This statistic shows the percentage of the population of select major cities who were foreign-born in 2015. In 2015, 83 percent of the population of Dubai were born outside of the United Arab Emirates,

The statistic represents the percentage of foreign-born residents in selected metropolitan areas around the world. With 83 percent of foreign-born residents, Dubai is the city with the largest percentage of foreign-born residents, as of 2009.

As of 2023, 27.3 percent of California's population were born in a country other than the United States. New Jersey, New York, Florida, and Nevada rounded out the top five states with the largest population of foreign born residents in that year. For the country as a whole, 14.3 percent of residents were foreign born.

Continuous Register Statistics: Population (spanish/foreigners) by country of birth and sex. Annual. Provinces.

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

Number of Immigrants: CF: City of Moscow data was reported at 1,096.000 Person in Jan 2024. This records a decrease from the previous number of 1,842.000 Person for Dec 2023. Number of Immigrants: CF: City of Moscow data is updated monthly, averaging 1,469.000 Person from Jan 1998 (Median) to Jan 2024, with 311 observations. The data reached an all-time high of 4,184.000 Person in Mar 2019 and a record low of 318.000 Person in Jan 2019. Number of Immigrants: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GE004: Number of Immigrants: by Region.

Continuous Population Statistics: Resident population by date, sex, age group and place of birth (Spanish/foreign). Quarterly. Autonomous Communities and Cities.

In 2023, the biggest communities of foreign nationals in Italy were in Milan, Bologna, Florence, Turin, Rome, and Genoa, where more than ten percent of the inhabitants were not of Italian origin. These cities are mostly located in the north of the country. On the contrary, in the southern municipalities of Bari, Catania, and Palermo the incidence of the immigrant population is minimal, well below five percent. Italian demographics In 2024, beyond five million foreign residents lived in Italy, compared to the total population of 59 million inhabitants. Projections assert that in the upcoming years, the number of Italian citizens will progressively decrease, mostly given to the aging population and low birth rates. In fact, it has been predicted that the median age could reach 53.6 years by 2050, whereas the country experienced a constant decline in the number of births. In 2010, almost 550,000 babies came into life, but ten years later only 400,000 births were recorded. The divide between north and south From the distribution of immigrant residents, there is an evident separation between the northern Italian regions and the southern part of the country, making those territories less attractive for foreigners in terms of work opportunities. Analysis on the index of the gross domestic product (GDP) per capita in 2005, in 2015 and 2025 reveal that the total wealth produced by the southern region represents only half of the one recorded in the north. Moreover, in 2023 the unemployment rate in northern regions was around four percent, whereas in the south it reached 14 percent.

Continuous Population Statistics: Immigration from abroad, by quarter and country of birth (top 3 countries). Quarterly. Autonomous Communities and Cities.

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

Population and Housing Censuses: Percentage of the population born abroad. Autonomous Communities and provinces.

Continuous Population Statistics: Emigration abroad, by quarter and country of birth (top 3 countries). Quarterly. Autonomous Communities and Cities.

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.

Area, village number, existing house number, village number household registration, number of neighbors, existing house number of neighbors, household number, gender, population, total number of immigrants, immigrants from abroad, immigrants from other provinces and cities in New Taipei City, immigrants from other provinces and cities in Taipei City, immigrants from other provinces and cities in Taoyuan City, immigrants from other provinces and cities in Taichung City, immigrants from other provinces and cities in Tainan City, immigrants from other provinces and cities in Kaohsiung City, immigrants from other provinces and cities in Taiwan Province, immigrants from other provinces and cities in Fujian Province, immigrants from other provinces and cities in other provinces and cities, immigrants from other counties or cities in this province, immigrants from other counties, towns, or districts in this county or city, initial household registration, others, total number of emigrants, emigrants to foreign countries, emigrants to other provinces and cities in New Taipei City, emigrants to other provinces and cities in Taipei City, emigrants to other provinces and cities in Taoyuan City, emigrants to other provinces and cities in Taichung City, emigrants to other provinces and cities in Tainan City, emigrants to other provinces and cities in Kaohsiung City, emigrants to other provinces and cities in Taiwan Province, emigrants to other provinces and cities in Fujian Province, emigrants to other provinces and cities in other provinces and cities, emigrants to other counties or cities in this province, emigrants to other counties, towns, or districts in this county or city, cancellation of household registration, others, number of address changes in, number of address changes out, increase in the number of administrative area adjustments, decrease in the number of administrative area adjustments, total number of births, total number of births within marriage, total number of births out of wedlock already claimed, total number of births out of wedlock unclaimed, total number of unattended children, birth mother originally from Mainland China, Hong Kong, Macau and Taiwan, birth mother originally from foreign countries, birth father originally from Mainland China, Hong Kong, Macau and Taiwan, birth father originally from foreign countries, posthumous child, twins, triplets or more, number of deaths, number of adoptions, number of adoptions terminated, number of guardians, number of assistants, number of minors bearing the responsibilities of rights and obligations, number of marriages, number of divorces.

