In 2022, an estimated 10.99 million unauthorized immigrants were living in the United States. This is an increase from about 3.5 million unauthorized immigrants who lived in the United States in 1990.

In 2019, California had the highest population of unauthorized immigrants, at around 2.74 million. The overall figure for the United States was estimated to be around 11.05 million unauthorized immigrants.

Unauthorized population estimates for 1990, 2000, and 2014.

In January 2022, it was estimated that about 1.85 million male illegal immigrants living in the United States were aged between 35 and 44 years old. In that same year, it was estimated that 1.52 million female illegal immigrants living in the U.S. were between 35 and 44 years old.

Emigration rates for border crossers during the 1990s.

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.

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

Abstract (en): The data comprising the Puerto Rico Census Project, 1920 contain individual and household records drawn from the 1920 Puerto Rican Population Census. The data include variables containing basic demographic information such as age, sex, race, marital status, number of children born and surviving, family size, place of birth, immigration status, county and neighborhood of residence, urban/rural status, and citizenship. The data also describe language proficiency, literacy, school attendance, and disabilities (blind or deaf) of the individuals. Other variables provide data on occupation, industry, ownership of residence, status of mortgage, and farm ownership. There are four classifications of variables belonging to this dataset: original input variables, coded variables, constructed variables, and quality flag variables. The original input variables contain the raw data collected by the enumerators. The coded variables are variables that were recoded by the University of Wisconsin Survey Center (UWSC) as part of the Puerto Rico Census Project. Constructed variables were produced by UWSC to capture additional relevant information. For example, one constructed variable measures literacy by combining separate variables containing data on whether the individual could read and if they could write. Finally, quality flag variables were created by UWSC to indicate whether it could be logically deduced that individual records had been hand edited by the Census Office. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Population (individuals and households) living in Puerto Rico in 1920. Smallest Geographic Unit: neighborhood Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. Eunice Kennedy Shriver National Institute of Child Health and Human Development (RO1-HD41053). The following variables have been masked to prevent any breach of ICPSR confidentiality standards: NAMELAST, NAMEFRST, ENUMNAME, and STREET.A number of the original input variables were initially captured as character variables. In the processing of this study, many of these were converted to numeric variables. Such variables contain the word, "original," in the variable label in order to distinguish it from the coded numeric variables produced by the University of Wisconsin Survey Center.

This statistic shows the estimated percentage of employed undocumented immigrants in the United States in 2016, distinguished by industry. In 2017, around 14.2 percent of agriculture workers were estimated to be undocumented immigrants.

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.

Abstract (en): This study provides annual estimates of the population of all counties and county equivalents in the United States for the years 1971-1974. Also included are county-level estimates of the components of population change (births, deaths, and net migration) for the same years. County equivalents are parishes in Louisiana, census divisions in Alaska, and independent cities in Virginia and Missouri. The dataset was prepared by ICPSR from Current Population Reports published by the Census Bureau and originally compiled by the Census Bureau with the assistance of designated state agencies in the Federal/State Cooperative Program for Population Estimates. Several ICPSR geographic identification variables were included in the dataset to improve its utility and maintain the comparability of the present data with other county-level materials in the ICPSR archive. ICPSR also calculated some variable values that were missing from the Census Bureau reports. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Checked for undocumented or out-of-range codes.. United States population between 1970-1974. The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Abstract (en): To reduce respondent burden for the 2013 collection, the Census of Jails was combined with the Deaths in Custody Reporting Program (DCRP). The census provides the sampling frame for the nationwide Survey of Inmates in Local Jails (SILJ) and the Annual Survey of Jails (ASJ). Previous jail enumerations were conducted in 1970 (ICPSR 7641), 1972 (ICPSR 7638), 1978 (ICPSR 7737), 1983 (ICPSR 8203), 1988 (ICPSR 9256), 1993 (ICPSR 6648), 1999 (ICPSR 3318), 2005 (ICPSR 20367), and 2006 (ICPSR 26602). The RTI International collected the data for the Bureau of Justice Statistics in 2013. The United States Census Bureau was the collection agent from 1970-2006. The 2013 Census of Jails gathered data from all jail detention facilities holding inmates beyond arraignment, a period normally exceeding 72 hours. Jail facilities were operated by cities and counties, by private entities under contract to correctional authorities, and by the Federal Bureau of Prisons (BOP). Excluded from the census were physically separate temporary holding facilities such as drunk tanks and police lockups that do not hold persons after being formally charged in court. Also excluded were state-operated facilities in Connecticut, Delaware, Hawaii, Rhode Island, Vermont, and Alaska, which have combined jail-prison systems. Fifteen independently operated jails in Alaska were included in the Census. The 2013 census collected facility-level information on the number of confined and nonconfined inmates, number of inmates participating in weekend programs, number of confined non-U.S. citizens, number of confined inmates by sex and adult or juvenile status, number of juveniles held as adults, conviction and sentencing status, offense type, number of inmates held by race or Hispanic origin, number of inmates held for other jurisdictions or authorities, average daily population, rated capacity, number of admissions and releases, program participation for nonconfined inmates, operating expenditures, and staff by occupational category. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Standardized missing values.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Datasets:DS1: Census of Jails, 2013 All locally, regionally, and federally administered jails in the United States. The respondent universe was derived from a facility list maintained by the Census Bureau for BJS, from correctional association directories, and from other secondary sources. Census forms were sent to each jail jurisdiction. In addition to a paper form, BJS offered respondents an electronic version via the internet, allowing them to complete and submit their completed questionnaires on-line. 2018-04-25 The dataset and the codebook have been updated2016-03-25 Two records needed to be updated. Funding insitution(s): United States Department of Justice. Office of Justice Programs. Bureau of Justice Statistics. mail questionnaire web-based survey

