This graph shows the distribution of nationalities among documented immigrants who arrived in the United States between 1820 and 1870. As we can see, over seven million people arrived in the US in this 50 year period, with the majority coming from Ireland, Germany and Britain. The largest groups, by far, were Irish and German, who together made up roughly two thirds of all immigrants to the US during this time. The reasons for this were because of the Irish Potato famine from 1845 to 1849, which resulted in the death or emigration of twenty to twenty five percent of the total Irish population, and a number of internal factors in Germany such as economic migration for farmers affected by industrialization, political/religious asylum, and in order to avoid conscription. One noteworthy exclusion from the information is of those transported to US as slaves, whose information was not recorded in this statistic (although the slave trade was abolished in 1808, the practice continued in the decades that followed).

The largest number of immigrants in Germany were from Ukraine, as of 2023. The top three origin countries were rounded up by Romania and Turkey. Immigrants are defined as having left a country, which may be their home country, to permanently reside in another. Upon arriving, immigrants do not hold the citizenship of the country they move to. Immigration in the EU All three aforementioned countries are members of the European Union, which means their citizens have freedom of movement between EU member states. In practice, this means that citizens of any EU member country may relocate between them to live and work there. Unrestricted by visas or residence permits, the search for university courses, jobs, retirement options, and places to live seems to be defined by an enormous amount of choice. However, even in this freedom of movement scheme, immigration may be hampered by bureaucratic hurdles or financial challenges. Prosperity with a question mark While Germany continues to be an attractive destination for foreigners both in and outside the European Union, as well as asylum applicants, it remains to be seen how current events might influence these patterns, whether the number of immigrants arriving from certain countries will shift. Europe’s largest economy is suffering. Climbing inflation levels in the last few months, as well as remaining difficulties from the ongoing coronavirus (COVID-19) pandemic are affecting global economic development. Ultimately, future immigrants may face the fact of moving from one struggling economy to another.

Annual number of international migrants by 5-year age groups and gender for Canada, provinces and territories.

Canada’s appeal as an immigration destination has been increasing over the past two decades, with a total of 464,265 people immigrating to the country in 2024. This figure is an increase from 2000-2001, when approximately 252,527 immigrants came to Canada. Immigration to the Great White North Between July 1, 2022 and June 30, 2023, there were an estimated 199,297 immigrants to Ontario, making it the most popular immigration destination out of any province. While the number of immigrants has been increasing over the years, in 2024 over half of surveyed Canadians believed that there were too many immigrants in the country. However, in 2017, the Canadian government announced its aim to significantly increase the number of permanent residents to Canada in order to combat an aging workforce and the decline of working-age adults. Profiles of immigrants to Canada The gender of immigrants to Canada in 2023 was just about an even split, with 234,279 male immigrants and 234,538 female immigrants. In addition, most foreign-born individuals in Canada came from India, followed by China and the Philippines. The United States was the fifth most common origin country for foreign-born residents in Canada.

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.

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.

In January 2022, it was estimated that about 1.85 million male illegal immigrants living in the United States were aged between 35 and 44 years old. In that same year, it was estimated that 1.52 million female illegal immigrants living in the U.S. were between 35 and 44 years old.

BACKGROUND:

The diamondback moth (DBM), Plutella xylostella (Lepidoptera: Plutellidae), is a notorious pest of cruciferous plants. In temperate areas, annual populations of DBM originate from adult migrants. However, the source populations and migration trajectories of immigrants remain unclear. Here, we investigated migration trajectories of DBM in China with genome-wide single nucleotide polymorphisms (SNPs) genotyped using double-digest RAD (ddRAD) sequencing. We first analyzed patterns of spatial and temporal genetic structure among southern source and northern recipient populations, then inferred migration trajectories into northern regions using discriminant analysis of principal components (DAPC), assignment tests and spatial kinship patterns.

RESULTS:

Temporal genetic differentiation among populations was low, indicating sources of recipient populations and migration trajectories are stable. Spatial genetic structure indicated three genetic clusters in the southern source populations. Assignment tests linked northern populations to the Sichuan cluster, and central-eastern populations to the South and Yunnan clusters, indicating that Sichuan populations are sources of northern immigrants and South and Yunnan populations are sources of central-eastern populations. First-order (full-sib) and second-order (half-sib) kin pairs were always found within populations, but about 35-40% of third-order (cousin) pairs were found in different populations. Closely related individuals in different populations were in about 35-40% of cases found at distances of 900 to 1500 km, while some were separated by over 2000 km.

