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.

Users can download reports regarding immigrant issues and view maps of the distribution of the foreign-born population in the U.S. Topics include: immigration policy, migration, English language proficiency, and adult education. Background The Migration Policy Institute is a think tank dedicated to studying human migration across the globe. This website is useful for policymakers and practitioners interested in understanding and responding to immigrant integration. Topics include, but are not limited to: migration, immigration policy, English language proficiency, immigration enforcement, and English language education. User Functionality Users can download reports regarding immigrant int egration issues and immigration trends. Users can also access the State Responses to Immigration Database, the American Community Survey of the Foreign Born, and Who's Where in the United States Database. Users can download data into SAS statistical software. In addition, users can view maps showing the distribution of the foreign-born population in the U.S. Demographic information is available by race/ethnicity, Hispanic origin, place of origin, citizenship status, sex/gender, and marital status. Data Notes Data sources include the New Immigrants Survey, U.S. Department of State, U.S. Department of Homeland Security Office of Immigration Statistics, U.S. Department of Education, United States Census Bureau, U.S. Government Accountability Office (GAO), U.S. Customs and Border Protection, U.S. Immigration and Customs Enforcement (ICE), U.S. Citizen and Immigration Services, Congressional Research Service, Transactional Records Access Clearinghouse, National Immigration Law Ce nter, among others. Full citations and years to which the data apply, are indicated in each report. Data are available on national, state and city levels, depending upon the report.

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.

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 layer shows children by nativity of parents by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of children who are in immigrant families (children who are foreign born or live with at least one parent who is foreign born). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B05009Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Virginia has a sizable immigrant community. About 12.3 percent of the state’s residents are foreign-born, and 6.7 percent of its U.S.-born residents live with at least one immigrant parent. Immigrants make up 15.6 percent of Virginia's labor force and support the local economy in many ways. They account for 20.7 percent of entrepreneurs, 21.8 percent of STEM workers, and 12.7 percent of nurses in the state. As neighbors, business owners, taxpayers, and workers, immigrants are an integral part of Virginia’s diverse and thriving communities and make extensive contributions that benefit all.

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.

This table contains 36 series, with data for years 1953 - 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 (1 items: Canada ...) Origin of migrants (2 items: Overseas and the United States; Overseas ...) Intended occupations and dependents (18 items: Total immigration; Workers; managerial and administrative; Workers; professional; Total workers ...).

International migrant stock is the number of people born in a country other than that in which they live. It also includes refugees. The data used to estimate the international migrant stock at a particular time are obtained mainly from population censuses. The estimates are derived from the data on foreign-born population--people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population--that is, people who are citizens of a country other than the country in which they reside--are used as estimates. After the breakup of the Soviet Union in 1991 people living in one of the newly independent countries who were born in another were classified as international migrants. Estimates of migrant stock in the newly independent states from 1990 on are based on the 1989 census of the Soviet Union. For countries with information on the international migrant stock for at least two points in time, interpolation or extrapolation was used to estimate the international migrant stock on July 1 of the reference years. For countries with only one observation, estimates for the reference years were derived using rates of change in the migrant stock in the years preceding or following the single observation available. A model was used to estimate migrants for countries that had no data.

The AmeriCorps Office of Research and Evaluation provides grants to researchers, scholars, and dissertators at institutions of higher education, enabling them to engage in comprehensive studies on civic engagement, volunteering, and national service. Studies include a variety of populations and ranges from local to organizational, and national contexts throughout the United States.

This AmeriCorps Research Grantee dataset provides comprehensive information about the grantees and their studies. For each award, we identify the: 1) study title; 2) background; 3) cohort year; 4) principal investigators and their affiliated university; 5) study location(s) associated with each grant; 6) civic engagement topic areas; and 7) the research approach. Please be aware that there may be multiple rows corresponding to a single research grantee study, reflecting the various study sites where the grantee is actively involved.

Each study was thematically coded to identify their civic engagement topic areas. An individual study can be categorized into more than one group. The topic areas include: • Arts & Culture, • Community Development, • Education Across the Life Course, • Youth Development, • Environment and Climate Change, • Health & Social Wellbeing, • Immigrants, Immigration & Refugees, • Economic Opportunity and Employment, • Social Equity, • Senior Development, and • Volunteering, Nonprofit Studies, and National Service.

Additionally, the research grantees’ studies were categorized into two distinct research approaches: traditional research and participatory research.

To learn more about the studies’ civic engagement topic areas and research approaches, please refer to the AmeriCorps Research Grantee Data Dictionary under Attachments.

