The United States hosted, by far, the highest number of immigrants in the world in 2020. That year, there were over ** million people born outside of the States residing in the country. Germany and Saudi Arabia followed behind at around ** and ** 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 ******* people that year. An additional ******* people became legal residents through employment opportunities. In terms of naturalized citizenship, ******* 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 **** million skilled workers by 2040, compared to **** million by 2040 in a high-migration scenario. In both scenarios, this is still a decrease from **** skilled workers in 2020.

All of the inhabitants in the Holy See, the home of the leader of the Roman Catholic Church, were immigrants in 2020, meaning that they were born outside of the country. Perhaps more interesting are the Gulf States the United Arab Emirates, Qatar, and Kuwait, all with an immigrant population of over ** percent of their total populations, underlining the high importance of migrant workers to these countries' economies. In terms of numbers, the United States had the highest number of immigrants in 2020. Migration to Gulf Cooperation Council states The United Arab Emirates, Qatar, and Kuwait, all members of the Gulf Cooperation Council (GCC), have a significant amount of migrant labor. The United Arab Emirates and Qatar both rank high in quality-of-life rankings for immigrants. A significant number of migrant workers in the GCC originate from Asia, with the most originating from Bangladesh. As of 2022, nearly ***** thousand Bangladeshi citizens expatriated to work in GCC nations. The American melting pot The United States is known for having high levels of diversity and migration. Migration to the United States experienced peaks from the periods of 1990-1999 as well as 1900-1909. Currently, Latin Americans are the largest migrant group in the United States, followed by migrants from Asia. Out of each state, California has some of the highest naturalization rates. In 2021, ******* people in California naturalized as U.S. citizens, followed by Florida, New York, Texas, and New Jersey.

The United States had the ******* net migration levels of the G7 countries between 2000 and 2025. This is unsurprising as it is also the country with the highest population of the seven. Moreover, net migration to the United States decreased from 2016 onwards, following the beginning of the Trump administration. Germany's net migration peaked in 2015 and 2022 after a high number of refugees immigrated to the country, but has been decreasing since. In terms of net migration per 1,000 inhabitants, the U.S. had the highest ratio in 2025.

The rating reflects the number of migrants in the country. It allows you to assess how attractive the country is for foreigners to live and work, as well as in terms of migration legislation.

United States Immigrants Admitted: All Countries data was reported at 1,127,167.000 Person in 2017. This records a decrease from the previous number of 1,183,505.000 Person for 2016. United States Immigrants Admitted: All Countries data is updated yearly, averaging 451,510.000 Person from Sep 1900 (Median) to 2017, with 118 observations. The data reached an all-time high of 1,827,167.000 Person in 1991 and a record low of 23,068.000 Person in 1933. United States Immigrants Admitted: All Countries data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s United States – Table US.G087: Immigration.

Of the G7 countries, Canada had the highest crude net migration rate most of the years between 2000 and 2022. In 2023, the net migration ratio of the average population in Canada reached ** per 1,000 inhabitants. On the other hand, the rate in Japan was *** per 1,000 inhabitants. Migration numbers were unusually low in 2020 and 2021 due to the COVID-19 pandemic.

Sweden was the Nordic country that received the highest number of immigrants from 2001 to 2021. In 2021, nearly ****** people immigrated to Sweden, but was overtaken by Denmark in 2022. Sweden was also the country in the region with the highest net migration over the last years. Iceland, which also has the smallest population of the five, had the lowest number of immigrants. Migration to Sweden As the Nordic country with the highest number of migrants, nearly ** percent of survey respondents consider immigration an important issue for Swedish society, more than other European countries. In 2023, most immigrants to Sweden were Swedes returning to the country, followed by India, Poland, and Germany. The need for migration in Nordic nations Migrants often fill in gaps within labor markets that local populations cannot fill. In Nordic nations, these gaps are becoming more apparent as fertility rates decrease. Over the past decade, crude birth rates have decreased in all Nordic countries. Meanwhile, those aged 70 years and older are becoming larger portions of Nordic societies. Declining birth rates combined with aging societies mean that labor markets will be challenged to have enough workers.