CONCLUSION:

This study unravels seasonal migration patterns in the DBM. We demonstrate how careful sampling and population genomic analyses can be combined to help understand cryptic migration patterns in insects.

Methods Specimen collection and DNA extraction DBM were sampled from potential source population locations in the annual breeding area of southern China. DBM were collected from cabbage and oilseed rape fields, and all sampling was completed before the first observations of DBM in northern China between March and May 1, 2. In order to reduce the likelihood of sampling siblings within populations, third- and fourth-instar larvae of DBM were collected from about 20 sites at each sampling location, each at least 10 m apart. Putative immigrant male adults were collected in northern China by sex pheromone trapping before the presence of first-generation larvae. Trapping of male DBM was conducted in unplanted fields with no greenhouses within 500 m, to reduce the likelihood of trapping individuals overwintering in protected conditions. The distance between traps was at least 50 m. The development of one generation of DBM takes about 30 days in early spring 3. This strategy therefore restricted sampling of genetically related individuals to within three generations between source and recipient populations, and reduced the influence of genomic admixture between immigrants from different sources. This sampling was conducted in 2017 and again in 2018, to examine annual variation in migratory trajectories and temporal variation in population genetic structure. In total, samples were collected from 16 locations in 2017 and 17 locations in 2018, and in 2018 four locations were sampled across multiple months (Fig. 1, Table 1). Twenty individuals from each population (specimens collected at different times from the same location were considered as different populations) were used for genotyping. Genomic DNA for library preparation was extracted from individual specimens using DNeasy Blood and Tissue Kit (Qiagen, Germany). SNP genotyping The ddRAD libraries were prepared following a published protocol 4 for identifying SNPs. Briefly, 120 ng of extracted genomic DNA from each sample was digested by the restriction enzymes NlaIII and AciI (New England Biolabs, USA) 5. The 50 μL digestion reaction was run for 3 hours at 37 °C, followed by DNA cleaning using 1.5× volume of AMPure XP beads (Beckman Coulter, USA) instead of a heat kill step. Next, we ligated each sample to adapters barcoded with a combinatorial index at 16 °C overnight in a 40 μL ligation reaction, labeling each population with a 6-bp index and each individual with a unique 9-bp barcode. After ligation, we pooled uniquely barcoded samples into multiplexed libraries. Fragments between 380-540 bp were selected using BluePippin and a 2% gel cassette (Sage Sciences, USA). Finally, the pooled libraries were enriched with 12 amplification cycles on a Mastercycler Nexus Thermal Cycler (Eppendorf, Germany). PCR products were cleaned with 0.8× volume of beads. We used Qubit 3.0 (Life Invitrogen, USA) and Agilent 2100 Bioanalyzer (Agilent Technology, USA) to check the concentration and size distribution of enriched libraries, respectively. Pooled libraries were sequenced on an Illumina HiSeq 2500 platform to obtain 150-bp paired-end reads, at BerryGenomics Company (Beijing, China). The Stacks v2.3 pipeline 6 was used to call SNPs, linking to the DBM genome (GenBank assembly accession: GCA_000330985.1) as reference 7. FastQC v 0.11.5 was employed to assess read quality and check for adapter contamination 8. Sequence data was demultiplexed and trimmed using process_radtags in Stacks v2.3 6, 9. Low quality reads with a Phred score below 20 were removed as well as any reads with an uncalled base. Reads were trimmed to 140 bp in length. The remaining paired-end reads were aligned to the DBM genome 7 using Bowtie v2.3.5 10. Output reads for all individuals were imported into Stacks pipeline ref_map.pl to call SNPs, requiring a minimum of three identical reads to create a stack. SNPs were called using a maximum likelihood statistical model. Finally, we obtained a catalog with all possible loci and alleles. The exported loci were present in all populations, and in at least 75% of individuals per population. The exported SNPs for populations that were collected in both years were further filtered using the R package vcfR 11 and VCFtools v0.1.16 12 with the following criteria: SNPs with sequencing depth ≤ 3 and in the highest 0.1% depth were removed, as were SNPs with missingness in all samples ≥ 0.05 and those with minimum minor allele count ≤ 20. An additional data matrix was generated by retaining only SNPs separated by at least 500 bp, to reduce linkage among SNPs. Genetic diversity, population structure and assignment tests Global population differentiation was estimated using Weir and Cockerham’s FST with 99% confidence intervals (1000 bootstraps) in diveRsity version 1.9.90. Pairwise FST for all population pairs was estimated using GenePop version 4.7.2 13. Discriminant analysis of principal components (DAPC) was performed in the R package adegenet v2.1.1 14, with the optimal number of clusters determined by the Akaike information criterion (AIC). Assignment tests were performed in assignPOP v1.1.7 15. Source groups of ST (south) and SW (southwest, this group was divided into YN and SC groups in 2018) (see Table 1 and Fig. 1 for locations) were trained using the support vector machine algorithm to build predictive models. For training, we used either 25, 28, or 32 random individuals (2017 samples) or 13, 15 or 17 random individuals (2018 samples) from each group, and loci with the highest 60%, 80% or 100% FST values. Monte-Carlo cross-validation was performed by resampling each training set combination 1000 times. The ratio of assignment probability between the most-likely and second most-likely assigned groups was calculated for each individual 16. When an individual showed an assignment ratio smaller than 2 in more than 30% of the resampling analysis, it was considered unstable and removed in subsequent training. This allowed us to remove individuals from source populations that are not similar enough to other individuals in that source population, thus leaving a set of source populations each comprised of individuals distinctive from those in other populations. Immigrants from the CE (central) and NT (north) regions (see Table 1 and Fig. 1 for locations) were assigned to the trained groups using the support vector machine algorithm. Kinship analysis As a complement to assignment tests (but focusing on the individual level rather than the population level), we investigated spatial patterns of kinship within and between populations. Related individuals were identified following the method of Jasper, Schmidt, Ahmad, Sinkins and Hoffmann 17. First, Loiselle’s K was calculated for all individual pairs using SPAGeDi 18 . Kinship coefficients represent the probability that any allele scored in both individuals is identical by descent, with theoretical mean K values for each kinship category as follows: full‐siblings = 0.25, half-siblings = 0.125, full‐cousins = 0.0625, half‐cousins = 0.0313, second-cousins = 0.0156 and unrelated = 0. To allocate pairs of individuals to relatedness categories across three orders of kinship, maximum‐likelihood estimation in the program ML‐Relate 19 was used to identify first‐order (full‐sibling) and second‐order (half‐sibling) pairs. The K scores of pairs within the full‐sibling and half-sibling data sets were used to calculate standard deviations for these categories. Using the theoretical means and standard deviations of K, we randomly sampled 100,000 simulated K scores from each kinship category. In the initial pool of 40755 pairings (2017) and 89676 pairings (2018), ML‐Relate identified 33 (2017) and 36 (2018) full‐sibling and half‐sibling pairs. Assuming that the data contained twice as many first cousin (full and half) pairings as sibling (full and half) pairings, and twice as many second cousin pairings as first cousin pairings, final sampling distributions were developed as follows: 100,000 unrelated, 320 second-cousins, 80 full‐cousins, 80 half‐cousins, 40

