Estimates for UK immigration, emigration and net migration, year ending June 2012 to year ending December 2024. These are official statistics in development. To access the most up-to-date data for each time period, please use the most recently published dataset.

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

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

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

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

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

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

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

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

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

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

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.

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.

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 ...).

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2020, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

This database automatically includes metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Immigrants aged 15 or more by activity status, citizenship, age groups and sex, Slovenia, annually”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

Please Note: As announced by the Minister for Immigration and Border Protection on 25 June 2017, the Department of Immigration and Border Protection (DIBP) retired the paper-based Outgoing Passenger Cards (OPC) from 1 July 2017. The information previously gathered via paper-based outgoing passenger cards is now be collated from existing government data and will continue to be provided to users. Further information can be accessed here: http://www.minister.border.gov.au/peterdutton/Pages/removal-of-the-outgoing-passenger-card-jun17.aspx.

Due to the retirement of the OPC, the Australian Bureau of Statistics (ABS) undertook a review of the OAD data based on a new methodology. Further information on this revised methodology is available at: http://www.abs.gov.au/AUSSTATS/abs@.nsf/Previousproducts/3401.0Appendix2Jul%202017?opendocument&tabname=Notes&prodno=3401.0&issue=Jul%202017&num=&view=

A sampling methodology has been applied to this dataset. This method means that data will not replicate, exactly, data released by the ABS, but the differences should be negligible.

Due to ‘Return to Source’ limitations, data supplied to ABS from non-DIPB sources are also excluded.

Overseas Arrivals and Departures (OAD) data refers to the arrival and departure of Australian residents or overseas visitors, through Australian airports and sea ports, which have been recorded on incoming or outgoing passenger cards. OAD data describes the number of movements of travellers rather than the number of travellers. That is, multiple movements of individual persons during a given reference period are all counted. OAD data will differ from data derived from other sources, such as Migration Program Outcomes, Settlement Database or Visa Grant information. Travellers granted a visa in one year may not arrive until the following year, or may not travel to Australia at all. Some visas permit multiple entries to Australia, so travellers may enter Australia more than once on a visa. Settler Arrivals includes New Zealand citizens and other non-program settlers not included on the Settlement Database. The Settlement Database includes onshore processed grants not included in Settler Arrivals.

These de-identified statistics are periodically checked for privacy and other compliance requirements. The statistics were temporarily removed in March 2024 in response to a question about privacy within the emerging technological environment. Following a thorough review and risk assessment, the Department of Home Affairs has republished the dataset.

Annual number of interprovincial migrants by province of origin and destination, Canada, provinces and territories.

Abstract of associated article: Immigrants are newcomers in a labor market. As a consequence, they lack host-country-specific labor market knowledge and other country-specific and not directly productive valuable assets affecting their relative bargaining position with employers. We introduce this simple observation into a search and matching model of the labor market and show that immigrants increase the employment prospects of competing natives. To test the predictions of our model, we exploit yearly variations between 1998 and 2004 in the share of immigrants within occupations in 13 European countries. We identify the impact of immigrants on natives׳ employment rate using an instrumental variable strategy based on historical settlement patterns across host countries and occupations by origin country. We find that natives׳ employment rate increases in occupations and sectors receiving more immigrants. Moreover, we show that this effect varies depending on immigrants׳ characteristics and on host country labor market institutions which affect relative reservation wages.

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards: Immigrants, Refugees, Muslims, Hispanics, Venezuelans News Media Consumption Trust in News Media and Societal Institutions Frequency and Valence of Intergroup Contact Realistic and Symbolic Intergroup Threat Right-wing Authoritarianism Social Dominance Orientation Political Efficacy Personality Characteristics Perceived COVID-threat, and Socio-demographic Characteristics For the adult population aged 25 to 65 in seven European countries: Austria Belgium Germany Hungary Italy Spain Sweden And for ages ranged from 18 to 65 for: United States of America Colombia The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

Datasets for: Winter, K., Scholl, A., & Sassenberg, K. (2022). Flexible minds make more moderate views: Subtractive counterfactuals mitigate strong views about immigrants’ trustworthiness. Group Processes & Intergroup Relations. https://doi.org/10.1177/13684302221102876 Public discourse on immigration has seemed to polarize over recent years—with some people strongly trusting, but others strongly distrusting immigrants. We examined whether a cognitive strategy could mitigate these biased outgroup judgments. Given that subtractive counterfactual thoughts (“If only I had not done X. . .”) facilitate cognitive flexibility and especially a relational processing style, we hypothesized that these thoughts (vs. additive counterfactuals “If only I had done X. . .” and no counterfactuals) would weaken the relationship between people’s political orientation and the perceived trustworthiness of immigrants. In five experiments (two preregistered; total N = 1,189), we found that inducing subtractive (but not additive) counterfactuals—either via rhetorical questions in a political speech or via mindset priming—had the predicted debiasing effect. Taken together, subtle means such as using subtractive counterfactual questions in political communication seem to be a promising way to reduce biased outgroup judgments in heated public debates.:

This table contains 15 series, with data for years 1946 - 2004 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...).

