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.

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:

1. Immigrants, Refugees, Muslims, Hispanics, Venezuelans
2. News Media Consumption
3. Trust in News Media and Societal Institutions
4. Frequency and Valence of Intergroup Contact
5. Realistic and Symbolic Intergroup Threat
6. Right-wing Authoritarianism
7. Social Dominance Orientation
8. Political Efficacy
9. Personality Characteristics
10. Perceived COVID-threat, and
11. Socio-demographic Characteristics

1. Austria
2. Belgium
3. Germany
4. Hungary
5. Italy
6. Spain
7. Sweden

1. United States of America
2. 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.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

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, nationality, country of previous residence 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.

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

United States Immigrants Admitted: United Kingdom data was reported at 10,948.000 Person in 2017. This records a decrease from the previous number of 12,673.000 Person for 2016. United States Immigrants Admitted: United Kingdom data is updated yearly, averaging 13,552.000 Person from Sep 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 19,973.000 Person in 1992 and a record low of 7,647.000 Person in 1999. United States Immigrants Admitted: United Kingdom data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s USA – Table US.G087: Immigration.

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 ...).

United States Immigrants Admitted: Ethiopia data was reported at 14,637.000 Person in 2017. This records an increase from the previous number of 13,232.000 Person for 2016. United States Immigrants Admitted: Ethiopia data is updated yearly, averaging 6,359.500 Person from Sep 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 16,152.000 Person in 2006 and a record low of 2,156.000 Person in 1987. United States Immigrants Admitted: Ethiopia 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.

The project aims at providing the data required to study the descriptive representation of citizens of immigrant origin (CIOs). The main aim is to provide an overview of the social and political profile of Member of Parliament (MPs), with a particular focus on identifying MPs of immigrant origin. In addition to the national level dataset described below, a corresponding regional level dataset is available.

Identification variables: Political level (regional, national); country-ID (NUTS); name of region; region-id (NUTS); date of relevant election; full name of district in which elected; level of electoral tier (first / Lower (or single tier); identifier for tier 1 to 3 districts at national level; number of legislatures in the country, as recorded by the parliament itself; date in which the legislature begins and ends; first name, first (second) surname of MP; MP-ID; national MP is also simultaneously a regional MP; which regional MP.

Demography: sex of MP; year of birth of MP; highest level of education (ISCED 1997); last occupation /profession of the MP before first ever becoming an MP (ISCO 2008); occupation sector when first elected; current occupation/ profession of the MP (ISCO 2008); current occupation sector.

Electoral and parliamentary tenure variables: number of times the MP has been previously elected to parliament in this district; type of electoral district; number of times the MP has been previously elected to parliament in this tier; Rookie: MP elected for the first time in this term; number of times the MP has been elected to parliament; number of times the MP has taken up the seat in parliament once elected; year when the MP was first elected to national/regional parliament; total number of years spent in national/regional parliament as MP, prior to this legislature (seniority); when was the MP elected for the last time prior to this legislature (continuity); MP was elected to chamber from inauguration; MP stayed continuously with no interruptions from the moment of taking up the seat until the end of the legislative term; number of months the MP did serve (if he did not serve a full legislative term); MP came back to reclaim the seat if MP left seat at some point; position in party list; rank position in which the MP was elected in district; double candidacy in another tier; MP won seat as incumbent, or as contender; parliamentary group the MP joined at the beginning and at the end of his/her term; full name and acronym of party or list in which elected; party code according to the CMP (Comparative Manifesto Project) dataset; party-ID.

Immigrant origin variables (corresponding coding for MPs mother and father): MP was born in the country of parliament; country (ISO 3166-1), world region (UN Classification for ‘Composition of macro geographical regions’), and country region (NUTS) in which the MP was born; data sources for country of birth (e.g. official parliamentary source, personal blogs, etc.); specific sources for country of birth; reliability of the data regarding the country of birth of the MP (as judged by the coder); year of immigration; born as a national citizen of the country of parliament; country of nationality at birth; data sources country of nationality at birth; specific sources for country of citizenship at birth; reliability of the data regarding citizenship at birth; year in which naturalized as a citizen; data sources year of naturalization; specific sources for date of naturalization; reliability of the data regarding naturalization.

Variables relating to aspects potentially related to discrimination: the MP is a native speaker of an official country language and data sources; specific sources for native language of MP; MP can be perceived by voters as a member of an ‘identifiable’ minority; source where picture found; specific sources for picture of MP; does the MP self-identify as a member of an ethnic minority; ethnicity; sources and specific sources for information on ethnic self-identification of MP; self-identification as a member of a certain religion; religion the MP identifies with.