In the Nordic countries, the largest number of people emigrated from Denmark in 2022. Nearly 70,000 people emigrated from Denmark that year, even though its neighbor Sweden has a population that is nearly twice as large. Iceland, on the other hand, had the lowest number of emigrants that year. Meanwhile, the highest number of immigrants over the last years arrived in Sweden.

According to estimates, South Sudan had the highest net migration rate in Africa as of 2023, at nearly ** per 1,000 inhabitants. This meant that, for 1,000 people in South Sudan, ** will immigrate to the country. The positive net immigration rate also indicated that the number of international migrants coming to South Sudan was higher than that of South Sudanese people leaving the nation. On the other hand, Eritrea had a net migration of minus **** per 1,000 inhabitants. The negative rate indicated a number of emigrants higher than that of immigrants.

Using a multilevel modelling approach to analyse a novel dataset of academic publications at all business schools in 11 European countries, this paper finds that the influence of organisational- and country-level contextual factors on researchers varies considerably based on the type of institution and the development level of the country they are located in. At the organisational-level, we find that greater spatial connectivity–operationalised through proximity to nearby business schools, rail stations, and airports–is positively related to scientific research volume and public dissemination (news mentions). While this result is significant only for high-income countries (above EU-average 2018 GDP per capita), this is likely because the low-income countries (below EU-average 2018 GDP per capita) examined here lack a ‘critical mass’ of well-connected universities to generate observable agglomeration effects. At the country-level, the results indicate that in high-income countries, less prestigious schools benefit from higher rates of recent international immigration from any foreign country, providing a direct policy pathway for increasing research output for universities that aren’t already well-known enough to attract the most talented researchers. In low-income countries, recent immigration rates are even stronger predictors of research performance across all levels of institutional prestige; more open immigration policies would likely benefit research performance in these countries to an even greater extent. Finally, the paper’s results show that, in low-income countries, a composite measure of a country’s quality of life (including self-rated life satisfaction, health, working hours, and housing overcrowding) is positively related to research outcomes through its interaction with school prestige. This suggests that the lower a country’s quality of life, the more researchers are incentivised to produce higher levels of research output. While this may in part reflect the greater disparities inherent in these countries’ economic systems, it is noteworthy–and perhaps concerning–that we have observed a negative correlation between country-level quality of life and research performance in low-income countries, which is particularly felt by researchers at less prestigious institutions.

As of 2022, the European country with the most citizens of the People's Republic of China was Italy, with around 300,000 people. Spain also hosted a substantial number of Chinese nationals at roughly 193,000 people. These figures are likely to underestimate the number of people who were born in China or are of Chinese ancestry, as many of these immigrants receive the citizenship of the European country which they migrated to after living there for a period of time, and the People's Republic of China does not allow its citizens to hold dual citizenship.

In 2024, the net migration rate in France reached 152,000. In recent years Europe and France have seen more people arrive than depart. The net migration rate is the difference between the number of immigrants (people coming into an area) and the number of emigrants (people leaving an area) throughout the year. France's highest net migration rate was reached in 2018 when it amounted to 201,000. Armed conflicts and economic migration are some of the reasons for immigration in Europe. The refugee crisis Studies have shown that there were 331,000 immigrant arrivals in France in 2022, which has risen since 2014. The migrant crisis, which began in 2015 in Europe, had an impact on the migration entry flows not only in France but in all European countries. The number of illegal border crossings to the EU over the Eastern Mediterranean route reached a record number of 885,386 crossings in 2015. Immigration in France Since the middle of the 19th century, France has attracted immigrants, first from European countries (like Poland, Spain, and Italy), and then from the former French colonies. In 2023, there were approximately 8.9 million people foreign-born in France. Most of them were living in the Ile-de-France region, which contains Paris, and in Provence-Alpes-Côte d’Azur in the Southeastern part of the country. In 2022, the majority of immigrants arriving in France were from Africa and Europe.

This table contains 25 series, with data for years 1955 - 2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Last permanent residence (25 items: Total immigrants; France; Great Britain; Total Europe ...).

Of the 98,000 people immigrating to Denmark in 2023, the highest number came from Ukraine. Over 8,000 immigrants from Ukraine were registered in Denmark that year. The high number of immigrants from Ukraine is related to the Russian invasion of Ukraine in February 2022. Germany followed in second at 7,000. The third most common country of origin for immigrants arriving in Denmark was the United States. The highest number of immigrants residing in Denmark at the beginning of 2024 came from Poland.

