In France, in 2021, ** percent of female immigrants who were born in a country outside the EU were working in the services to enterprises sector.
In 2023, the employment rate of immigrants was **** percent, eight percentage points lower than that of people with no migration background.

As of 2021, Germany was the European Union country which saw the largest number of immigrants from non-EU countries, with over 430,000 migrants with non-EU citizenship moving to Germany. Spain was the country with the second largets number of extra-EU immigrants, at roughly 346,000 people, while Italy and France saw 200,000 and 170,000 respectively.

This statistic shows the share of foreign nationals in the total population of European Union member states in 2022. Foreign nationals are people who do not possess the nationality of the country they are residing in permanently. This includes stateless. In 2022, the share of foreign nationals in Luxembourg's population amounted to 47.1 percent.

With over **** million foreign persons residing in ******* in 2023, the country had the highest number of foreign-born people living in its territory among the 27 Member States of the European Union. Followed by ****** with around *** million and Spain at over ***** million.

Table of INEBase Percentage of foreign population by Autonomous Community and province, sex, member EU/Non member EU and size of municipality. Autonomous Community and province. Continuous Register Statistics

Percentage of enrolment in post-secondary non-tertiary education in private institutions (%) in European Union was reported at 39.71 % in 2019, according to the World Bank collection of development indicators, compiled from officially recognized sources. European Union - Percentage of enrolment in post-secondary non-tertiary education in private institutions - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

In 2021, Germany, Spain, and France were the countries which saw the highest immigration in the European Union. Germany alone say over 870,000 immigrants entering the country in that year, with a majority coming from non-EU countries. There is significant variation in the make-up of the inflows of migrants in different EU member states, with countries such as Spain and Italy seeing large majorities coming from outside the EU, while France and Germany saw roughly an equal number of migrants coming from other EU countries or being returning citizens of those countries. The Netherlands and Belgium stand out as countries which saw more intra-EU migrants than non-EU migrants, with approximately 90,000 and 63,000 moving to these countries respectively from within the EU. Several EU member states saw the greatest share of migrants being citizens of the country themselves, with Romania, Ireland, Greece, and Portugal being notable in this respect. These countries have all seen large flows of people working in other EU member states in recent years, who in many case return to their country of origin within a couple of years.

The data on 'EU foreigners' and 'Non-EU foreigners' for various years was was downloaded for Urban Audit cities from Eurostat, added together and joined spatially with the Urban Audit 2011-14 city centroids, also downloaded from Eurostat (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14)Subject: people born in a different country to the country of residence may be more vulnerable to climate-related hazards such as heatwaves and flooding. This is because they may not speak the official language, or their knowledge of this language is not sufficient to understand the warnings and communicate with the emergency services. Also, these people may be less familiar with the area and the specificity of climate hazards there. They may often live in rented accommodation, which means that they may not be able to make changes to their dwelling to prepare it better for extreme weather events. Considering the proportion of the population that is foreign-born is thus an important aspects of adaptation planning in a city.

Percentage of non-EU foreigners among foreigners on December 31, 2016 (MSS 2017, PLR, context indicator: K 17), map with equidistant group formation

Statistics Netherlands collects data about people in the Netherlands from various registers. This table contains information on non-Dutch immigrants aged 16 to 65 who settled in the Netherlands between 1999 and 2003. The information consists of data on the migration motive, year of residence, gender and the main source of income. In the table, the most important sources of income are expressed per migration motive as a percentage of the total (selected) group with that migration motive. Data available from: 1999 Frequency: discontinued Status of the figures All figures included in the table are final.

While the overall impact of naturalisation requirements on citizenship acquisition rates is well understood, the relevance of economic requirement remains largely understudied in Europe. In this chapter we look at the case of the Netherlands, where fees have increased from 336 euro in 2003 to 901 euro in 2020 – an increase of 168% points – for a single application, with significant hikes in the fee in 2010 and 2011. Simultaneous changes in the civic integration requirements for permanent residence likely had a positive effect on naturalisation rates among non-EU immigrants and consequently may have obfuscated the impact of the higher fees. We draw on longitudinal microdata from administrative registers on the complete immigrant population between 2007 and 2014, and use a two-step identification strategy. First, we apply a single-difference regression, based on a fixed-effects model, to investigate immigrant naturalisation rates in conjunction with increased application costs. We subsequently explore impact heterogeneity by household income and use a double-difference regression, based on a difference-in-differences model, to test whether the relevance of the fee increase is conditioned by income groups. Results from our single-difference models reveal a decrease in EU immigrants’ naturalisation rate after the fee increase in 2010, all else constant. Consistent with our expectation that economic requirements matter particularly to immigrants with limited financial means, subgroup analyses show a stronger decrease among those with below modal household incomes compared to immigrants with higher incomes. Double-difference models confirm that the main findings cannot be fully attributed to unmeasured period shocks, and that there is indeed a statistically significant difference in the relevance of the fee increase by household income.