Acknowledgement

The following table is imported from the 2019 Yearbook of Immigration Statistics under the Department of Homeland Security:

The 2019 Yearbook of Immigration Statistics is a compendium of tables that provide data on foreign nationals who are granted lawful permanent residence (i.e., immigrants who receive a “green card”), admitted as temporary nonimmigrants, granted asylum or refugee status, or are naturalized. The Yearbook also presents data on immigration enforcement actions, including apprehensions and arrests, removals, and returns.

Table 39. Aliens Removed or Returned: Fiscal Years 1892 to 2019 (https://www.dhs.gov/immigration-statistics/yearbook/2019/table39)

Inspiration

The data was collected to observe trends in history reflecting the number of immigrants deported - more specifically removed or returned.

Age influences behaviour, survival, and reproduction; hence variation in population age structure can affect population-level processes. The extent of spatial age structure may be important in driving spatially-variable demography, particularly when space-use is linked to reproduction, yet it is not well understood. We use long-term data from a wild bird population to quantify covariance between territory quality and age and examine spatial age structure. We find associations between age and aspects of territory quality, but little evidence for spatial age structure compared to the spatial structure of territory quality and reproductive output. We also report little between-year repeatability of spatial age structure compared to structure in reproductive output. We suggest that high breeding site fidelity among individuals that survive between years, yet frequent territory turnover driven by high mortality and immigration rates, limits the association between age and territory quality and weakens overall spatial age structure. Greater spatial structure and repeatability in reproductive output compared to age suggests that habitat quality may be more important in driving spatially-variable demography than age in this system. We suggest that the framework developed here can be used in other taxa to assess spatial age structure, particularly in longer-lived species where we predict from our findings there may be greater structure. Methods (i) Study system and data collection The great tit Parus major is a passerine bird found in woodlands across Europe, with breeding ages ranging 1–9, averaging 1.8 years [1–3]. Although there are some continuous changes with age [1], the main age effects on individual-level traits are captured by two age-classes: first-years (hereafter yearlings) and older (hereafter adults [2–6]). The species is socially monogamous, with pairs defending territories during annual breeding seasons [7]. Data used here are from a long-term study in Wytham Woods, Oxford (51°46’N, 1°20’W), a 385ha mixed deciduous woodland surrounded by farmland [8]. The tit population has been monitored since 1947, where breeding adults and their chicks have been marked with unique BTO (British Trust for Ornithology) rings since the 1960s; and standard reproductive metrics are collected [9]. Individuals breed almost exclusively in the 1026 nest-boxes which are in fixed positions with known GPS coordinates [10,11]. All chicks are ringed at 14-days of age, while adults are trapped at nest-boxes and identified by ring number, or marked with a new ring if they are immigrants. Age is based on year of hatching for local birds, or plumage characteristics for immigrants [12]. Although immigration rates are high (46%), most are first caught as yearlings (78%) and can therefore be aged accurately. (ii) Data selection We constructed a dataset that assigns the year of hatching to all individuals between 1950–2022, across which exact age was calculated for 88.8% of 46062 identified breeding individuals. In this study, we included birds in analyses that attempted to breed between 1978–2022, for which data were more complete compared to earlier dates. Individuals that were first caught post-fledging are assumed to be immigrants, as locally-hatched tits are marked as nestlings in nest-boxes and the proportion of birds hatched in natural cavities is very low [13]. Immigrants that entered the population with adult plumage were assigned a minimum age of 2, and subsequent age estimates were based on this (6.7% and 10.0% of breeding females and males). Age was therefore determined for 68.7% of breeding individuals where at least one egg was laid (due to a combination of nests failing prior to adult trapping and unsuccessful trapping attempts, there are cases where the identity of parents is unknown). (iii) Statistical analyses Determining breeding territories We defined annual breeding territories through a Dirichlet tessellation technique that forms Thiessen polygons [14,15] around each occupied nest-box. The polygon includes all space within the habitat that is closer to the focal box than any other (with a boundary also imposed by the woodland edge). This metric of territory has been shown to be biologically meaningful in terms of territory size and territorial neighbours in tit species and is strongly related to other methods of calculating territories [10,16–19]. However, a limitation is that unrealistically large polygons are formed in areas where nest-boxes are placed at great distances from each other. We therefore capped