Different migration-related data sources at local authority level including migration flows, non-UK-born and non-British populations, National Insurance number registrations, GP registrations, and births to non-UK-born mothers.

The Multi-aspect Integrated Migration Indicators (MIMI) dataset is the result of the process of gathering, embedding and combining traditional migration datasets, mostly from sources like Eurostat and UNSD Demographic Statistics Database, and alternative types of data, which consists in multidisciplinary features and measures not typically employed in migration studies, such as the Facebook Social Connectedness Index (SCI). Its purpose is to exploit these novel types of data for: nowcasting migration flows and stocks, studying integration of multiple sources and knowledge, and investigating migration drivers.

The MIMI dataset is designed to have a unique pair of countries for each row. Each record contains country-to-country information about: migrations flows and stock their share, their strength of Facebook connectedness and other features, such as corresponding populations, GDP, coordinates, NET migration, and many others.

Methodology.

After having collected bilateral flows records about international human mobility by citizenship, residence and country of birth (available for both sexes and, in some cases, for different age groups), they have been merged together in order to obtain a unique dataset in which each ordered couple (country-of-origin, country-of-destination) appears once. To avoid duplicate couples, flow records have been selected by following this priority: first migration by citizenship, then migration by residence and lastly by country of birth.

The integration process started by choosing, collecting and meaningfully including many other indicators that could be helpful for the dataset final purpose mentioned above.

• International migration stocks (having a five-year range of measurement) for each couple of countries.
• Geographical features for each country: ISO3166 name and official name, ISO3166-1 alpha-2 and alpha-3 codes, continent code and name of belonging, latitude and longitude of the centroid, list of bordering countries, country area in square kilometres. Also, the following features have been included for each pair of countries: geodesic distance (in kilometres) computed between their respective centroids.

• Non-bidirectional migration measures for each country: total number of immigrants and emigrants for each year, NET migration and NET migration rate in a five-year range.

• Other multidisciplinary indicators (cultural, social, anthropological, demographical, historical features) related to each country: religion (single one or list), yearly GDP at PPP, spoken language (or list of languages), yearly population stocks (and population densities if available), number of Facebook users, percentage of Facebook users, cultural indicators (PDI, IDV, MAS, UAI, LTO). Also the following feature have been included for each pair of countries: Facebook Social Connectedness Index.

Once traditional and non-traditional knowledge is gathered and integrated, we move to the pre-processing phase where we manage the data cleaning, preparation and transformation. Here our dataset was subjected to various computational standard processes and additionally reshaped in the final structure established by our design choices.

The data quality assessment phase was one of the longest and most delicate, since many values were missing and this could have had a negative impact on the quality of the desired resulting knowledge. They have been integrated from additional sources such as The World Bank, World Population Review, Statista, DataHub, Wikipedia and in some cases extracted from Python libraries such as PyPopulation, CountryInfo and PyCountry.

The final dataset has the structure of a huge matrix having countries couples as index (uniquely identified by coupling their ISO 3166-1 alpha-2 codes): it comprises 28725 entries and 485 columns.

Nowadays, new branches of research are proposing the use of non-traditional data sources for the study of migration trends in order to find an original methodology to answer open questions about cross-border human mobility. The Multi-aspect Integrated Migration Indicators (MIMI) dataset is a new dataset to be exploited in migration studies as a concrete example of this new approach. It includes both official data about bidirectional human migration (traditional flow and stock data) with multidisciplinary variables and original indicators, including economic, demographic, cultural and geographic indicators, together with the Facebook Social Connectedness Index (SCI). It is built by gathering, embedding and integrating traditional and novel variables, resulting in this new multidisciplinary dataset that could significantly contribute to nowcast/forecast bilateral migration trends and migration drivers.

Thanks to this variety of knowledge, experts from several research fields (demographers, sociologists, economists) could exploit MIMI to investigate the trends in the various indicators, and the relationship among them. Moreover, it could be possible to develop complex models based on these data, able to assess human migration by evaluating related interdisciplinary drivers, as well as models able to nowcast and predict traditional migration indicators in accordance with original variables, such as the strength of social connectivity. Here, the SCI could have an important role. It measures the relative probability that two individuals across two countries are friends with each other on Facebook, therefore it could be employed as a proxy of social connections across borders, to be studied as a possible driver of migration.

All in all, the motivations for building and releasing the MIMI dataset lie in the need of new perspectives, methods and analyses that can no longer prescind from taking into account a variety of new factors. The heterogeneous and multidimensional sets of data present in MIMI offer an all-encompassing overview of the characteristics of human migration, enabling a better understanding and an original potential exploration of the relationship between migration and non-traditional sources of data.

The MIMI dataset is made up of one single CSV file that includes 28,821 rows (records/entries) and 876 columns (variables/features/indicators). Each row is identified uniquely by a pairs of countries, built from the joining of the two ISO-3166 alpha-2 codes for the origin and destination country, respectively. The dataset contains as main features the country-to-country bilateral migration flows and stocks, together with multidisciplinary variables measuring cultural, demographic, geographic and economic variables for the two countries, together with the Facebook strength of connectedness of each pair.