Class of worker by visible minority, selected sociodemographic characteristics and the census year: Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts (1) Frequency: Occasional Table: 98-10-0645-01 Release date: 2024-03-26 Geography: Canada, Geographical region of Canada, Province or territory, Census metropolitan area, Census metropolitan area part Universe: Persons in private households in occupied private dwellings, 2021 and 2016 censuses — 25% Sample data Variable List: Class of worker, Gender (2), Age (3) and first official language spoken (4), Immigrant and generation status (5, 6), Visible minority (7), Highest certificate, diploma or degree, Percent, Census year Abbreviation notes: List of abbreviations and acronyms found within various Census products. (https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm) Footnotes: 1 Historical comparison of geographic areas The boundaries and names of census geographies can change from one census to the next. In order to facilitate data comparisons between censuses, previous census data have been adjusted to reflect as closely as possible the 2021 boundaries of these areas. The methodology used for this adjustment involved spatially linking blocks of previous censuses (concordance to the 1996 Census used the 1996 enumeration areas to the 2021 boundaries). A previous census block was linked to the 2021 area within which its representative point fell. A limited number of interactive linkages were completed to further enhance the adjustment in certain areas. For some census geographies, it was not possible to reflect the 2021 boundaries. The 2021 boundaries may not be reflected as there was no previous census block to assign to the 2021 area. As well previous census data for some 2021 areas may not be available due to the fact that the concordance did not produce an accurate representation of the 2021 area. 2 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. The sex variable in census years prior to 2021 and the two-category gender variable in the 2021 Census are included together. Although sex and gender refer to two different concepts, the introduction of gender is not expected to have a significant impact on data analysis and historical comparability, given the small size of the transgender and non-binary populations. For additional information on changes of concepts over time, please consult the Age, Sex at Birth and Gender Reference Guide. 3 Age 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date). 4 First official language spoken First official language spoken refers to the first official language (English or French) spoken by the person. 5 'Immigrant status' refers to whether the person is a non-immigrant, an immigrant or a non-permanent resident. 'Period of immigration' refers to the period in which the immigrant first obtained landed immigrant or permanent resident status. For more information on immigration variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021. 6 Generation status Generation status refers to whether or not the person or the person's parents were born in Canada. 7 Visible minority Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. 8 Class of worker Class of worker refers to whether a person is an employee or is self-employed. The self-employed include persons with or without a business, as well as unpaid family workers. 9 'High (secondary) school diploma or equivalency certificate' includes only people who have this as their highest educational credential. It excludes persons with a postsecondary certificate, diploma or degree. 10 Includes persons aged 15 years and over who have worked at some point in time during the reference period. In 2021, this period was between January 2020 and May 2021. 11 Includes self-employed persons aged 15 years and over with or without an incorporated business and with or without paid help, as well as unpaid family workers. 13 Includes self-employed persons whose business is incorporated with or without employees. 14 Includes self-employed persons whose business is unincorporated. Also included among the self-employed are unpaid family workers. This category includes persons who work without pay in a business, farm or professional practice owned and operated by another family member living in the same dwelling.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

