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.

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

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 ...).

This table contains 32 series, with data for years 1956 - 1976 (not all combinations necessarily have data for all years), and was last released on 2012-02-16. This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (32 items: Outside Canada; Great Britain; France; Europe ...).

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.

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...

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 provides the number of temporary foreign workers in Canada and in provinces by their country of citizenship.

Population in The Netherlands on 1 January by sex, age, marital status, generation and migration background.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from 1996 to 2022.

Status of the figures: All figures in the table are final.

Changes per 13 January 2023: None, this table was discontinued.

When will new figures be published? No longer applicable. This table is succeeded by the table Population; sex, age, country of origin, country of birth, 1 January. See section 3.

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 ...).

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.

While the presence of foreign-born footballers in national teams has a long history, it is often believed that the World Cup has become more migratory over time. The presumed increases in the volume and diversity of foreign-born footballers have, however, remained empirically untested. In this article, we empirically test whether the presence of foreign-born footballers at the World Cup has changed over time in respect to these two dimensions of migration. We conducted an analysis on 4.761 footballers, derived from the fifteen national teams that competed in at least ten editions of the World Cup between 1930 and 2018, which comprises of 301 foreign-born football players. We argue that countries’ different histories of migration, in combination with historically used citizenship regimes, largely influence the migratory dimensions of their representative football teams. Our outcomes show that the (absolute) volume of foreign-born footballers in World Cups is indeed increasing over time. Moreover, foreign-born footballers seem to come from an increasingly diverse range of countries. We, therefore, conclude that the World Cup has become more migratory in terms of volume and diversity from an immigration perspective.

This dataset presents the estimates of the internal migration statistics of Australia by Local Government Areas (LGA) following the 2011 Australian Statistical Geography Standard (ASGS). The dataset spans from the 2006-07 financial year up to the 2015-16 financial year.

Regional internal migration is the movement of people from one region to another within Australia (both interstate and intrastate). For example, it incorporates moves from a Local Government Area (LGA) to any other LGA within the country. Net regional internal migration is the net gain or loss of population through this movement.

The ABS has developed a new series of annual regional internal migration estimates (RIME) based on the 2011 edition of the Australian Statistical Geography Standard (ASGS). The Medicare and Defence data used for estimating interstate migration is now also used to estimate internal migration below the state/territory level. However, as Medicare and Defence change of address counts are supplied to the ABS by postcode a method was developed to convert these counts to SA2, the base spatial unit of the ASGS. The method used correspondences to convert to SA2, and adjustments were applied to account for known deficiencies in the Medicare and Defence data. A similar method was used to prepare RIME at the LGA level, based on 2011 boundaries.

This data is Australian Bureau of Statistics (ABS) data (catalogue number: 3412.0) used with permission from the ABS.

For more information please visit the ABS Explanatory Notes.

Please note: RIME are not directly comparable with estimated resident populations (ERPs) because of the different methods and source data used to prepare each series. The combination of natural increase and net migration (internal and overseas) therefore may not correspond with change in ERP. AURIN has spatially enabled the original data.