Party career and committee membership variables: year in which the MP joined the party for which she/he was elected in this legislative term; highest position within the party; MP changed party affiliation during the legislative term; date of change; full name and party acronym of the new party joined, CMP code of the new party and Pathways identifier for party; (corresponding coding if the MP changed party affiliation during the legislative term a second time); ever a local councilor or mayor prior to, or while, being elected an MP this legislative term; total number of years in the local council as a mayor and/or councilor; ever a member of the regional parliament prior to being elected a national MP this legislative term; number of years in the regional parliament prior to this legislature; ever a member of the European Parliament prior to being elected a...

Name searchable index to Series S5631 Card Register of Immigrants and Missing Immigrants. This series is a card register of immigrants and missing immigrants dealt with in various correspondence although most cards contain very little information. The cards mainly register the administrative file movement/s of the Immigration Department's correspondence regarding a particular immigrant, but may include the name of the immigrant/missing immigrant, a file number, i.e. 269/24, name of ship, date of arrival, movements of File (dates and file numbers), the name of (correspondence) writer, subject and action. Some cards contain annotations such as "file returned to (other state)" or details concerning an immigrant, for example, age at date of departure etc. There are stamp marks on many of the cards, for example, "Domestic", Decontrolled", "Final", "Decentralised", "Salvation Army", etc

United States Immigrants Admitted: Russia data was reported at 9,297.000 Person in 2016. This records an increase from the previous number of 8,799.000 Person for 2015. United States Immigrants Admitted: Russia data is updated yearly, averaging 12,079.000 Person from Sep 1992 (Median) to 2016, with 25 observations. The data reached an all-time high of 20,771.000 Person in 2002 and a record low of 6,718.000 Person in 2010. United States Immigrants Admitted: Russia data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s USA – Table US.G086: Immigration.

StatBank dataset: RAS206 Title: Immigrants 16-66 years (end November) by country of origin, length of residence, socioeconomic status, sex and age Period type: years Period format (time in data): yyyy The oldest period: 2008 The most recent period: 2023

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.

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

The dataset is derived from the "Level of living among immigrants 1996" survey, commisioned by the Ministry of Labour and Social Inclusion and conducted by Statistics Norway. The purpose of the survey is to map out the important aspects of the living conditions of different immigration groups and their descendants in Norway. A similar surveys was conducted in 1983 (the research foundation FAFO also carried out two relevant surveys in Oslo in 18993 and 1995), but only including immigrants that weren't Norwegian residents and first and second generatopm immigrants with Norwegian citizenship. In the 1996 survey, both foreign citizens and first and second generation immigrants of Norwegian citizenship were included.

The questions included in the survey covers issues such as residence and living conditions, household/family, children, proffession/working conditions, education, bullying/violence, friends/family, leisure activities and membership in different unions and organisations. In order to make it possible to compare the situation for the immigration population with the Norwegian population, a big part of the questionnaire is derived from the Level of Living survey 1995, the Living conditions survey 1995, the Level of Living for long-term unemployed 1991 and the level of living survey for foreign citizens 1983.

The survey comprises persons with background from former Yugoslavia, Turkey, Iran, Pakistan. Vietnam, Sri Lanka, Somalia and Chile.

The data from the survey are available on two different files, the interview file (N = 2561) and the household file ( N = 9548). The interview file contains the interview based data for immigrants that weren't interviewed, besides additional register data for the respondents, and in some cases for the persons in the respondents' households.

This is the interview file.

Additional variables: Some variables are derived from the income register. This register is composed by data from several sources: (1) Population statistics in SSB, (2) The Tax Register for Personal Tax Payers, (3) Norwegian State Educational Loan Fund, (4) Norwegian State Housing Bank living support register, (5) Social Aid Register, (6) GR1 (National Insurance Administration), (7) Children benefits (calculated for 1994), (8) Education register, (9) register on End of the Year Certificates (10) Tax Return Register. In addition, two variables are calculated (12) combined income and Disposable income.

The last letter (or the two last) in the variables on individual level indicates which part of the income register that is used. L- The Tax Register for Personal Tax Payers, U- Norwegian State Educational Loan Fund, H - Norwegian State Housing Bank. S - Social Aid Register, R- National Insurance Administration, B- Children benefits, T- register on End of the Year Certificates , SA- Tax return.

Also, the file contains variables that stems from (a) birth country file (situation pr 1.1.1996), (b) employee/employer register (1st quarter of 1996), (c) SOFA-applicant register(1st quarter 1996), and (d) education register (highest education in population 1.10.1994).