In 2022, the largest group of immigrants arriving in Norway came from Ukraine after the Russian invasion of the country in February that year. Moreover, the origin of nearly 17,000 immigrants was unknown. Immigrants from Poland made up the third largest group. At the beginning of 2023, a total of nearly 112,000 Polish people resided in Norway.

This data harmonizes waves 2, 4, and 5 from the European Social Survey, waves 5 and 6 from the World Values Survey, and wave 4 from the European Values Study. The aim of the study was to analyze gender attitudes using the statement "Men should have more right to a job than women when jobs are scarce". For information on those people who stayed in the sending countries data from WVS6 for the following countries was chosen: Algeria, Argentina, Australia, Brazil, Chile, China, Colombia, Cyprus, Ecuador, Estonia, Ghana, Hong Kong, India, Iraq, Japan, Kazakhstan, Kyrgyzstan, Lebanon, Mexico, Morocco, Nigeria, Pakistan, Peru, Philippines, Poland, Romania, Russia, Rwanda, Singapore, South Africa, South Korea, Thailand, Tunisia, Turkey, Ukraine, the United States, Uruguay, and Zimbabwe.

I also employ data for several countries from Wave 5 for those societies that were not covered during the last wave: Bulgaria, Canada, Egypt, Finland, Hungary, Indonesia, Italy, Iran, Moldova, Norway, Vietnam, Serbia and Montenegro, and Zambia.

I add European societies that have not been covered by the WVS by using the European Values Study 2008: Albania, Austria, Bosnia and Herzegovina, Croatia, Czech Republic, Denmark, Greece, Ireland, Lithuania, Luxembourg, Macedonia, Slovak Republic, and Slovenia. This gives 65 sending societies in total. As people could have migrated from the European countries of the main focus, namely, Belgium, Germany, France, the Netherlands, Portugal, Spain, Sweden, Switzerland, and the UK, I add those as well, with a final total of 73 sending countries.

Such variables as age, gender, migration status, religiosity measured by self-attribution (How religious are you?), Importance of God, and church attendance as well as denomination are added. Education is binarized for higher o higher. Employment is measured by 6 categories, marital status - by 5 categories. Those who refused to answer were coded into a separate category "refused".

Country-level variables: Human Development Index (HDI), GDP per capita, Polity IV, Freedom House Civil Liberties Index, Gender Inequality Index (by UNDP), unemployment ratio of women to men; percentage of women in the labor market, percentage of women in parliaments, percentage of Islamic population in the country, Islamic majority in the country (binary), level of religiosity in the country (country average for ``How important is God in your life?"), post-communism, Cultural zones from Inglehart's cultural map (8 groups).

Among countries with the highest number of overseas Chinese on each continent, the largest Chinese diaspora community is living in Indonesia, numbering more than ten million people. Most of these people are descendants from migrants born in China, who have moved to Indonesia a long time ago. On the contrary, a large part of overseas Chinese living in Canada and Australia have arrived in these countries only during the last two decades. China as an emigration country Many Chinese people have emigrated from their home country in search of better living conditions and educational chances. The increasing number of Chinese emigrants has benefited from loosened migration policies. On the one hand, the attitude of the Chinese government towards emigration has changed significantly. Overseas Chinese are considered to be strong supporters for the overall strength of Chinese culture and international influence. On the other hand, migration policies in the United States and Canada are changing with time, expanding migration opportunities for non-European immigrants. As a result, China has become one of the world’s largest emigration countries as well as the country with the highest outflows of high net worth individuals. However, the mass emigration is causing a severe loss of homegrown talents and assets. The problem of talent and wealth outflow has raised pressing questions to the Chinese government, and a solution to this issue is yet to be determined. Popular destinations among Chinese emigrants Over the last decades, English speaking developed countries have been popular destinations for Chinese emigrants. In 2022 alone, the number of people from China naturalized as U.S. citizens had amounted to over 27,000 people, while nearly 68,000 had obtained legal permanent resident status as “green card” recipients. Among other popular immigration destinations for Chinese riches are Canada, Australia, Europe, and Singapore.

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

# FUME data

Population data and migration flows from FUME projections.