In 2015 a team of researchers based in Greece, Italy, Turkey and Malta undertook interviews with refugees and migrants as well as stakeholders and observed events of the so-called ‘migration crisis’ as they unfolded. The dataset deposited here includes information on semi-structured interviews with a total of 500 refugees and migrants, 440 of whom had crossed the Mediterranean by boat in 2015 to Greece (215 interviews), Italy (205 interviews) and Malta (20 interviews) together with a further 60 respondents who had moved to Turkey and were considering making the onward journey to Europe. These countries reflected the key locations of the crisis. The initial analysis of the dataset has meant that it has been coded to record who was on the move, the journeys that they made and the routes that they took. This enables an examination of the relationship between micro-level characteristics and the geographies of migration that were recorded. Specifically the dataset here includes: 1. Methodological note - a description of the project, the approach to the fieldwork and the analysis undertaken 2. Individuals - data on the demographic and socio-economic characteristics of the interviewees. 3. Routes - data on the routes taken by our interviewees and the duration taken to travel along them. 4. Journeys - data setting out the individual stops and journeys recorded from each interviewee, travel method between them and duration of travel. This has been prepared for insertion into GIS Mapping software. In the first six months of 2015 more than 100,000 migrants crossed the Mediterranean, arriving at the shores of southern Europe in search of protection or a better life. In the same period more than 1,800 people lost their lives, drowning as overloaded and often unseaworthy boats sank into the sea. Although the crisis is in many ways nothing new, these scenes have captured the public and media imagination and have challenged the ability of European States to respond appropriately. Recent months have seen increasingly heated discussions at the national and EU level about whether rescues at sea are a vital humanitarian intervention or simply encourage others to attempt the crossing, and whether those who arrive can be dealt with through mandatory or voluntary relocation quotas. Many of these discussions are underpinned by assumptions about why it is that migrants make the journey to Europe in the first place. In this context the research aims to better understand the dynamics of migration in the Mediterranean region by providing the first large-scale, systematic and comparative study of the backgrounds, experiences, aspirations and routes of migrants in four European countries (Italy, Greece, Malta and Turkey) gathered from 550 migrants who have recently arrived and 100 stakeholders. The research pushes the theoretical and conceptual boundaries of migration studies, encouraging critical reflexive dialogue and practice by opening new and inclusive spaces for questioning and challenging established ways of categorising and thinking about with the Mediterranean migration crisis. In so doing it will create opportunities for increased policy dialogue and academic collaboration between the case study countries - and across the EU more generally - around the evidence gathered. The research is underpinned by a number of urgent research questions which inform the collection and analysis of the data: 1. What are the underlying factors shaping migration from countries of origin and how do the characteristics and backgrounds of migrants shape the response to structural issues? 2. What are the opportunity structures that shape, inform or constrain migrant journeys to Europe? 3. What are the differences between the Central Mediterranean (principally from Libya to Italy and Malta) and Eastern (Turkey to Greece) Mediterranean routes? 4. To what extent are migrant journeys to Europe, or particular countries within Europe, shaped or even determined by non-state actors (agents, facilitators and civil society)? 5. What are the impacts of policies intended to deter or prevent migrants from crossing the Mediterranean? The research will be delivered by a team of leading UK migration scholars from the Universities of Coventry, Oxford, Birmingham and Sussex working in collaboration with academic partners in the case study countries and supported by experts from international, governmental and non-governmental organisations. The team has established relationships with local civil society organisations that will facilitate access to research participants and provide advice and information on policy or other developments which could impact on the delivery of the project. The research will benefit a wide range of academic, governmental, international and civil society organisations and inform the development of strategic, political and policy responses to the migration crisis in the Mediterranean. This impact will be secured through a three-stage stage process involving: the production of evidence in formats accessible to a range of audiences; a clearly articulated process for securing impact involving dissemination events and outreach activities to connect the evidence with audiences that influence and inform the policy making process and; the development of cross-national networks of researchers, policy makers and practitioners to drive longer term policy change. These will create feedback loops and opportunities for further research. We carried out semi-structured interviews with a total of 500 refugees and migrants, 440 of whom had crossed the Mediterranean by boat in 2015 to Greece (215 interviews), Italy (205 interviews) and Malta (20 interviews) together with a further 60 respondents who had moved to Turkey and were considering making the onward journey to Europe. Our approach to the fieldwork had to be agile and the sampling strategy purposive in order to adapt to different social and political contexts as well us enabling us to interview people who had recently arrived as well as those who were looking to transit onwards.

To what extent does exposure to immigration condition the types of immigrants citizens are willing to admit? Extending the conjoint approach adopted by Hainmueller and Hopkins (2015), this study investigates whether the admission preferences of French natives vary based on personal exposure to immigration, as proxied by local demographics and self-reported social contact. Methodologically, we propose and apply new methods to compare attribute salience across different subgroups of respondents. We find that although an inflow of immigrants into respondents' municipalities has a limited influence on how French natives evaluate prospective immigrants, social contact with immigrants matters. Specifically, French natives who do not frequently interact with immigrants are significantly less favorable toward immigrants from non-western countries, and more favorable toward immigrants from western countries. In contrast, natives who report frequent social interactions with immigrants place less weight on nationality as a criterion for immigrant admission. Although scholars have noted an increasing consensus in immigration attitudes across developed democracies, our findings suggest that individual experiences with immigration condition preferences for immigration policy at the national level.