Overview of educational characteristics of visible minority groups in Canada, provinces, territories and cities, with percent distribution of highest certificate, diploma or degree.

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.

Employment income (in 2019 and 2020) of visible minority groups by educational characteristics, for cities.

Resettled refugees who arrived in Canada as part of the Government of Canada's Refugee and Humanitarian Resettlement Program. Datasets include resettled refugees who have received settlement services. 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 data collection contains population projections for UK ethnic groups and all local area by age (single year of age up to 100+) and sex. Included in the data set are also input data to the cohort component model that was used to project populations into the future-fertility rates, mortality rates, international migration flows and internal migration probabilities. Also included in data set are output data: Number of deaths, births and internal migrants. All data included are for the years 2011 to 2061. We have produced two ethnic population projections for UK local authorities, based on information on 2011 Census ethnic populations and 2010-2011-2012 ethnic components. Both projections align fertility and mortality assumptions to ONS assumptions. Where they differ is in the migration assumptions. In LEEDS L1 we employ internal migration rates for 2001 to 2011, including periods of boom and bust. We use a new assumption about international migration anticipating that the UK may leave the EU (BREXIT). In LEEDS L2 we use average internal migration rates for the 5 year period 2006-11 and the official international migration flow assumptions with a long term balance of +185 thousand per annum.

This project aims to understand and to forecast the ethnic transition in the United Kingdom's population at national and sub-national levels. The ethnic transition is the change in population composition from one dominated by the White British to much greater diversity. In the decade 2001-2011 the UK population grew strongly as a result of high immigration, increased fertility and reduced mortality. Both the Office for National Statistics (ONS) and Leeds University estimated the growth or decline in the sixteen ethnic groups making up the UK's population in 2001. The 2011 Census results revealed that both teams had over-estimated the growth of the White British population and under-estimated the growth of the ethnic minority populations. The wide variation between our local authority projected populations in 2011 and the Census suggested inaccurate forecasting of internal migration. We propose to develop, working closely with ONS as our first external partner, fresh estimates of mid-year ethnic populations and their components of change using new data on the later years of the decade and new methods to ensure the estimates agree in 2011 with the Census. This will involve using population accounting theory and an adjustment technique known as iterative proportional fitting to generate a fully consistent set of ethnic population estimates between 2001 and 2011.

We will study, at national and local scales, the development of demographic rates for ethnic group populations (fertility, mortality, internal migration and international migration). The ten year time series of component summary indicators and age-specific rates will provide a basis for modelling future assumptions for projections. We will, in our main projection, align the assumptions to the ONS 2012-based principal projection. The national assumptions will need conversion to ethnic groups and to local scale. The ten years of revised ethnic-specific component rates will enable us to study the relationships between national and local demographic trends. In addition, we will analyse a consistent time series of local authority internal migration. We cannot be sure, at this stage, how the national-local relationships for each ethnic group will be modelled but we will be able to test our models using the time series.

