United States Immigrants Admitted: All Countries data was reported at 1,127,167.000 Person in 2017. This records a decrease from the previous number of 1,183,505.000 Person for 2016. United States Immigrants Admitted: All Countries data is updated yearly, averaging 451,510.000 Person from Sep 1900 (Median) to 2017, with 118 observations. The data reached an all-time high of 1,827,167.000 Person in 1991 and a record low of 23,068.000 Person in 1933. United States Immigrants Admitted: All Countries data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s United States – Table US.G087: Immigration.

The estimated population of unauthorized immigrants in the U.S. stands at around ** million people. Although the number has stabilized, the United States has seen a spike in migrant encounters in the last few years, with over * million cases registered by the U.S. Border Patrol in 2023. This is a slight decrease from the previous year, when there were over *** million cases registered. Due to its proximity and shared border, Mexico remains the leading country of origin for most undocumented immigrants in the U.S., with California and Texas being home to the majority.

Immigration and political division

Despite the majority of the population having immigrant roots, the topic of immigration in the U.S. remains one of the country’s longest-standing political debates. Support among Republicans for restrictive immigration has grown alongside Democratic support for open immigration. This growing divide has deepened the polarization between the two major political parties, stifling constructive dialogue and impeding meaningful reform efforts and as a result, has led to dissatisfaction from all sides. In addition to general immigration policy, feelings toward illegal immigration in the U.S. also vary widely. For some, it's seen as a significant threat to national security, cultural identity, and economic stability. This perspective often aligns with support for stringent measures like Trump's proposed border wall and increased enforcement efforts. On the other hand, there are those who are more sympathetic toward undocumented immigrants, as demonstrated by support for the Deferred Action for Childhood Arrivals (DACA) program.

Virginia has a sizable immigrant community. About 12.3 percent of the state’s residents are foreign-born, and 6.7 percent of its U.S.-born residents live with at least one immigrant parent. Immigrants make up 15.6 percent of Virginia's labor force and support the local economy in many ways. They account for 20.7 percent of entrepreneurs, 21.8 percent of STEM workers, and 12.7 percent of nurses in the state. As neighbors, business owners, taxpayers, and workers, immigrants are an integral part of Virginia’s diverse and thriving communities and make extensive contributions that benefit all.

This table contains 36 series, with data for years 1953 - 1976 (not all combinations necessarily have data for all years), and was last released on 2012-02-16. This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Origin of migrants (2 items: Overseas and the United States; Overseas ...) Intended occupations and dependents (18 items: Total immigration; Workers; managerial and administrative; Workers; professional; Total workers ...).

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.

This layer shows children by nativity of parents by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of children who are in immigrant families (children who are foreign born or live with at least one parent who is foreign born). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B05009Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This table contains 25 series, with data for years 1955 - 2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Last permanent residence (25 items: Total immigrants; France; Great Britain; Total Europe ...).

We note that we include only do files, and a log file of our work and not any raw data. This is because, as we note in the online appendix, we use individual level data from Swedish registers. The data material is located on an encrypted server to which we have to log in through a remote desktop application in order to perform all of our data analyses. Due to the sensitivity of the data, we are under contractual and ethical obligation not to distribute these data to others. For those researchers who want to replicate our results there are two ways to get access to the ad- ministrative data. The first way is to order the data directly from Statistics Sweden (SCB). Statis- tics Sweden presently requires that researchers obtain permission from the Swedish Ethical Review Board before data can be ordered (a description of how to order data from Statistics Sweden is available at: https://www.scb.se/en/services/guidance-for-researchers-and-universities/). We will also make available a complete list all of the variables that we ordered from Statistics Sweden for this project, together with the statistical code used for the analyses. The second way to replicate our analyses is to come to Sweden and reanalyze these data through the same remote server system that we used. Researchers interested in using this option should reach out to us prior to coming to Sweden so that we can apply for approval from the Ethical Review Board for the researcher to temporarily be added to our research team, which is mandatory in order to get access to the remote server system.

Analysis of ‘Missing Migrants Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jmataya/missingmigrants on 14 February 2022.

