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

This is the main page for migration indicator related data and reports. The following are included as part of this work: * Internal migration * International migration - Long-Term International Migration (LTIM) - International Passenger Survey (IPS) * National Insurance Number (NINo) allocations * 'Flag 4' GP registrations * Short-term international migration All Updates and the accompanying data can be downloaded. The Excel workbook contains the raw data as well as charts for the different migration indicators. Latest data update: December 2016 release. Next data update: February 2017 release. N.B. - written Updates are refreshed twice a year to coincide with the May and November data releases.

This is the data collection template for regional parliaments produced in the context of the project "Pathways to Power: The Political Representation of Citizens of Immigrant Origin in Seven European Democracies". It is made available for the purposes of future data collection of comparable data in other countries or periods.

This dataset provides information on human population by census area in Alaska, and migration between census areas, using the Permanent Fund Dividend (PFD) applications to determine area of residence. Using the PFD as the source for this information has the advantage that the data have broad in-state coverage at an annual level, since most (~90% in 2017) Alaska residents submit applications. These data have the disadvantage that they may lag on new migrants from outside the state, however, because new migrants aren’t eligible for the PFD until they’ve lived in Alaska for one calendar year. Additionally, PFD data do not capture people who don’t live here long enough to qualify for a PFD. This archival record contains an excel file of migration data broken down by census area, age, and gender, downloaded from the State of Alaska Department of Labor and Workforce Development website (accessed 2019-02-20, http://live.laborstats.alaska.gov/pop/migration/PFDMigrationByAgeBySexBCA.xls). More information on the PFD-based migration data can be found here: http://live.laborstats.alaska.gov/pop/migration.cfm. Also contained in this record is an RMarkdown document which accesses the archived excel file, reformats the file, and plots migration information for Cook Inlet boroughs.

This Excel file contains annual net migration records for Russian regions by 1-year age groups, from 0 to 80, for the periods 2003-2010 and 2011-2013. The first period is defined by the two Russian Censuses (end of 2002 and end of 2010). The second period is limited by the availability of data. Moreover, there was a significant change in the current migration record in 2011; so, the data for the two periods are barely comparable. There are 78 regions , as the data for Moscow and Leningrad regions are merged with the data for the federal cities of Moscow and St.Petersburg, correspondingly. List of data files:IR_0310.csv - inter-regional migration in 2003-2010IN_0310.csv - international migration in 2003-2010IR_1113.csv - inter-regional migration in 2011-2013IN_1113.csv - international migration in 2011-2013

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

Data sheet for the entry of bi-weekly waterfowl survey data from the state of Kansas. This Excel file contains the data entry sheet and a chart displaying waterfowl counts over the survey period. This data sheet is empty and is a template for bi-weekly waterfowl survey data entry.

The data comprises two forms of data collected across four African countries; Ghana, Nigeria, Mozambique and Kenya. These were: • The results of a business survey administered to both migrant-owned and non-migrant owned businesses in the four case study countries. The survey data is contained within an Excel spreadsheet with responses organised in four separate sheets by case study country. The code '777' is used in individual cells to denote that no answer was given for that particular question. • Transcripts of, or fieldnotes from, semi-structured interviews with migrants, organisations connected to migration, host nationals working for migrant businesses and selected government Ministries and Departments connected to migration policy in the four case study countries. The interview data is organised by country and sub-divided into five separate folders categorised by key informant group; i) Government Ministries, Departments and Agencies; ii) Civil Society Organisations, iii) Migrant Community Representatives (organisations or leaders); iv) Migrant Business Owners and; v) Host Nationals Working for Migrant Business owners.

An Excel Workbook containing Internal Revenue Service (IRS) Statistics of Income (SOI) county-to-county migration data from 2011-2020 summed in total for Broward County.

This Excel tool enables you to interrogate data annual data between 2004 and 2016 at London borough level released by the Office for National Statistics in the Local Area Migration Indicators suite (LAMI). Data includes: Long-term international migration Internal migration new migrant GP registrations Births National Insurance number registrations to overseas nationals

These estimates take into account the counts of the 2006 Census,adjusted for net census undercoverage and are based on the 2006 Standard Geographical Classification (SGC). The publication includes statistics for the demographic components that were used to produce the population estimates (births, deaths, marriages, divorces, immigration, emigration, net temporary emigration, returning emigration, internal migration and non-permanent residents) by age and sex. In addition, the publicat ion contains highlights of current demographic trends and a description of the methodology. It also provides additional data such as a chronological series of estimates by various levels of geography. With regard to provinces and territories, the estimates date back to 1971 (tables and animated age pyramid), 1996 for census divisions, census metropolitan areas and economic regions as well as census families. Note that the title of this product has changed for the 2008/09 edition, which is called Canada's Demographic Estimates.

