household) described by a specific record could be determined with a high level of confidence. The anonymization process employs the k-anonymity method to calculate the re-identification risk. Risk measurement, anonymization and utility measurement for the PLMS were done using sdcMicro, an add-on package for the statistical software R for Statistical Disclosure Control (SDC) of microdata.

Since the household questionnaire was shortened when the survey switched from face-to-face to phone-based data collection, there face-to-face datasets and phone-based datasets are not identical, but they are consistent and can be harmonized. The mapping guide enclosed in this publication provides a guide to data users to wish to harmonize them.

Household expenditure variables in the household dataset and individual wage variable in the household member dataset are in USD. Local currencies were converted into USD based on the following exchange rates: 1 Tongan Pa'anga= 0.42201412 USD; 1 Vanuatu Vatu= 0.0083905322 USD; 1 Kiribati dollar= 0.66942499 USD.

Response rate

Face-to-face segment of the PLMS household survey: not applicable. Phone-based segment of the PLMS household survey: 26%. The PLMS Worker survey: 31%