For up-to-date information surrounding the AmeriCorps Research Grantees please see: • What is a Research Grant? https://americorps.gov/sites/default/files/document/What%20is%20a%20Research%20Grant_053023_final_508.pdf • AmeriCorps Research Grantee Activities and Insights: https://americorps.gov/grantees-sponsors/research-evaluation/grantee-profiles • Participatory Research: https://americorps.gov/sites/default/files/document/2021_07_20_ParticipatoryResearchOnePager_ORE.pdf

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows children by nativity of parents by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of children who are in immigrant families (children who are foreign born or live with at least one parent who is foreign born). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B05009 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

AbstractQuantifying sex-specific additive genetic variance (VA) in fitness, and the cross-sex genetic correlation (rA), is prerequisite to predicting evolutionary dynamics and the magnitude of sexual conflict. Further, quantifying VA and rA in underlying fitness components, and genetic consequences of immigration and resulting gene flow, is required to identify mechanisms that maintain VA in fitness. However, these key parameters have rarely been estimated in wild populations experiencing natural environmental variation and immigration. We used comprehensive pedigree and life history data from song sparrows (Melospiza melodia) to estimate VA and rA in sex-specific fitness and underlying fitness components, and to estimate additive genetic effects of immigrants alongside inbreeding depression. We found evidence of substantial VA in female and male fitness, with a moderate positive cross-sex rA. There was also substantial VA in male but not female adult reproductive success, and moderate VA in juvenile survival but not adult annual survival. Immigrants introduced alleles with negative additive genetic effects on local fitness, potentially reducing population mean fitness through migration load, but alleviating expression of inbreeding depression. Our results show that VA for fitness can be maintained in the wild, and be broadly concordant between the sexes despite marked sex-specific VA in reproductive success. Usage notesWolak_et_al_SOSP_fitness_QG_DataData for SEX-SPECIFIC ADDITIVE GENETIC VARIANCES AND CORRELATIONS FOR FITNESS IN A SONG SPARROW (MELOSPIZA MELODIA) POPULATION SUBJECT TO NATURAL IMMIGRATION AND INBREEDING by Wolak, Arcese, Keller, Nietlisbach, & Reid published in Evolution These data come from the long-term song sparrow field study on Mandarte Island, BC, Canada. The data provided here are sufficient to replicate the analyses presented in the above paper, and are therefore a restricted subset of the full Mandarte dataset. If you are interested in running additional analyses that require further data then please get in touch with at least one (preferably all) of the following project leaders: - Prof Peter Arcese (University of British Columbia): peter.arceseubc.ca - Prof Lukas Keller (University of Zurich): lukas.kellerieu.uzh.ch - Prof Jane Reid (University of Aberdeen): jane.reidabdn.ac.uk We are always happy to develop collaborations with researchers who have good ideas for new analyses. We would also appreciate it if you could let us know if you are intending to make use of the dataset below in order to facilitate coordination of different ongoing research efforts and allow us to keep track of all outputs from the long-term field study.Wolak_et_al_SOSP_fitness_QG.zipWolak_et_al_SOSP_fitness_QG_AnalysisCodeCode for SEX-SPECIFIC ADDITIVE GENETIC VARIANCES AND CORRELATIONS FOR FITNESS IN A SONG SPARROW (MELOSPIZA MELODIA) POPULATION SUBJECT TO NATURAL IMMIGRATION AND INBREEDING by Wolak, Arcese, Keller, Nietlisbach, & Reid published in Evolution These data come from the long-term song sparrow field study on Mandarte Island, BC, Canada. The data provided here are sufficient to replicate the analyses presented in the above paper, and are therefore a restricted subset of the full Mandarte dataset. If you are interested in running additional analyses that require further data then please get in touch with at least one (preferably all) of the following project leaders: - Prof Peter Arcese (University of British Columbia): peter.arceseubc.ca - Prof Lukas Keller (University of Zurich): lukas.kellerieu.uzh.ch - Prof Jane Reid (University of Aberdeen): jane.reidabdn.ac.uk We are always happy to develop collaborations with researchers who have good ideas for new analyses. We would also appreciate it if you could let us know if you are intending to make use of the dataset below in order to facilitate coordination of different ongoing research efforts and allow us to keep track of all outputs from the long-term field study.Wolak_et_al_fitness_AnalysisCode.R

Maryland International Migration from 2001 to 2022, which includes net foreign-born international migration, net movement to/ from Puerto Rico, net Armed Forces movement and native emigration. Source from the Population Division, U.S. Census Bureau, March 2023.

This dataset contains multiple measures of psychological processes that have previously been found to vary cross-culturally: individualism, collectivism, closeness to others, attributional style, object categorisation, drawing style and self-enhancement. Respondents are all residents of East London, with most falling into three main groups: first generation British Bangladeshis, second generation British Bangladeshis, and non-migrants with no Bangladeshi or South Asian heritage. The data also contains various demographic and lifestyle measures, such as education level, family contact, mass media use, age, and age of migration (for first generation migrants). Please see attached publication (Mesoudi, Magid & Hussain, 2016, PLOS ONE) for more details.