An Excel workbook containing tables of historical census data for a range of indicators dating back to 1961. Available in Excel 2003 (csv download) and Excel 2007-10 (excel download) formats.

Since 2013, the Dutch Migration Chain has had a chain-wide data dictionary, the Data Dictionary Migration Chain (GMK). The Migration Chain consists of the following organisations: - Central Agency for the Reception of Asylum Seekers - Correctional Institutions Agency, Ministry of Justice and Security - Repatriation and Departure Service, Ministry of Justice and Security - Directorate-General for Migration, Ministry of Justice and Security - Immigration and Naturalization Service , Ministry of Justice and Security - International Organization for Migration - Royal Netherlands Marechaussee - Ministry of Foreign Affairs - National Police - Council of State - Council for the Judiciary - Netherlands Council for Refugees - Seaport Police. ### Data dictionary Migration chain One of the principles in the basic starting architecture of the migration chain is that there is no difference of opinion about the meaning of the information that can be extracted from an integrated customer view. A uniform conceptual framework goes further than a glossary of the most important concepts: each shared data can be related to a concept in the conceptual framework; in the description of the concepts, the relations to each other are named. Chain parties have aligned their own conceptual frameworks with the uniform conceptual framework in the migration chain. The GMK is an overview of the common terminology used within the migration chain. This promotes a correct interpretation of the information exchanged within or reported on the processes of the migration chain. A correct interpretation of information prevents miscommunication, mistakes and errors. For users in the migration chain, the GMK is available on the non-public Rijksweb (gmk.vk.rijksweb.nl). In the context of openness and transparency, it has been decided to make the description of concepts and management information from the GMK accessible as open data. This means that the data via Data.overheid.nl is available and reusable for everyone. By making the data transparent, the Ministry also hopes that publications by and about work in the migration chain, such as the State of Migration, can be better explained and contextualised. ### Manual Manual for using the open datasets of the migration chain in Excel.

This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z-direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.

Files:

figure_excel_files.zip:

Excel files for figures in the publication, and excel files of main data tables for FCC and HCP vacancy formation energies and vacancy migration energies.

fcc_hvf_hvm.tar.gz and hcp_hvf_hvm.tar.gz:

Raw VASP files corresponding to FCC and HCP vacancy formation energies and vacancy migration energies.

bulk_modulus.tar.gz:

Raw VASP files corresponding to FCC bulk modulus calculations.

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

This repository contains the replication material for the article "Jeannet, A.-M. and Dražanová, L. 2023. Blame it on my youth: the origins of attitudes towards immigration. Acta Politica. doi: 10.1057/s41269-023-00314-6" - The Stata .do file "Drazanova_origins_attitudes_immigration.do" contains all code to reproduce the results and to generate the 4 figures and 1 table in the article - The Stata .do file "Jeannet_manifesto_replication.do" runs all the code to generate the manifesto data - The Excel .xlsx file "original Manifesto_values.xlsx" contains the recoded Manifesto data used for the analysis - The Excel .xlsx files "cohort principles.xlsx" and "period principles.xlsx" contain data based on the "Manifesto Project Data" to be loaded to the main dataset - The Excel .xlsx files "historical unemployment.xlsx" and "period unemployment.xlsx" data files contain data based on the "OECD Economic Outlook 10" data to be loaded to the main dataset - The Excel .xlsx files "historical net migration" and "period net migration" data files contain data based on the United Nations Department of Economic and Social Affairs’ Population Division dataset - the Word document "READ ME.doc" describes all the data used in detail (2023-10-12)

The Eastern Partnership Risk Analysis Network (EaP-RAN) performs monthly exchanges of statistical data and information on the most recent irregular migration trends. This information is compiled at the level of the Frontex Risk Analysis Unit (RAU) and analysed in cooperation with the regional partners on a quarterly and annual basis. The annual reports offer a more in-depth analysis of the occurring developments and phenomena which impact the regional and common borders while the quarterly reports are meant to provide regular updates and identify emerging trends in order to maintain situational awareness. Both types of reports are aimed at offering support for strategic and operational decision making.

The Eastern Partnership Quarterly statistical overview is focused on quarterly developments for the seven key indicators of irregular migration: (1) detections of illegal border-crossing between BCPs; (2) detections of illegal border-crossing at BCPs; (3) refusals of entry; (4) detections of illegal stay; (5) asylum applications; (6) detections of facilitators; and (7) detections of fraudulent documents.

The backbone of this overview are monthly statistics provided within the framework of the EaP-RAN (Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine) and reference period statistics from common border sections of the neighbouring EU Member States and Schengen Associated Countries (Norway, Finland, Estonia, Latvia, Lithuania, Poland, Slovakia, Hungary and Romania). The data are processed, checked for errors and merged into an Excel database for further analysis.