## Introduction

International projection model with dimensions Age, Sex, Education and Country of Birth.

Projected from 2015 to 2050, four different scenarios; Benchmark, Short War, Scenario B and Scenario C. Additional scenario with no migration also included.

Benchmark scenario: Identical to SSP2 from Koch & Leimbach (2022), including COVID
shock but not Ukraine war.

Short-war scenario: Same as benchmark scenario but using the IMF estimate (International
Monetary Fund, 2022) until 2027, then linear transition over 5 years back to SSP2 growth
rates.

Scenario B - Recovery in Europe, stagnation in developing countries: Same as short-war
scenario, but instead of all countries transitioning to SSP2, European countries transition
towards the SSP in which they have the highest growth rates; while developing countries
(including emerging economies) transition towards the SSP in which they have the lowest
growth rates. These might be different SSPs for different countries. All other countries (e.g.
USA, Australia etc.) transition towards SSP2.

Scenario C - Rise of the East: Same as Scenario B, but opposite: European countries
transition towards the SSP in which they have the lowest growth rates; while developing
countries (including emerging economies) transition towards the SSP in which they have
the highest growth rates. All other countries (E.g. USA, Australia etc.) transition towards
SSP2.

## Variables

period: Start year of projection step

dest: Country of residency / Migration destination country

CoB: Country of Birth

area: ISO3 numeric country code of destination

age: Age, five year groups, 0 - 100+

edu: Education, 6 levels, (e1 = No Education, e2 = Some Primary, e3 = Primary, e4 = Lower Secondary, e5 = Upper
Secondary, e6 = Post Secondary)

sex: Sex, two categories

pop: Population

### Migration rate data specific variable names

POB: Place Of Birth (Country of Birth)

Orig: Country of origin

Dest: Country of destination

flow: Migration rate

Skill: Skill categories, (Low (Secondary and Less) and High (Post secondary+))

age: Age groups (1 (0-24), 2 (25-64), 3 (65+))

flowM: Male specific migration rate

flowF: Female specific migration rate

## Countries

Countries currently included in the model are in total 171 (given in ISO3 country codes):

```
"AFG" "AUT" "BEL" "BGR" "CYP" "CZE" "DEU" "DNK" "ESP" "EST" "FIN" "FRA" "GBR"
"GRC" "HRV" "HUN" "IRL" "ITA" "LTU" "LUX" "LVA" "MLT" "NLD" "POL" "PRT" "ROU"
"SVK" "SVN" "SWE" "AGO" "ALB" "ARE" "ARG" "ARM" "AUS" "AZE" "BDI" "BEN" "BFA"
"BGD" "BHR" "BHS" "BIH" "BLR" "BLZ" "BOL" "BRA" "BTN" "BWA" "CAF" "CAN" "CHE"
"CHL" "CHN" "CIV" "CMR" "COD" "COG" "COL" "COM" "CPV" "CRI" "CUB" "DOM" "DZA"
"ECU" "EGY" "ETH" "FJI" "GAB" "GEO" "GHA" "GIN" "GMB" "GNB" "GNQ" "GTM" "GUY"
"HKG" "HND" "HTI" "IDN" "IND" "IRN" "IRQ" "ISL" "ISR" "JAM" "JOR" "JPN" "KAZ"
"KEN" "KGZ" "KHM" "KOR" "KWT" "LAO" "LBN" "LBR" "LCA" "LKA" "LSO" "MAC" "MAR"
"MDA" "MDG" "MDV" "MEX" "MKD" "MLI" "MMR" "MNE" "MNG" "MOZ" "MUS" "MWI" "MYS"
"NAM" "NER" "NGA" "NIC" "NOR" "NPL" "NZL" "OMN" "PAK" "PAN" "PER" "PHL" "PRI"
"PRY" "PSE" "QAT" "RUS" "RWA" "SAU" "SDN" "SEN" "SGP" "SLB" "SLE" "SLV" "SOM"
"SRB" "STP" "SUR" "SWZ" "SYR" "TCD" "TGO" "THA" "TJK" "TKM" "TLS" "TTO" "TUN"
"TUR" "TZA" "UGA" "UKR" "URY" "USA" "VCT" "VEN" "VNM" "VUT" "WSM" "YEM" "ZAF"
"ZMB" "ZWE"
```