Migration in India report was published for the first time along with annual PLFS exercise to provide details with respect to migrants in the country. The report covers the period from July to June 2020-2021. Migrants have been defined as those whose last usual place of residence is different from the present place of enumeration. Usual place of residence is the place (village/town) where the person stayed continuously for a period of 6 months or more or intends to stay for 6 months or more. The percentage distribution of migrants by reasons for migration has been compiled in this dataset.

Census: Number of Migrants: Punjab data was reported at 13,735,616.000 Person in 03-01-2011. This records an increase from the previous number of 9,189,438.000 Person for 03-01-2001. Census: Number of Migrants: Punjab data is updated decadal, averaging 9,189,438.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 13,735,616.000 Person in 03-01-2011 and a record low of 6,960,431.000 Person in 03-01-1991. Census: Number of Migrants: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAG001: Census of India: Migration: Number of Migrants: by States.

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.

The thirteenth of 20 years of International Social Survey Programme (ISSP) surveys in New Zealand by Professor Philip Gendall, Department of Marketing, Massey University.A verbose rundown on topics covered follows.Questions on national consciousness and national identity. Identification with the town, the city, the region, the nation and with the respective continent; most important characteristics for national identity; identification with one’s own nation and national pride (scale); perceived pride in the democracy of the country, the political influence of the country in the world, the economic achievement, the social security system, the scientific achievements, the achievements in sports, the achievements in arts or literature, the armed forces, the history and equal rights of all social groups in society.Preference for protective duty to support the national economy; attitude to the right of international institutions to enforce solutions to be accepted nationally; attitude to enforcing national interests regardless of evoking conflicts with other countries; rejection of acquisition of land by foreigners in one’s country; preference for national films in national television stations; damage done by large international companies to the local business; attitude to free trade; attitude to follow the decisions of international organisations even if the local government does not agree with them; international organisations take away too much power from the country.Availability of worldwide information as a benefit of the internet; importance of sharing national customs and traditions to achieve full nationality; attitude to government support of national minorities to preserve their customs and habits; preference for assimilation of minorities or retention of their identity; hostility to foreigners and prejudices against immigrants (scale); attitude to a reduction of immigration of foreigners; respondents citizenship; citizenship of parents at birth of respondent; birthplace or citizenship of parents should allow naturalization of children; same rights for citizens and legal immigrants; attitude towards stronger measures regarding illegal immigrants; languages spoken at home; perceived ethnic affiliation and strength of this feeling.Demography: Sex; age; marital status; steady life-partner; years in school, current employment status; current employment status of spouse; hours worked weekly; occupation of respondent and spouse (ISCO-88); respondent and spouse working for private, public sector or self-employed; supervisor function; union membership; household size; family income; respondents earnings; household composition; self-placement on a left-right continuum; party preference; vote last election; religious denomination; frequency of church attendance; self-placement on a top-bottom scale; region; town size, rural or urban region; ethnicity or nationality. Additionally encoded: Mode of data collection.

Migration has been occurring in the GMS for centuries, largely in an informal and unregulated fashion. The GMS Labor Migration program, launched in June 2005 by the World Bank, sought to address some of these key knowledge gaps. The objectives of this multi-year program are to: (i) improve knowledge about labor migration in the GMS focusing on the socio-economic impact of migration on sending and receiving countries; (ii) raise awareness about these issues and their significance for poverty reduction at the highest levels of policy making; and (iii) strengthen the capacity of governments and development partners to refine and implement a regional system to facilitate and regulate labor migration. This working paper is one of multiple outputs of the first phase of that program and gives findings on migrations patterns, economic role of immigrants, country by country data, migrant living conditions, and policy agenda.