BackgroundHigher risks of psychiatric disorders and lower-than-average subjective health in adulthood have been demonstrated in offspring of immigrants in Sweden compared with offspring of native Swedes, and linked to relative socioeconomic disadvantage. The present study investigated mortality rates in relation to this inequity from a gender perspective.MethodsWe used data from national registers covering the entire Swedish population aged 18-65 years. Offspring of foreign-born parents who were either Swedish born or had received residency in Sweden before school age (<7 years) were defined as “offspring of immigrants.” We used Cox regression models to examine the association between parental country of birth and mortality between 1990 and 2008, with adjustment for education, income, age and family type.ResultsMale offspring of immigrants from the Middle East (HR:2.00, CI:1.66-2.26), other non-European countries (HR:1.80, CI:1.36-2.36) and Finland (HR:1.56, CI:1.48-1.65) showed an age-adjusted excess mortality risk from all causes of death when compared to offspring with Swedish-born parents. Income, but not education, greatly attenuated these increased mortality risks. No excess mortality rates were found among female offspring of immigrants, with the exception of external cause of death among offspring of Finnish immigrants.ConclusionThe study demonstrates high mortality rates in male offspring of immigrants from Finland and non-European countries that are associated with economic, but not educational, disadvantage. No increased mortality rates were found among female offspring of immigrants. Future studies are needed to explain this gender differential and why income, but not education, predicts mortality in male offspring of immigrants.

Notes on the basis for this dataset: This dataset is based on a Eurostat dataset (ISOC_CI_CFP_CU):

Online data code:ISOC_CI_CFP_CU Source of data:Eurostat Last data update:10/05/2023 11:00 Last structure update:08/02/2021 23:00 Data navigation tree location: Science, technology, digital society > Digital economy and society > ICT usage in households and by individuals > Connection to the internet and computer use Cross cutting topics > Skills-related statistics > Skills supply - self-reported measures > Digital skills - ICT usage in households and by individuals > Internet and computer use

Header and data descritions of the filtered dataset: This filtered dataset contains the following headers and the corresponding data:

date [year in format yyyy form 2007 untill (and including) 2017 in reverse order; last line in the filtered dataset contains increase in percent-points] ATHN [Neutron Monitor in Athens, Greece, Europe; data: neutron detections per second averaged over a 1 year period] AT [ Austria , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] BE [ Belgium , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] BG [ Bulgaria , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] CY [ Cyprus , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] CZ [ Czechia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] DE [ Germany , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] DK[ Denmark , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EE [ Estonia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EL [ Greece , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] ES [ Spain , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EU28 [all 28 member countries of the EU between 2007 and 2017, Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] FI [ Finland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] FR [ France , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] HR [ Croatia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] HU [ Hungary , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] IE [ Ireland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] IT [ Italy , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LT [ Lithuania , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LU [ Luxembourg , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LV [ Latvia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] MT [ Malta , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] NL [ Netherlands , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] PL [ Poland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] PT [ Portugal , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] RO [ Romania , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SE [ Sweden , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SI [ Slovenia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SK [ Slovakia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] UK [ United Kingdom , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points]

Obtaining the filtered dataset:

Economically Active Population Survey: Persons between 16 and 74 years old with foreign nationality and not from the European Union, according to whether or not the duration of their residency permit is limited, by sex and nationality. Autonomous Communities.

According to data, the largest share of female immigrants regularly living in Italy came from Russia (81.2 percent of the total number of Russians in Italy), followed by Ukraine with 78.6 percent. In 2020, Morocco was the most common country of origin of foreigners in Italy. They amounted to almost 430 thousand people. Females made up about 47 percent of the total.