--- Dataset description provided by original source is as follows ---

About the Missing Migrants Data

This data is sourced from the International Organization for Migration. The data is part of a specific project called the Missing Migrants Project which tracks deaths of migrants, including refugees , who have gone missing along mixed migration routes worldwide. The research behind this project began with the October 2013 tragedies, when at least 368 individuals died in two shipwrecks near the Italian island of Lampedusa. Since then, Missing Migrants Project has developed into an important hub and advocacy source of information that media, researchers, and the general public access for the latest information.

Where is the data from?

Missing Migrants Project data are compiled from a variety of sources. Sources vary depending on the region and broadly include data from national authorities, such as Coast Guards and Medical Examiners; media reports; NGOs; and interviews with survivors of shipwrecks. In the Mediterranean region, data are relayed from relevant national authorities to IOM field missions, who then share it with the Missing Migrants Project team. Data are also obtained by IOM and other organizations that receive survivors at landing points in Italy and Greece. In other cases, media reports are used. IOM and UNHCR also regularly coordinate on such data to ensure consistency. Data on the U.S./Mexico border are compiled based on data from U.S. county medical examiners and sheriff’s offices, as well as media reports for deaths occurring on the Mexico side of the border. Estimates within Mexico and Central America are based primarily on media and year-end government reports. Data on the Bay of Bengal are drawn from reports by UNHCR and NGOs. In the Horn of Africa, data are obtained from media and NGOs. Data for other regions is drawn from a combination of sources, including media and grassroots organizations. In all regions, Missing Migrants Projectdata represents minimum estimates and are potentially lower than in actuality.

Updated data and visuals can be found here: https://missingmigrants.iom.int/

Who is included in Missing Migrants Project data?

IOM defines a migrant as any person who is moving or has moved across an international border or within a State away from his/her habitual place of residence, regardless of

  (1) the person’s legal status; 
  (2) whether the movement is voluntary or involuntary; 
  (3) what the causes for the movement are; or 
  (4) what the length of the stay is.[1]

Missing Migrants Project counts migrants who have died or gone missing at the external borders of states, or in the process of migration towards an international destination. The count excludes deaths that occur in immigration detention facilities, during deportation, or after forced return to a migrant’s homeland, as well as deaths more loosely connected with migrants’ irregular status, such as those resulting from labour exploitation. Migrants who die or go missing after they are established in a new home are also not included in the data, so deaths in refugee camps or housing are excluded. This approach is chosen because deaths that occur at physical borders and while en route represent a more clearly definable category, and inform what migration routes are most dangerous. Data and knowledge of the risks and vulnerabilities faced by migrants in destination countries, including death, should not be neglected, rather tracked as a distinct category.

How complete is the data on dead and missing migrants?

Data on fatalities during the migration process are challenging to collect for a number of reasons, most stemming from the irregular nature of migratory journeys on which deaths tend to occur. For one, deaths often occur in remote areas on routes chosen with the explicit aim of evading detection. Countless bodies are never found, and rarely do these deaths come to the attention of authorities or the media. Furthermore, when deaths occur at sea, frequently not all bodies are recovered - sometimes with hundreds missing from one shipwreck - and the precise number of missing is often unknown. In 2015, over 50 per cent of deaths recorded by the Missing Migrants Project refer to migrants who are presumed dead and whose bodies have not been found, mainly at sea.

Data are also challenging to collect as reporting on deaths is poor, and the data that does exist are highly scattered. Few official sources are collecting data systematically. Many counts of death rely on media as a source. Coverage can be spotty and incomplete. In addition, the involvement of criminal actors in incidents means there may be fear among survivors to report deaths and some deaths may be actively covered-up. The irregular immigration status of many migrants, and at times their families as well, also impedes reporting of missing persons or deaths.

The varying quality and comprehensiveness of data by region in attempting to estimate deaths globally may exaggerate the share of deaths that occur in some regions, while under-representing the share occurring in others.

What can be understood through this data?