Abstract

As of July 2019, it is estimated that over 4,054,000 Venezuelans have left the country and approximately 168,357 have either requested asylum or temporary residency in Brazil, mainly in Roraima state and progressively in the city of Manaus in Amazonas state. Utilising an Area-Based Approach, REACH collected localised information on the situation of Venezuelan asylum seekers and migrants living in host communities and abrigos managed by humanitarian actors in city neighbourhoods across Boa Vista, Pacaraima and Manaus. The aim was to increase the understanding of humanitarian actors of the living conditions, primary needs, vulnerabilities and coping strategies of the asylum seekers and migrants. This study aims to provide a representative overview of the profiles of Venezuelan asylum seekers and migrants living in different geographic locations and shelter settings in Brazil, for the purpose of increasing the understanding of humanitarian actors as to the extent to which the living conditions, needs, and vulnerabilities of Venezuelan households vary between households living in abrigos and those living in host communities, across three cities that are relevant nodes in the Brazilian refugee response: Pacaraima, Boa Vista, and Manaus. The findings indicate that challenges related to accessing services are relatively similar across different locations and shelter settings. The findings indicate that challenges related to accessing services are relatively similar across different locations and shelter settings. Of all services, Venezuelans seem to face the most challenges regarding access to education; findings suggest that a lack of required documents and a limited local capacity are constraining the enrolment of Venezuelan children into local schools. These two factors were also the most likely to pose barriers to accessing social services and healthcare facilities. Difficulties in speaking the local language and long distances to facilities were found to further constrain households' access to services, albeit to a lesser extent.

Geographic coverage

Pacaraima, Boa Vista, and Manaus.

Analysis unit

Household

Universe

Households living in shelters.

Kind of data

Sample survey data [ssd]

Sampling procedure

A master list of households resident within each abrigo was requested from the relevant site manager. The requested dataset required the following fields:

• Tent location (Sector / Tent Number) to facilitate locating selected households;
• Household composition (number of household members, age, sex, focal point y/n);
• Individual and/or Group ID to facilitate secondary data verification to ProGress dataset (if necessary).

The dataset from each abrigo was merged into one master list. Each household within the master dataset was allocated with a consecutive number and households were selected using a random number generator. A total of 1119 households were interviewed.