Of course, all future projections of the population are uncertain. We will therefore work to measure the uncertainty of component rates. The error distributions can be used to construct probability distributions of future populations via stochastic projections so that we can define confidence intervals around our projections. Users of projections are always interested in the impact of the component assumptions on future populations. We will run a set of reference projections to estimate the magnitude and direction of impact of international migrations assumptions (net effect of immigration less emigration), of internal migration assumptions (the net effect of in-migration less out-migration), of fertility assumptions compared with replacement level, of mortality assumptions compared with no change and finally the effect of the initial age distribution (i.e. demographic potential).

The outputs from the project will be a set of technical reports on each aspect of the research, journal papers submitted for peer review and a database of projection inputs and outputs available to users via the web. The demographic inputs will be subject to quality assurance by Edge Analytics, our second external partner. They will also help in disseminating these inputs to local government users who want to use them in their own ethnic projections. In sum, the project will show how a wide range of secondary data sources can be used in theoretically refined demographic...

In 2022, the largest foreign group of immigrants to the Netherlands came from Ukraine, with 99,700 immigrants. Polish, Dutch, Syrian and Turkish rounded out the top five foreign nationalities for immigrants to the Netherlands in that year.

Temporary residents who are in Canada on a study permit in the observed calendar year. Datasets include study permit holders by year in which permit(s) became effective or with a valid permit in a calendar year or on December 31st. 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.

In 2023, around 1.93 million people immigrated to Germany. Numbers fluctuated during the time period covered in the graph at hand, peaking in 2015 during the high point of Europe’s refugee crisis. Significantly lower figures in 2020 may be attributed to the first year of the coronavirus (COVID-19) pandemic, and subsequent restrictions implemented by the German government on entering the country, in order to control the spread of the disease. Immigration to Germany “Immigrant” is a term used from the point of view of the receiving country, or the country being migrated to by a person. While reasons for and circumstances leading to an immigrant entering a foreign country may vary, they often include love, include seeking residence, employment, family reunions, or applying for asylum. Various countries are represented among foreigners living in Germany, though currently the leading three by numbers are Turkey, Ukraine, and Syria. Around 5.2 million immigrants living in Germany do not need a residence permit due to having EU citizenship, and therefore being allowed freedom of movement based on EU law. Another 2.64 million immigrants were granted an unlimited permit to stay in Germany. The near future Germany remains a popular choice for immigrants, even in currently challenging economic and political times. Welfare benefits, healthcare, and various support initiatives for those moving to or arriving in the country are on the list of selling points, though in practice, difficulties may be encountered depending on individual situations and laws in different German federal states. While the unemployment rate among foreigners living in Germany had gone up in 2020, it dropped again in the following years, but increased once more in 2023 and 2024 to over 16 percent. The country is Europe’s largest economy, housing many global players in various industries, which continues to attract jobseekers, despite these very industries facing struggles of their own brought on both by the ongoing coronavirus pandemic and geopolitical events in Europe.

Data on resident buyers who are persons that purchased a residential property in a market sale and filed their T1 tax return form: number of and incomes of residential property buyers, sale price, price-to-income ratio by the number of buyers as part of a sale, age groups, first-time home buyer status, buyer characteristics (sex, family type, immigration status, period of immigration, admission category).

People born in Syria made up the largest group of Sweden's foreign-born population in 2023. Nearly 200,000 people born in Syria lived in Sweden as of 2023. Iraqi made up the second largest group of foreign-born citizens, followed by Sweden's neighboring country Finland. The total number of foreign-born citizens living in the Scandinavian country increased over the past 10 years.

Migration contributes to population growth

Sweden's positive net migration rate meant that it's population increased steadily since 2000. In 2022, over 100,000 people immigrated to Sweden, which was still significantly lower than the record year 2016.

Syrians fleeing civil war

The record number of refugees arriving in 2016 was driven by Syrians fleeing the Civil War in the country. Following the Arab spring and protests for democracy in 2011, fighting broke out between the Syrian national army and several armed factions. Several million people fled the country as a result, some of them seeking refuge in Sweden.