The available data can give an indication of changing conditions and trends related to migration routes and the people travelling on them, which can be relevant for policy making and protection plans. Data can be useful to determine the relative risks of irregular migration routes. For example, Missing Migrants Project data show that despite the increase in migrant flows through the eastern Mediterranean in 2015, the central Mediterranean remained the more deadly route. In 2015, nearly two people died out of every 100 travellers (1.85%) crossing the Central route, as opposed to one out of every 1,000 that crossed from Turkey to Greece (0.095%). From the data, we can also get a sense of whether groups like women and children face additional vulnerabilities on migration routes.

However, it is important to note that because of the challenges in data collection for the missing and dead, basic demographic information on the deceased is rarely known. Often migrants in mixed migration flows do not carry appropriate identification. When bodies are found it may not be possible to identify them or to determine basic demographic information. In the data compiled by Missing Migrants Project, sex of the deceased is unknown in over 80% of cases. Region of origin has been determined for the majority of the deceased. Even this information is at times extrapolated based on available information – for instance if all survivors of a shipwreck are of one origin it was assumed those missing also came from the same region.

The Missing Migrants Project dataset includes coordinates for where incidents of death took place, which indicates where the risks to migrants may be highest. However, it should be noted that all coordinates are estimates.

Why collect data on missing and dead migrants?

By counting lives lost during migration, even if the result is only an informed estimate, we at least acknowledge the fact of these deaths. What before was vague and ill-defined is now a quantified tragedy that must be addressed. Politically, the availability of official data is important. The lack of political commitment at national and international levels to record and account for migrant deaths reflects and contributes to a lack of concern more broadly for the safety and well-being of migrants, including asylum-seekers. Further, it drives public apathy, ignorance, and the dehumanization of these groups.

Data are crucial to better understand the profiles of those who are most at risk and to tailor policies to better assist migrants and prevent loss of life. Ultimately, improved data should contribute to efforts to better understand the causes, both direct and indirect, of fatalities and their potential links to broader migration control policies and practices.

Counting and recording the dead can also be an initial step to encourage improved systems of identification of those who die. Identifying the dead is a moral imperative that respects and acknowledges those who have died. This process can also provide a some sense of closure for families who may otherwise be left without ever knowing the fate of missing loved ones.

Identification and tracing of the dead and missing

As mentioned above, the challenge remains to count the numbers of dead and also identify those counted. Globally, the majority of those who die during migration remain unidentified. Even in cases in which a body is found identification rates are low. Families may search for years or a lifetime to find conclusive news of their loved one. In the meantime, they may face psychological, practical, financial, and legal problems.

Ultimately Missing Migrants Project would like to see that every unidentified body, for which it is possible to recover, is adequately “managed”, analysed and tracked to ensure proper documentation, traceability and dignity. Common forensic protocols and standards should be agreed upon, and used within and between States. Furthermore, data relating to the dead and missing should be held in searchable and open databases at local, national and international levels to facilitate identification.

For more in-depth analysis and discussion of the numbers of missing and dead migrants around the world, and the challenges involved in identification and tracing, read our two reports on the issue, Fatal Journeys: Tracking Lives Lost during Migration (2014) and Fatal Journeys Volume 2, Identification and Tracing of Dead and Missing Migrants

Content

The data set records

In 2024, the net migration rate in France reached 152,000. In recent years Europe and France have seen more people arrive than depart. The net migration rate is the difference between the number of immigrants (people coming into an area) and the number of emigrants (people leaving an area) throughout the year. France's highest net migration rate was reached in 2018 when it amounted to 201,000. Armed conflicts and economic migration are some of the reasons for immigration in Europe. The refugee crisis Studies have shown that there were 331,000 immigrant arrivals in France in 2022, which has risen since 2014. The migrant crisis, which began in 2015 in Europe, had an impact on the migration entry flows not only in France but in all European countries. The number of illegal border crossings to the EU over the Eastern Mediterranean route reached a record number of 885,386 crossings in 2015. Immigration in France Since the middle of the 19th century, France has attracted immigrants, first from European countries (like Poland, Spain, and Italy), and then from the former French colonies. In 2023, there were approximately 8.9 million people foreign-born in France. Most of them were living in the Ile-de-France region, which contains Paris, and in Provence-Alpes-Côte d’Azur in the Southeastern part of the country. In 2022, the majority of immigrants arriving in France were from Africa and Europe.