Mode of data collection

Face-to-face interview

The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

Attitudes towards the topics of internal security, asylum, immigration and counter-terrorism. 1. Internal security: subjective feeling of security or concern about criminal attacks; assessment of relevant sources of fear of crime (e.g. through portrayals of violence on television, through the daily news about crime and criminality, own experiences of victimisation, etc.); concern about concrete crimes (attacks by Islamist terrorists, organised criminal gangs from abroad, young people´s propensity to violence, theft and assaults on the open street, break-ins and break-ins of cars, violence in the family, police assaults); assessment of policy efforts to ensure security; assessment of appropriate security measures (more visible presence of police and law enforcement officers, faster deportation of foreign criminals, law enforcement officers in public transport, video surveillance of public places and in public transport vehicles, tougher penalties, more intensive police checks on motorways, stronger internet surveillance, more social prevention projects for young people); assessment of stronger cooperation between different German and international security bodies (police authorities of the Federation and the federal states, police and intelligence services of the EU states, resp. police and intelligence services of Germany and the USA); attitude towards video surveillance in public places (feeling of security or rather of unease or both at the same time); attitude towards data retention. 2. Attitudes towards asylum and immigration: Dealing with refugees with rejected asylum applications (deportation or toleration under certain conditions); concern about the increase in the number of refugees; concrete concerns regarding the topic of asylum and refugees in Germany (alienation, increasing criminality, rise in right-wing radicalism, loss of German identity, increase in illegal immigrants in the country, excessive demands on social security systems); necessity of selected measures to improve and tighten asylum law (immediate deportation in the case of rejected asylum applications, work permits for asylum seekers, toleration despite rejection of the asylum application, stricter examination of the grounds for asylum); assessment of selected criteria for immigration to Germany (occupation in demand, family members in Germany, young or old, political persecution in home country, university degree, good knowledge of German, no criminal offences in home country, desire for education); opinion on the immigration of highly qualified skilled workers from abroad; assessment of the domestic significance of German immigration policy; assessment of the importance of a welcoming culture for immigrants; immigrants feel welcome vs. not welcome in Germany. 3. Counter-terrorism: assessment of the danger of terrorist attacks in Germany; assessment of the federal government´s efforts with regard to protection against a terrorist attack; opinions on punishment of financial supporters and of sympathisers of terrorist organisations. Demography: sex; age; highest level of education; employment; occupational status; marital status (household structure); religious denomination; net household income. Additionally coded were: Respondent ID; weighting factor; city size; federal state. Einstellungen zu den Themen Innere Sicherheit, Asyl, Einwanderung und Terrorbekämpfung. 1. Innere Sicherheit: Subjektives Sicherheitsgefühl bzw. Sorge vor kriminellen Übergriffen; Einschätzung von maßgeblichen Quellen für Kriminalitätsfurcht (z. B. durch Gewaltdarstellungen im Fernsehen, durch die täglichen Nachrichten über Verbrechen und Kriminalität, eigene Viktimisierungserfahrungen, etc.); Sorge vor konkreten Verbrechen (Anschläge islamistischer Terroristen, organisierte kriminelle Banden aus dem Ausland, Gewaltbereitschaft Jugendlicher, Diebstahl und Überfälle auf offener Straße, Einbrüche und Aufbrechen von Autos, Gewalt in der Familie, Polizeiübergriffe); Beurteilung der Bemühungen der Politik, Sicherheit zu gewährleisten; Beurteilung von geeigneten Sicherheitsmaßnahmen (mehr sichtbare Präsenz von Polizei und Ordnungskräften, schnellere Abschiebung von ausländischen Straftätern, Ordnungskräfte im öffentlichen Nahverkehr, Videoüberwachung öffentlicher Plätze und in Nahverkehrsmitteln, härtere Strafen, intensivere Polizeikontrollen auf Autobahnen, stärkere Internetüberwachung, mehr soziale Präventionsprojekte für Jugendliche); Beurteilung der stärkeren Zusammenarbeit von verschiedenen deutschen und internationalen Sicherheitsorganen (Polizeibehörden von Bund und Ländern, Polizei und Nachrichtendienste der EU-Staaten bzw. Polizei und Nachrichtendienste von Deutschland und den USA); Einstellung zur Videoüberwachung auf öffentlichen Plätzen (Gefühl von Sicherheit oder eher von Unbehagen oder beides gleichzeitig); Einstellung zur Vorratsdatenspeicherung. 2. Einstellungen zu Asyl und Einwanderung: Umgang mit Flüchtlingen mit abgelehntem Asylantrag (Abschiebung oder Duldung unter bestimmten Bedingungen); Sorge um Anstieg der Flüchtlingszahlen; konkrete Sorgen in Bezug das Thema Asyl und Flüchtlinge in Deutschland (Überfremdung, steigende Kriminalität, Anstieg des Rechtsradikalismus, Verlust der deutschen Identität, Zuwachs an Illegalen im Land, Überforderung der sozialen Sicherungssysteme); Notwendigkeit ausgewählter Maßnahmen zur Verbesserung und Verschärfung des Asylrechts (sofortige Abschiebung bei abgelehnten Asylanträgen, Arbeitserlaubnis für Asylbewerber, Duldung trotz Ablehnung des Asylantrags, strengere Prüfung der Asylgründe); Beurteilung ausgewählter Kriterien für die Einwanderung nach Deutschland (nachgefragter Beruf, Familienangehörige in Deutschland, jung oder alt, politische Verfolgung im Heimatland, Universitätsabschluss, gute Deutschkenntnisse, keine Straftaten im Heimatland, Ausbildungswunsch); Meinung zur Zuwanderung hochqualifizierter Fachkräfte aus dem Ausland; Beurteilung der innenpolitischen Bedeutsamkeit der deutschen Einwanderungspolitik; Beurteilung der Wichtigkeit einer Willkommenskultur für Einwanderer; Einwanderer fühlen sich in Deutschland willkommen vs. nicht willkommen. 3. Terrorbekämpfung: Einschätzung der Gefahr terroristischer Anschläge in Deutschland; Einschätzung der Bemühungen der Bundesregierung im Hinblick auf den Schutz vor einem terroristischen Anschlag; Meinungen zu Bestrafung von finanziellen Unterstützern und von Sympathiewerbern von Terrororganisationen. Demographie: Geschlecht; Alter; höchster Bildungsabschluss; Erwerbstätigkeit; berufliche Stellung; Familienstand (Haushaltsstruktur); Konfession; Haushaltsnettoeinkommen. Zusätzlich verkodet wurden: Befragten-ID; Gewichtungsfaktor; Ortsgröße; Bundesland.

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.