territories at 2ha, which is a more realistic maximum spatial scale at which individuals use territories, as supported in previous analytical and field studies [10,11,20,21]. Age and territory quality We first assessed covariance between the age of individuals and their territories’ quality. We measured territory quality through four measures: the number of oak trees Quercus spp. within 75m of the nest-box; average territory density; the edge distance index; and the long-term nest-box popularity index. Each of these is justified below. Great tits predominantly provision offspring with caterpillars collected close to their nests [3], thus variation in caterpillar availability is directly linked to reproductive success [22–24]. Caterpillars are found most abundantly on oak trees [3,22], therefore oak proximity, health and abundance is important for breeding success [10,18,25,26]. A radius of 75m was chosen as the abundance of oaks within this distance has been shown to be particularly important for breeding [27,28]. The density of conspecifics breeding in proximity may influence resource availability if foraging ranges overlap, and therefore territory density may also represent an aspect of territory quality. Additionally, territory density may affect site quality through social mechanisms, such as increased competition and emergent need for territory defence leading to reduced foraging [29], or conversely mutual benefits between familiar neighbours [18,30,31]. We calculated average territory density directly from the Thiessen polygon area produced from tessellation by taking the reciprocal of the mean polygon area. Territories at woodland edges are associated with lower reproductive success in great tits [21]. Following Wilkin et al. (2007) [21], we defined the edge distance index (EDI) for each nest-box by multiplying the distance to forest edge by the proportion of woodland habitat within a 75m radius of the box. Thus, boxes within 75m of the edge have an EDI value in proportion to the amount of woodland habitat within this radius, therefore considering not only the distance to edge, but also the number and geometric arrangement of edges relative to nest-box. Finally, the frequency a territory is occupied in the long-term may provide a measure of quality as individuals may choose sites that confer reproductive benefits more often, as evidenced in other species [32–36]. There is evidence of this in Wytham, where the number of times a nest-box has been occupied positively correlates with the average number of offspring that fledge per breeding attempt. We therefore calculated the frequency of occupancy of each nest-box independent of individual breeding site fidelity by calculating the number of times a nest-box has been occupied since 1965 by a new breeding individual (i.e. attempts where either the female or male had previously used the same box were removed). However, nest-box occupation frequency is related to nest-box density, because in areas of high density there are multiple unoccupied boxes which would likely be associated with the same territorial range if they were occupied (thus, in regions of high box density, birds may re-occupy the same territory over multiple years, but not necessarily the exact same box). To correct for this, we ran a linear model between the number of boxes within 30m of a focal nest-box and the number of times said box has been occupied by a new breeding individual, and took the residuals as the long-term nest-box popularity index. We constructed a generalised linear mixed-effects model assuming a binomial error distribution to analyse the association between these four measures of territory quality and the age of the breeding individual. We modelled age (juvenile/adult) as the response variable, with the territory quality measures as explanatory variables, which were z-transformed to compare their relative effects in predicting age. Individual ID, nest-box ID, and breeding year were included as random effects. We ran three sets of these models: one with all individuals; one with only females; and one with only males, allowing us to assess potential sex-specific differences in the association between aspects of territory quality and age. All models were conducted in R statistical software [37] using the lme4 package [38]. Spatial age structure For each year’s breeding population, we constructed an individual-by-individual matrix denoting breeding neighbours i.e. a network of breeding territories, where nodes represent individuals and edges represent the spatial connectivity of territories. Specifically, edges connected individuals if their territories share a boundary from the tessellation technique and were weighted relative to the distance between nest-boxes of neighbouring territories. We created three networks per year: one with all individuals (but removing edges within breeding pairs that occupy the same territory); one with only females; and one with only males. Edges connecting individuals of unknown identity were removed. Across these networks, we calculated the assortativity coefficient of age (juvenile/adult), which measures the correlation between individuals’ age and that of their territorial neighbours accounting for edge weight (proximity of neighbouring nest-boxes) and the relative proportion of the two age classes across the

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.