Race and ethnicity are fluid self-identities in the United States, particularly among immigrants, who often redefine their racial and ethnic self-identification as they navigate assimilation and cultural integration. This study uses repeated cross-sectional data from the 2000–2021 American Community Surveys to examine the specific racial and ethnic groups among U.S. immigrants that experienced substantial increases in self-identification. Given that fixed immigration cohorts typically decline in size over time due to emigration and mortality, any observed increase within a cohort indicates individuals reclassifying their reported identity. By controlling for the year of entry into the United States, this analysis employs ordinary least squares (OLS) regressions to estimate annual changes in size and percentage across 46 racial and ethnic categories. The analysis reveals significant increases in identification with multiracial whites and single-race or multiracial “Write-In” groups—categories not printed in the survey questionnaire. These findings underscore the fluidity and complexity of ethnic identities and highlight a shift from broad racial classifications to more specific identities that reflect heritage more accurately. These insights contribute to a broader understanding of identity dynamics and a growing diversity and inclusivity within the U.S. racial and ethnic landscape.

This layer shows children by nativity of parents by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of children who are in immigrant families (children who are foreign born or live with at least one parent who is foreign born). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B05009Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Analysis of ‘MISSING MIGRANTS (2014-2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/methoomirza/missing-migrants-20142021 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Missing Migrants Project tracks deaths of migrants, including refugees and asylum-seekers, who have died or gone missing in the process of migration towards an international destination. Please note that these data represent minimum estimates, as many deaths during migration go unrecorded

What is included in Missing Migrants Project data?

Missing Migrants Project counts migrants who have died at the external borders of states, or in the process of migration towards an international destination, regardless of their legal status. The Project records only those migrants who die during their journey to a country different from their country of residence. Missing Migrants Project data include the deaths of migrants who die in transportation accidents, shipwrecks, violent attacks, or due to medical complications during their journeys. It also includes the number of corpses found at border crossings that are categorized as the bodies of migrants, on the basis of belongings and/or the characteristics of the death. For instance, a death of an unidentified person might be included if the decedent is found without any identifying documentation in an area known to be on a migration route. Deaths during migration may also be identified based on the cause of death, especially if is related to trafficking, smuggling, or means of travel such as on top of a train, in the back of a cargo truck, as a stowaway on a plane, in unseaworthy boats, or crossing a border fence. While the location and cause of death can provide strong evidence that an unidentified decedent should be included in Missing Migrants Project data, this should always be evaluated in conjunction with migration history and trends.

What is excluded?

The count excludes deaths that occur in immigration detention facilities or after deportation to a migrant’s homeland, as well as deaths more loosely connected with migrants´ irregular status, such as those resulting from labour exploitation. Migrants who die or go missing after they are established in a new home are also not included in the data, so deaths in refugee camps or housing are excluded. The deaths of internally displaced persons who die within their country of origin are also excluded. There remains a significant gap in knowledge and data on such deaths. Data and knowledge of the risks and vulnerabilities faced by migrants in destination countries, including death, should not be neglected, but rather tracked as a distinct category.

What sources of information are used in the Missing Migrants Project database?

The Missing Migrants Project currently gathers information from diverse sources such as official records – including from coast guards and medical examiners – and other sources such as media reports, NGOs, and surveys and interviews of migrants. In the Mediterranean region, data are relayed from relevant national authorities to IOM field missions, who then share it with the Missing Migrants Project team. Data are also obtained by IOM and other organizations that receive survivors at landing points in Italy and Greece. IOM and UNHCR also regularly coordinate to validate data on missing migrants in the Mediterranean. Data on the United States/Mexico border are compiled based on data from U.S. county medical examiners, coroners, and sheriff’s offices, as well as media reports for deaths occurring on the Mexican side of the border. In Africa, data are obtained from media and NGOs, including the Regional Mixed Migration Secretariat and the International Red Cross/Red Crescent. The quality of the data source(s) for each incident is assessed through the ‘Source quality’ variable, which can be viewed in the data. Across the world, the Missing Migrants Project uses social and traditional media reports to find data, which are then verified by local IOM staff whenever possible. In all cases, new entries are checked against existing records to ensure that no deaths are double-counted. In all regions, Missing Migrants Project data represent a minimum estimate of the number of migrant deaths. To learn more about data sources, visit the thematic page on migrant deaths and disappearances in the Global Migration Data Portal.