Related paper: Goglia, D., Pollacci, L., Sirbu, A. (2022). Dataset of Multi-aspect Integrated Migration Indicators. https://doi.org/10.5281/zenodo.6500885

Abstract

This dataset, a product of the Trade Team - Development Research Group, is part of a larger effort in the group to measure the extent of the brain drain as part of the International Migration and Development Program. It measures international skilled migration for the years 1975-2000.

The methodology is explained in: "Tendance de long terme des migrations internationals. Analyse à partir des 6 principaux pays recerveurs", Cécily Defoort.

This data set uses the same methodology as used in the Docquier-Marfouk data set on international migration by educational attainment. The authors use data from 6 key receiving countries in the OECD: Australia, Canada, France, Germany, the UK and the US.

It is estimated that the data represent approximately 77 percent of the world’s migrant population.

Bilateral brain drain rates are estimated based observations for every five years, during the period 1975-2000.

Geographic coverage

Australia, Canada, France, Germany, UK and US

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

The data project includes the mobile indicators of migrant stocks, migrant flows from extended detail records (xDR) and python scripts used for data processing. The mobile indicators are calculated at varying geographic levels (grids, neigborhood, district).

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also nee

Abstract

This dataset, a product of the Trade Team - Development Research Group, is part of a larger effort in the group to measure the extent of the brain drain as part of the International Migration and Development Program. It measures international skilled migration, giving new estimates controlling for age of entry.

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

This Public Database on Irregular Migration Flow Estimates and Indicators, in short MIrreM D5.2, is a MIrreM project deliverable under work package 5. This database provides an inventory and critical appraisal of available estimates and indicators related to irregular migration flows. More specifically, the database contains the country-level data collected by MIrreM’s national rapporteurs, as well as EU-level data from sources other than Eurostat. The datasets include meta-level information on sources and methodology and a quality assessment based on MIrreM’s criteria. Users of this database are advised to consult the following companion document (henceforth, MIrreM Working Paper No. 9/2024) for a full discussion of the context, the underlying concepts, and the methodology used: Siruno, L., Leerkes, A., Hendow, M. & Brunovská, E. 2024. Working Paper on Irregular Migration Flows. MIrreM Working Paper No. 9. Krems: University for Continuing Education Krems (Danube University Krems). https://doi.org/10.5281/zenodo. 10702228. The MIrreM project is a follow-up to CLANDESTINO, which covered the period 2000-2007. MIrreM extends this to the subsequent period 2008-2023. The data covered in this database reflect what is available within this period. Most of the data was collected between June and October 2023, and thus in some cases, the data are only until 2022 pending complete reports for 2023.

The dissertation consists of three chapters relating to the measurement of immigration policies, which developed out of my work as an initial co-author of the International Migration Policy and Law Analysis (IMPALA) Database Project. The first chapter entitled, “Brain Gain? Measuring skill bias in U.S. migrant admissions policy,” develops a conceptual and operational definition of skill bias. I apply the measure to new data revealing the level of skill bias in U.S. migrant admissions policy between 1965 and 2008. Skill bias in U.S. migrant admissions policy is both a critical determinant of the skill composition of the migrant population and a response to economic and public demand for highly skilled migrants. However, despite its central role, this is the first direct, comprehensive, annual measure of skill bias in U.S. migrant admissions policy. The second chapter entitled, “Stalled in the Senate: Explaining change in US migrant admissions policy since 1965,” presents new data characterizing change in U.S. migrant admissions policy as both expansive and infrequent over recent decades. I present a new theory of policy change in U.S. migrant admissions policy that incorporates the role of supermajoritarian decision making procedures and organized anti-immigration groups to better account for both the expansive nature and t he infrequency of policy change. The theory highlights the importance of a coalition of immigrant advocacy groups, employers and unions in achieving policy change and identifies the conditions under which this coalition is most likely to form and least likely to be blocked by an anti-immigration group opposition. The third chapter entitled, “Post-coding aggregation: A methodological principle for independent data collection,” presents a new technique developed to enable independent collection of flexible, high quality data: post-coding aggregation. Post-coding aggregation is a methodological principle that minimizes data loss, increases transparency, and grants data analysts the ability to decide how best to aggregate information to produce measures. I demonstrate how it increases the fl exibility of data use by expanding the utility of data collections for a wider range of research objectives and improves the reliability and the content validity of measures in data analysis.

Are international migration flows racially biased? Despite widespread consensus that racism and xenophobia affect migration processes, no measure exists to provide systematic evidence on this score. In this research note, I construct such a measure—the migration deviation. Migration deviations are the difference between the observed migration between states, and the flow that we would predict based on a racially blind model that includes a wide variety of political and economic factors. Using this measure, I conduct a descriptive analysis and provide evidence that migrants from majority black states migrate far less than we would expect under a racially blind model. These results pave a new way for scholars to study international racial inequality.