Social and economic figures for 67 large West German cities. The data aggregated at city level have been collected for most topics over several years, but not necessarily over the entire reference time period.

Topics: 1. Situation of the city: surface area of the city; fringe location in the Federal Republic.

1. Residential population: total residential population; German and foreign residential population.

2. Population movement:live births; deaths; influx; departures; birth rate; death rate; population shifts; divorce rate; migration rate; illegitimate births.

3. Education figures: school degrees; occupational degrees; university degrees.

4. Wage and income: number of taxpayers in the various tax classes as well as municipality income tax revenue in the respective classes; calculated income figures, such as e.g. inequality of income distribution, mean income or mean wage of employees as well as standard deviation of these figures; GINI index.

5. Gross domestic product and gross product: gross product altogether; gross product organized according to area of business; gross domestic product; employees in the economic sectors.

6. Taxes and debts: debt per resident; income tax and business tax to which the municipality is entitled; municipality tax potential and indicators for municipality economic strength.

7. Debt repayment and management expenditures: debt repayment, interest expenditures, management expenditures and personnel expenditures.

8. From the ´BUNTE´ City Test of 1979 based on 100 respondents per city averages of satisfaction were calculated. satisfaction with: central location of the city, the number of green areas, historical buildings, the number of high-rises, the variety of the citizens, openness to the world, the dialect spoken, the sociability, the density of the traffic network, the OEPNV prices {local public passenger transport}, the supply of public transportation, provision with culture, the selection for consumers, the climate, clean air, noise pollution, the leisure selection, real estate prices, the supply of residences, one´s own payment, the job market selection, the distance from work, the number of one´s friends, contact opportunities, receptiveness of the neighbors, local recreational areas, sport opportunities and the selection of further education possibilities.

9. Traffic and economy: airport and Intercity connection; number of kilometers of subway available, kilometers of streetcar, and kilometers of bus lines per resident; car rate; index of traffic quality; commuters; property prices; prices for one´s own home; purchasing power.

10. Crime: recorded total crime and classification according to armed robbery, theft from living-rooms, of automobiles as well as from motor vehicles, robberies and purse snatching; classification according to young or adult suspects with these crimes; crime stress figures. 12. Welfare: welfare recipients and social expenditures; proportion of welfare recipients in the total population and classification according to German and foreign recipients; aid with livelihood; expenditures according to the youth welfare law; kindergarten openings; culture expenditures per resident. 13. Foreigners: proportion of foreigners in the residential population.

11. Students: number of German students and total number of students; proportion of students in the residential population.

12. Unemployed: unemployment rate; unemployed according to employment office districts and employment office departments.

13. Places of work: workers employed in companies, organized according to area of business.

14. Government employees: full-time, part-time and total government employees of federal government, states and municipalities as well as differentiated according to workers, employees, civil servants and judges.

15. Employees covered by social security according to education and branch of economy: proportion of various education levels in the individual branches of the economy.

Continuous Population Statistics: Population residing in family dwellings by date, sex, age group and place of birth (Spanish/foreign). Quarterly. Autonomous Communities and Cities.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Foreign born excludes people born outside the United States to a parent who is a U.S. citizen..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Table of INEBase Immigration from abroad, by quarter and country of birth (top 3 countries). Quarterly. Autonomous Communities and Cities. Continuous Population Statistics

Many western liberal democracies have witnessed increased discrimination against immigrants and opposition to multiculturalism. Prior research identifies ethno-linguistic differences between immigrant and native populations as the key source of such bias. Linguistic assimilation has therefore been proposed as an important mechanism to reduce discrimination and mitigate conflict between natives and immigrants. Using large-scale field experiments conducted in 29 cities across Germany--a country with a high influx of immigrants and refugees--we empirically test whether linguistic assimilation reduces discrimination against Muslim immigrants in every-day social interactions. We find that it does not; Muslim immigrants are no less likely to be discriminated against even if they appear to be linguistically assimilated. However, we also find that ethno-linguistic differences alone do not cause bias among natives in a country with a large immigrant population and state policies that encourage multiculturalism.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..Foreign born excludes people born outside the United States to a parent who is a U.S. citizen..While the 2018 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas, in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available....