Content

What are the variables used in the Missing Migrants Project database?

This section presents the list of variables that constitute the Missing Migrants Project database. While ideally, all incidents recorded would include entries for each of these variables, the challenges described above mean that this is not always possible. The minimum information necessary to register an incident is the date of the incident, the number of dead and/or the number of missing, and the location of death. If the information is unavailable, the cell is left blank or “unknown” is recorded, as indicated in below.

1. Web ID - An automatically generated number used to identify each unique entry in the dataset.

2. Region - Region in which an incident took place. For more about regional classifications used in the dataset, click here.

3. Incident Date - Estimated date of death. In cases where the exact date of death is not known, this variable indicates the date in which the body or bodies were found. In cases where data are drawn from surviving migrants, witnesses or other interviews, this variable is entered as the date of the death as reported by the interviewee. At a minimum, the month and the year of death is recorded. In some cases, official statistics are not disaggregated by the incident, meaning that data is reported as a total number of deaths occurring during a certain time period. In such cases the entry is marked as a “cumulative total,” and the latest date of the range is recorded, with the full dates recorded in the comments.

4. Year - The year in which the incident occurred.

5. Reported month - The month in which the incident occurred.

6. Number dead - The total number of people confirmed dead in one incident, i.e. the number of bodies recovered. If migrants are missing and presumed dead, such as in cases of shipwrecks, leave blank.

7. Number missing - The total number of those who are missing and are thus assumed to be dead. This variable is generally recorded in incidents involving shipwrecks. The number of missing is calculated by subtracting the number of bodies recovered from a shipwreck and the number of survivors from the total number of migrants reported to have been on the boat. This number may be reported by surviving migrants or witnesses. If no missing persons are reported, it is left blank.

8. Total dead & missing - The sum of the ‘number dead’ and ‘number missing’ variables.

9. Number of survivors - The number of migrants that survived the incident, if known. The age, gender, and country of origin of survivors are recorded in the ‘Comments’ variable if known. If unknown, it is left blank.

10. Number of females - Indicates the number of females found dead or missing. If unknown, it is left blank. This gender identification is based on a third-party interpretation of the victim's gender from information available in official documents, autopsy reports, witness testimonies, and/or media reports.

11. Number of males - Indicates the number of males found dead or missing. If unknown, it is left blank. This gender identification is based on a third-party interpretation of the victim's gender from information available in official documents, autopsy reports, witness testimonies, and/or media reports.

12. Number of children - Indicates the number of individuals under the age of 18 found dead or missing. If unknown, it is left blank.

13. Age - The age of the decedent(s). Occasionally, an estimated age range is recorded. If unknown, it is left blank.

14. Country of origin - Country of birth of the decedent. If unknown, the entry will be marked “unknown”.

15. Region of origin - Region of origin of the decedent(s). In some incidents, region of origin may be marked as “Presumed” or “(P)” if migrants travelling through that location are known to hail from a certain region. If unknown, the entry will be marked “unknown”.

16. Cause of death - The determination of conditions resulting in the migrant's death i.e. the circumstances of the event that produced the fatal injury. If unknown, the reason why is included where possible. For example, “Unknown – skeletal remains only”, is used in cases in which only the skeleton of the decedent was found.

17. Location description - Place where the death(s) occurred or where the body or bodies were found. Nearby towns or cities or borders are included where possible. When incidents are reported in an unspecified location, this will be noted.

18. Location coordinates - Place where the death(s) occurred or where the body or bodies were found. In many regions, most notably the Mediterranean, geographic coordinates are estimated as precise locations are not often known. The location description should always be checked against the location coordinates.