Until recently, psychologists assumed that people from different societies all think in the same way as we do in the West - that there is a universal human psychology shared by everyone on the planet. However, when psychologists started testing non-Western people, rather than the American and British undergraduates who typically do psychology experiments, they found intriguing cultural differences. For example, there are differences in perception: Westerners focus on single objects, whereas non-Westerners focus on the relationships between objects. If you show a British and a Japanese person a scene containing lots of objects, the British person is subsequently better at recognising the objects if they are presented on their own, whereas the Japanese person has better memory if the object is presented in the original scene. Or differences in explaining other people's behaviour: Westerners explain behaviour of others in terms of fixed personality traits, whereas non-Westerners explain actions in terms of social contexts. A British teacher might explain a student's poor exam performance in terms of their laziness or lack of intelligence, whereas a Korean teacher might appeal instead to the overbearing pressure to succeed academically.

But why do people from different cultures think differently? This is the central question addressed by this project. Several explanations are possible: it could be that psychological variation is caused by genetic differences between populations, and cognitive style is inherited genetically from parents. Alternatively, parents could have a non-genetic influence, through direct teaching or passive observation. Or psychological traits could be transmitted non-parentally, via peers, formal schooling, or the mass media.

We will take advantage of a unique natural experiment to tease apart these factors: immigration. If the UK-born children of non-Western immigrants resemble their parents in their psychological traits, we can infer that those traits are transmitted from parents either genetically or culturally. If, on the other hand, they resemble local non-immigrants, then non-parental influence must be at work. We will then see whether this shift is associated with specific factors, such as years of schooling, exposure to mass media, or bilingualism.

Another way of explaining psychological variation is in terms of history. For example, it has been suggested that Western individualism arose in ancient Greece as a response to solitary herding, whereas Eastern collectivism arose in ancient China as a response to collective rice farming. We will test this by simulating these conditions in the lab, as an experimental "microcosm" of cultural history, to see whether solitary action stimulates individualism and collective action stimulates collectivism.

Finally, we will develop a web app that will let us test these ideas in multiple countries, beyond the UK, and specifically targeting immigrant groups. If these relationships hold across several regions, we can be more confident that they are valid. On the other hand, differences between regions might also be valuable. If immigrants acculturate faster in London than elsewhere, as suggested by pilot data, we can identify why this is, such as differences in mass media influence, bilingualism or family size.

This project has major potential benefits for the successful integration of immigrants to the UK. Psychological differences can constitute a barrier to successful social and economic integration. For example, non-Western students can find it difficult to cope in Western educational systems that favour autonomy and creative thinking. Knowing the origin of these differences can help to overcome them better, for example by targeting parents (if parents have an influence) or the media (if the media plays a role).

The Bodie-Wassuk pronghorn (Antilocapra americana) herd contains migrants, but this herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual ranges were modeled using year-round data to demarcate high use areas instead of modeling the specific winter ranges commonly seen in other ungulate analyses in California (for example, see the “Casa Diablo Mule Deer” section of this report). Though the Bodie-Wassuk pronghorn aren’t traditional migrants, much of the herd displays a somewhat nomadic migratory tendency, moving between the Bodie Hills east of U.S. Highway 395 in California to a basin west of the Wassuk Range between Aurora Crater and Corey Peak in Nevada. A few collared individuals moved as far north as the Gray Hills, staying west of the Wassuk Range, and one collared individual moved as far south as the Alkali Valley. This herd prefers low sagebrush (Artemisia arbuscula) habitat followed by big sagebrush (Artemisia tridentata) communities. Pronghorn were reintroduced just north of Mono Lake beginning in the 1940s as well as to the Adobe Valley in the 1980s, but low fawn recruitment due to marginal habitat quality, a lack of permanent water availability, abundant wildlife exclusion fencing, and the expansion of pinyon-juniper woodlands and invasive cheat-grass (Bromus tectorum) into preferred native shrub and forb habitats has prevented population growth. The current estimate is stable at <150 animals (Taylor, 2011). Additional conservation concerns for the herd include human activities, such as off-highway vehicle use and proposed mining, as well as an increasing wild horse population. These mapping layers show the _location of the migration corridors for pronghorn (Antilocapra americana) in the Bodie-Wassuk population in California. They were developed from 102 migration sequences collected from a sample size of 18 animals comprising GPS locations collected every 4 hours.

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

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

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

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

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

Chart and table of the Philippines net migration rate from 1950 to 2025. United Nations projections are also included through the year 2100.