In 2022, the highest number of immigrants to Denmark for several years was registered, a number that must be seen in relation with the Russian invasion of Ukraine. That year, a total of 121,000 people immigrated to Denmark, compared to 76,000 the previous year. In 2023, the number fell below 100,000.

Eastern Europeans largest group of immigrants

Today, Ukrainians make up the second largest group of immigrants living in Denmark, a high number of who are refugees from the Russia-Ukraine war. Meanwhile, Poles made up the largest group. Many of these are people moving to Denmark for work.

Immigration drives population increase

With the annual number of people immigrating to Denmark being higher than the number of people emigrating, migration has played an important part in the country's increasing population.

Number of persons in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by immigrant status and age group, last 5 years.

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.

With nearly 49,000 living in Denmark as of January 1, 2024, most immigrants were from Poland. The second and third largest groups of immigrants were from Ukraine and Romania, amounting to roughly 41,000 and 40,000 people, respectively. Tightening immigration policies Like many European countries, Denmark experienced a heightened influx of immigrants in 2015. In the wake of the refugee situation, however, the number of immigrants, notably asylum seekers, declined in part due to sharpened immigration policies. In 2015, over 21,000 refugees applied for asylum in Denmark, whereas applications fell below 5,000 in 2022. Residence permits Among the different types of residence permits, permits granted based on asylum were the least delivered type of permit. Only 1,400 people were granted asylum in Denmark in 2022. The highest number of asylum seekers came from Ukraine, even when excluding Ukrainians arriving via the temporary protection act. In 2022, Denmark registered around 2,000 applications from Ukrainians.

The largest number of immigrants in Germany were from Ukraine, as of 2023. The top three origin countries were rounded up by Romania and Turkey. Immigrants are defined as having left a country, which may be their home country, to permanently reside in another. Upon arriving, immigrants do not hold the citizenship of the country they move to. Immigration in the EU All three aforementioned countries are members of the European Union, which means their citizens have freedom of movement between EU member states. In practice, this means that citizens of any EU member country may relocate between them to live and work there. Unrestricted by visas or residence permits, the search for university courses, jobs, retirement options, and places to live seems to be defined by an enormous amount of choice. However, even in this freedom of movement scheme, immigration may be hampered by bureaucratic hurdles or financial challenges. Prosperity with a question mark While Germany continues to be an attractive destination for foreigners both in and outside the European Union, as well as asylum applicants, it remains to be seen how current events might influence these patterns, whether the number of immigrants arriving from certain countries will shift. Europe’s largest economy is suffering. Climbing inflation levels in the last few months, as well as remaining difficulties from the ongoing coronavirus (COVID-19) pandemic are affecting global economic development. Ultimately, future immigrants may face the fact of moving from one struggling economy to another.

This statistic shows the number of immigrants in France in 2022, distributed by geographical origin. It displays that more than 887 thousand immigrants were born in Algeria, and more than 836 thousands in Morocco.

There were approximately 149,200 immigrants entering the Republic of Ireland in 2024, compared with 141,600 in the previous year. During the provided time period, the number of immigrants coming to Ireland peaked at 151,100 in 2007. Due to the departure of 69,900 people from Ireland in 2024, the net migration figure for this year was 79,300.

Colombia ranked as the country of origin of the largest immigration group arriving into Spain in 2023, as revealed by the latest data. Over 170,000 people migrated from the South American country to Spain that year. The second largest group was comprised by Moroccans with around 122,000 newcomers. 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 2023, Moroccans ranked first as the foreign nationality residing Spain, followed by people from Romania and the Colombians. 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 and Africa. Migration figures show that Germany accommodated approximately 15 million foreign-born citizens, ranking it as the country that most hosted immigrants in Europe in 2023. By comparison, Spain’s foreign population stood over eight million, positioning the Western Mediterranean country third on the European list of foreign-born population. Unfortunately, thousands of persons have died or 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.