19. Migration route - Name of the migrant route on which incident occurred, if known. If unknown, it is left blank.

20. UNSD geographical grouping - Geographical region in which the incident took place, as designated by the United Nations Statistics Division (UNSD) geoscheme. For more about regional classifications used in the dataset, click here.

21. Information source - Name of source of information for each incident. Multiple sources may be listed.

22. Link - Links to original reports of migrant deaths /

This article reviews evidence on the labor market performance of Hispanics in the United States, with a particular focus on the US-born segment of this population. After discussing critical issues that arise in the US data sources commonly used to study Hispanics, we document how Hispanics currently compare with other Americans in terms of education, earnings, and labor supply, and then we discuss long-term trends in these outcomes. Relative to non-Hispanic Whites, US-born Hispanics from most national origin groups possess sizeable deficits in earnings, which in large part reflect corresponding educational deficits. Over time, rates of high school completion by US-born Hispanics have almost converged to those of non-Hispanic Whites, but the large Hispanic deficits in college completion have instead widened. Finally, from the perspective of immigrant generations, Hispanics experience substantial improvements in education and earnings between first-generation immigrants and the second-generation consisting of the US-born children of immigrants. Continued progress beyond the second generation is obscured by measurement issues arising from high rates of Hispanic intermarriage and the fact that later-generation descendants of Hispanic immigrants often do not self-identify as Hispanic when they come from families with mixed ethnic origins.

A detailed analysis of H-1B visa sponsorship trends, featuring data on labor certifications, top sponsoring employers, most common job titles, leading immigration law firms, key industries, and geographic distribution. This dataset provides valuable insights into employment-based immigration patterns, helping professionals, employers, and policymakers make informed decisions.

Over the last several years, the topic of immigration has gained increased attention from politicians, policymakers, and the media. This attention has centered on the prevalence of undocumented immigrants entering and residing within the United States, concern over increasing crime rates involving undocumented immigrants, and the appropriateness of the various policies aimed at controlling the influx of undocumented immigrants into the country. The recent wave of immigration from Latin America has led to a renewed public outcry and overall concerns regarding the relationship between immigration, crime and gang involvement, and the safety of the American public. Thus, the goal of this project was to conduct a multi-methodological study to examine immigrants' involvement in crime, gang membership, and experiences with violent victimization. In addition, this project examined alcohol and drug use among immigrants. This project relied on data collected in Maricopa County, Arizona. Specifically, this project relied on analyses of previously collected quantitative self-report data from a sample of recently booked arrestees, analyses of quantitative self-report data collected from a community sample of immigrants (of different immigration statuses) and US-born citizens, and analysis of qualitative data collected from a community sample of immigrants (of different immigration statuses) and US-born citizens. The results provide a more comprehensive understanding of the relationship between immigration status and crime, gang involvement, and victimization as well as an understanding of immigrants' alcohol and drug use, relative to US-born citizens.

Historical chart and dataset showing U.S. net migration by year from 1960 to 2024.

The United States hosted, by far, the highest number of immigrants in the world in 2020. That year, there were over ** million people born outside of the States residing in the country. Germany and Saudi Arabia followed behind at around ** and ** million, respectively. There are varying reasons for people to emigrate from their country of origin, from poverty and unemployment to war and persecution. American Migration People migrate to the United States for a variety of reasons, from job and educational opportunities to family reunification. Overall, in 2021, most people that became legal residents of the United States did so for family reunification purposes, totaling ******* people that year. An additional ******* people became legal residents through employment opportunities. In terms of naturalized citizenship, ******* people from Mexico became naturalized American citizens in 2021, followed by people from India, the Philippines, Cuba, and China. German Migration Behind the United States, Germany also has a significant migrant population. Migration to Germany increased during the mid-2010's, in light of the Syrian Civil War and refugee crisis, and during the 2020’s, in light of conflict in Afghanistan and Ukraine. Moreover, as German society continues to age, there are less workers in the labor market. In a low-migration scenario, Germany will have **** million skilled workers by 2040, compared to **** million by 2040 in a high-migration scenario. In both scenarios, this is still a decrease from **** skilled workers in 2020.