Dataset Overview

This dataset provides comprehensive statistics on migration in the United Kingdom from 1901 to 2023. It includes data on immigration, emigration, net migration, and detailed breakdowns by nationality, reason for migration, visa categories, and regional distributions. The data is sourced from the UK Parliament’s Commons Library briefing paper titled “Migration Statistics”, which aims to explain the concepts and methods used in measuring migration and offers a range of data on migration in the UK and European Union countries.

Dataset Folder: CBP06077-data.xlsx

2.2 (1) - Long-term international migration estimates in the UK

2.2 (2) - Estimated average annual net migration in the UK, 1901-2021

2.5 - Long-term international migration estimates in the UK, by nationality

2.6 (1) - Immigration by main reason for migration

2.6 (2) - Entry clearance visas granted by category, excluding tourist visas

2.6 (3) - Work visas granted by current category and prior equivalent

4.1 - Immigration and net migration of foreign nationals in EU countries and the UK, 2021

4.2 - Foreign-national and foreign-born populations of EU countries, 2021

5.1 - Estimated number of EU nationals living in the UK by nationality, 2021

5.2 - EU nationals by region, United Kingdom, 2021

5.4 (1) - Estimated number of British nationals living in EU countries, 2017

5.4 (2) - UN estimates of British citizens living in other EU countries, 2020

Cover Note - Additional information about the dataset

Dataset Contents

The dataset comprises multiple Excel files, each corresponding to specific tables and figures from the original report. Below is a detailed description of each file:

1. Long-term International Migration Estimates in the UK

•  Filename: long_term_international_migration_estimates_uk.xlsx
•  Description: Annual estimates of immigration, emigration, and net migration in the UK from 1991 to 2012.
•  Columns:
•  Year ending
•  Immigration
•  Emigration
•  Net migration

2. Estimated Average Annual Net Migration in the UK (1901-2021)

•  Filename: estimated_average_annual_net_migration_1901_2021.xlsx
•  Description: Decadal average net migration estimates based on census data from 1901 to 2012.
•  Columns:
•  Decade
•  Censuses ending
•  Average annual net migration

3. Long-term International Migration Estimates by Nationality

•  Filename: long_term_migration_by_nationality.xlsx
•  Description: Immigration, emigration, and net migration figures broken down by British, EU, and Non-EU nationals from 1991 to 2012.
•  Columns:
•  Year ending
•  Immigration: British, EU, Non-EU
•  Emigration: British, EU, Non-EU
•  Net migration: British, EU, Non-EU

4. Immigration by Main Reason for Migration

•  Filename: immigration_by_reason.xlsx
•  Description: Immigration figures categorized by main reasons such as work, accompanying/joining family, study, other, and none stated, from 1991 to 2012.
•  Columns:
•  Year ending
•  Work related
•  Accompany/Join
•  Study
•  Other
•  None Stated

5. Entry Clearance Visas Granted by Category (Excluding Tourist Visas)

•  Filename: entry_clearance_visas_granted.xlsx
•  Description: Data on entry clearance visas granted in work, study, family, and other categories from 2006 to 2024.
•  Columns:
•  Year
•  Work: Main applicants, Including dependants
•  Study: Main applicants, Including dependants
•  Family: All
•  Other: All

6. Work Visas Granted by Category

•  Filename: work_visas_granted_by_category.xlsx
•  Description: Details of work visas granted, categorized into Worker (T2), Temporary Worker (T5), Investor/Business Development/Talent (T1), and others from 2010 to 2024.
•  Columns:
•  Year
•  Worker (T2)
•  Temporary Worker (T5)
•  Investor, Business Development and Talent (T1)
•  Other
•  Total

7. Immigration and Net Migration of Foreign Nationals in EU Countries (2021)

•  Filename: immigration_net_migration_eu_2021.xlsx
•  Description: Immigration and net migration figures of foreign nationals in EU countries and the UK for the year 2021.
•  Columns:
•  Country
•  Immigration
•  Net migration

8. Foreign-national and Foreign-born Populations of EU Countries (2021)

•  Filename: foreign_population_eu_2021.xlsx
•  Description: Number and percentage of foreign-national and foreign-born populations in EU countries as of 2021.
•  Columns:
•  Country
•  FOREIGN NATIONAL: Number, As % of population
•  FOREIGN BORN: Number, As % of population
•  Total Population

9. Estimated Number of EU Nationals Living in the UK by Nationality (2021)

•  Filename: eu_nationals_in_uk_2021.xlsx
•  Description: Estimates of EU nationals residing in the UK, broken down by country of nationality for 2021.
•  Columns:
•  Country of nationality
•  Stock

10. EU Nationals by Region in the UK (2021)

•  Filename: eu_nationals_by_region_uk_2021.xlsx
•  Descri...

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

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

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2022, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

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

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

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

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

This is the interview file.

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

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

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