This dataset presents the migration rate in small regions of Australia based on the 2016 Census and aggregated following the 2016 edition of the Australian Statistical Geography Standard (ASGS). The data has been provided by The National Centre for Social and Economic Modelling (NATSEM). The migration rate is the proportion of people in the area who were not born in Australia, that is, who have migrated to Australia in the past. All indicators were extracted from the ABS Tablebuilder system using the usual residence profile. For usual residence data, the ABS moves people back to where they live, rather than using the location the data were collected (place of enumeration). Usual residence data is preferred for individual level data because it removes the effect of respondents travelling or holidaying. For more information please view the NATSEM Technical Report. Please note: AURIN has spatially enabled the original data provided directly from NATSEM. Where data values are NULL, the data is either unpublished or not applicable mathematically. In the calculation, Inadequately Described, At sea, Not Stated and Overseas Visitor were excluded from both the numerator and denominator as there is no information on these respondents. Methodology between the 2016 NATSEM and 2011 OECD data release may have changed, please refer to the technical report for parity status and specific changes.

The purpose of this dataset is to provide a systematic set of standardised contextual (economic, socio-political, cultural and legal) indicators in order to identify and measure on a comparative basis those contextual factors that have an (beneficial or inhibiting) impact on European, but not exclusively, responses to mass migration. Attention has been paid to existing socio-economic conditions and to national policies related to immigrants and asylum seekers. In this respect, the dataset comprises a set of both macro-level indicators measuring the socio-economic, political and institutional context of migration and cultural – or individual-level – indicators addressing ordinary citizens’ subjective attitudes, behaviours and perceptions about migration related-phenomena (e.g. perceived discrimination on ethnic grounds; immigration being bad or good for a country's economy; a country's cultural life being undermined or enriched by immigration).

SM.POP.NETM. Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens. The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

In New Zealand, internal migration is typically the most difficult component of net migration’s contribution to subnational population change to measure. Internal migrants are not required to register their moves with any agency. The five-yearly census of population and dwellings has included a question on “usual residence five years ago” since 1971, which has been the authoritative data source for measuring internal migration. However, the infrequency of the collection (every five years), and the ‘snapshot’ nature of a transition-based measure are significant limitations. Other measures of annual subnational population change, such as the Treasury’s Insights tool, provide estimates of internal migration flows between TAs by using linked administrative data. Their approach identifies a set of decision rules for assigning location to individuals, based on a quality assessment of a wide range of address sources in the IDI (Where we come from, where we go). The TA location transitions provide the basis for deriving statistics of annual internal migration as demonstrated by the Insights tool. The data published with this report is the first series we’ve created by estimating all internal migration flows using a movement-based approach. From individuals’ unique address notification histories in key data sources, the paired origin and destination locations defined individuals’ movements. Traditionally, we combined change of address data from a range of administrative sources with other information on international migration to produce estimates of net migration for broad subnational areas. Now, we can derive direct estimates of movements from address histories from the anonymised unit record information of address notifications in the IDI. This gives a better understanding of people’s movements within New Zealand. Internal migration information is of great interest to local and central government, businesses, and communities. Churn and turnover of populations at local area level is one of the contributors of subnational population change, in both size and characteristics.

Read the full report here: https://www.stats.govt.nz/reports/internal-migration-estimates-using-linked-administrative-data-201417

Migration indicators from ONS and DWP. The table below details the sources of the datasets available and the dates of their next update. Migration Statistics Quarterly Report Statistical bulletins, ONS 26 November 2020 National Insurance numbers issued to overseas nationals, Stats-Xplore, DWP. 26 November 2020 Population Estimates for UK, England and Wales, Scotland and Northern Ireland, ONS June 2021 Local area migration indicators suite, ONS. TBA Internal migration - Detailed estimates dataset by origin and destination local authorities, sex and single year of age, ONS. June 2021 Population of the UK by country of birth and nationality, ONS. November 2020 Short term international migration for England and Wales – accompanying data Discontinued - latest available data for 2017

Ukraine UA: Net Migration data was reported at -200,000.000 Person in 2012. This records a decrease from the previous number of 269,541.000 Person for 2007. Ukraine UA: Net Migration data is updated yearly, averaging 104,383.000 Person from Dec 1962 (Median) to 2012, with 11 observations. The data reached an all-time high of 454,164.000 Person in 1962 and a record low of -462,264.000 Person in 1997. Ukraine UA: Net Migration data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ukraine – Table UA.World Bank: Population and Urbanization Statistics. Net migration is the net total of migrants during the period, that is, the total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.; ; United Nations Population Division. World Population Prospects: 2017 Revision.; Sum;

This dataset contains internationally comparable indicators regarding the migration of health care workforce for country members or partners of OECD (The Organization for Economic Co-operation and Development) and for countries in accession negotiations with OECD. The indicators values cover the period 2000-2016.

SM.POP.NETM. Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens. The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

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.

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