Farming and herding were introduced to Europe from the Near East and Anatolia; there are, however, considerable arguments about the mechanisms of this transition. Were it the people who moved and either outplaced, or admixed with, the indigenous hunter-gatherer groups? Or was it material and information that moved---the Neolithic Package---consisting of domesticated plants and animals and the knowledge of their use? The latter process is commonly referred to as cultural diffusion and the former as demic diffusion. Despite continuous and partly combined efforts by archaeologists, anthropologists, linguists, palaeontologists and geneticists, a final resolution of the debate has not yet been reached. In the present contribution we interpret results from the Global Land Use and technological Evolution Simulator (GLUES). GLUES is a mathematical model for regional sociocultural development, embedded in the geoenvironmental context, during the Holocene. We demonstrate that the model is able to realistically hindcast the expansion speed and the inhomogeneous space-time evolution of the transition to agropastoralism in western Eurasia. In contrast to models that do not resolve endogenous sociocultural dynamics, our model describes and explains how and why the Neolithic advanced in stages. We uncouple the mechanisms of migration and information exchange and also of migration and the spread of agropastoralism. We find that: (1) An indigenous form of agropastoralism could well have arisen in certain Mediterranean landscapes, but not in Northern and Central Europe, where it depended on imported technology and material. (2) Both demic diffusion by migration and cultural diffusion by trade may explain the western European transition equally well. (3) Migrating farmers apparently contribute less than local adopters to the establishment of agropastoralism. Our study thus underlines the importance of adoption of introduced technologies and economies by resident foragers. Parameters:(1) Time - Unit: simulation years since 0001-01-01, Range: -8000 to -3500(2) Latitude - Unit: degree_north, Range: 31 to 57(3) Longitude - Unit: degree_east, Range: -10 to 42(4) Region - Description: Unique integer index of land region, Valid range: 1 to 685(5) Farming - Description: fraction of agriculturalist and pastoralist activities in population, Range: 0.0 to 1.0(5) Timing of farming - Description: Time when >=50% are devoted to farming, Units: simulation years since 0001-01-01(6) Percentage of immigrant farmers, Valid range 0.0 to 1.0Data are presented as instantaneous values every 50 years on a geographic grid with half degree resolution, where latitude and longitude values denote the central geographic location within a grid cell.

Suspect percentages, benefits, work, household type, non-western immigrants in the area. Origin, age, gender. 1999 - 2006. Changed February 20, 2009. Frequency: Discontinued.

European Union EA: Credit Terms and Conditions-NPL Ratio-Enterprise: Weighted Net Percentage: BL data was reported at 6.970 % in Mar 2025. This records a decrease from the previous number of 7.192 % for Sep 2024. European Union EA: Credit Terms and Conditions-NPL Ratio-Enterprise: Weighted Net Percentage: BL data is updated quarterly, averaging 4.060 % from Sep 2018 (Median) to Mar 2025, with 14 observations. The data reached an all-time high of 10.213 % in Sep 2020 and a record low of -1.426 % in Mar 2022. European Union EA: Credit Terms and Conditions-NPL Ratio-Enterprise: Weighted Net Percentage: BL data remains active status in CEIC and is reported by European Central Bank. The data is categorized under Global Database’s European Union – Table EU.KB021: European Central Bank: Bank Lending Survey: Non-Performing Loans Ratio: by Enterprise.

This indicator measures by municipality the number and proportion of services located in flood-prone areas intended for the general population (excluding strategic public services) or intended for vulnerable populations . The result is a number and a percentage. Exposed buildings: the categories taken into account are sports halls and grounds, social centres, association halls, leisure centres, shopping centres, pharmacies, medical analysis laboratories, home help and care centres, day centers for vulnerable populations, etc.

As recorded by the source, Moroccans ranked as the foreign nationality with more residents in Spain in 2023, closely followed by Romanians. After years of losing its foreign population, Spain’s immigration figures started to pick up in 2015, with the number of people that moved to the Mediterranean country surpassing the number of foreigners that decided to leave.

A matter of balance The net migration rate of Spain changed its course mainly due to the great inflow of foreigners that move to reside in the Mediterranean country. Spain’s immigration flow slowed down after the 2008 financial crisis, albeit the number of foreigners that opted to change their residence saw a significant growth in the last years. In 2022, Colombians ranked first as the foreign nationality that most relocated to Spain, distantly followed by Moroccans and Ukranians.

Spain does not have the highest number of immigrants in Europe In recent years, the European Union confronted a rising number of refugees arriving from the Middle East. Migration figures show that Germany accommodated approximately 15 million foreign-born citizens, ranking it as the country that most hosted immigrants in Europe in 2022. By comparison, Spain’s foreign population stood slightly over seven million, positioning the Western Mediterranean country third on the European list of foreign-born population. Unfortunately, thousands of persons have died ore gone missing trying to reach Spanish territory, as more and more irregular migrants opt to use dangerous maritime routes to arrive at Southern Europe from Africa's coasts.