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 data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards: Immigrants, Refugees, Muslims, Hispanics, Venezuelans News Media Consumption Trust in News Media and Societal Institutions Frequency and Valence of Intergroup Contact Realistic and Symbolic Intergroup Threat Right-wing Authoritarianism Social Dominance Orientation Political Efficacy Personality Characteristics Perceived COVID-threat, and Socio-demographic Characteristics For the adult population aged 25 to 65 in seven European countries: Austria Belgium Germany Hungary Italy Spain Sweden And for ages ranged from 18 to 65 for: United States of America Colombia The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

Immigrants Admitted: Philippines data was reported at 53,287.000 Person in 2016. This records a decrease from the previous number of 56,478.000 Person for 2015. Immigrants Admitted: Philippines data is updated yearly, averaging 54,446.000 Person from Sep 1986 (Median) to 2016, with 31 observations. The data reached an all-time high of 74,606.000 Person in 2006 and a record low of 30,943.000 Person in 1999. Immigrants Admitted: Philippines data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s USA – Table US.G086: Immigration.

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

Census: Number of Migrants: Punjab data was reported at 13,735,616.000 Person in 03-01-2011. This records an increase from the previous number of 9,189,438.000 Person for 03-01-2001. Census: Number of Migrants: Punjab data is updated decadal, averaging 9,189,438.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 13,735,616.000 Person in 03-01-2011 and a record low of 6,960,431.000 Person in 03-01-1991. Census: Number of Migrants: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAG001: Census of India: Migration: Number of Migrants: by States.

At a time when many states are increasing restrictions on immigration, others are using formal agreements on international economic migration to open their borders. The use of international agreements on migration presents a puzzle, as most states can open their borders to migrants unilaterally. I argue that, when states cannot generate large enough flows of migrants or the right type of migrants to fill open positions in the labor market, they turn to the sending state to help them. States that need migrants can negotiate a bilateral labor agreement with a sending state, which then acts as a recruiter, helping to channel labor to the receiving state. This article details the conditions under which immigrant-receiving countries use these treaties and tests the implications of the argument on a new dataset on migration treaties.

Canada’s appeal as an immigration destination has been increasing over the past two decades, with a total of 464,265 people immigrating to the country in 2024. This figure is an increase from 2000-2001, when approximately 252,527 immigrants came to Canada. Immigration to the Great White North Between July 1, 2022 and June 30, 2023, there were an estimated 199,297 immigrants to Ontario, making it the most popular immigration destination out of any province. While the number of immigrants has been increasing over the years, in 2024 over half of surveyed Canadians believed that there were too many immigrants in the country. However, in 2017, the Canadian government announced its aim to significantly increase the number of permanent residents to Canada in order to combat an aging workforce and the decline of working-age adults. Profiles of immigrants to Canada The gender of immigrants to Canada in 2023 was just about an even split, with 234,279 male immigrants and 234,538 female immigrants. In addition, most foreign-born individuals in Canada came from India, followed by China and the Philippines. The United States was the fifth most common origin country for foreign-born residents in Canada.

This paper demonstrates the effect of country level corruption on illicit behavior of individuals in a foreign country. The empirical research investigates the probability of individuals being apprehended overseas due to the influence of corrupt environment in their home countries. Using cross-sectional data for empirical analysis from 104 different countries over the period of 2009– 2011, the authors focused on finding how people from various countries act and behave differently while stationing outside of their home countries. Their findings reveal some evidences that individuals coming to the United States from corruption-ridden countries are more likely to be apprehended than individuals from less corrupt countries are.

Census: Number of Migrants: All India data was reported at 314,541,350.000 Person in 2001. This records an increase from the previous number of 232,112,973.000 Person for 1991. Census: Number of Migrants: All India data is updated yearly, averaging 273,327,161.500 Person from Mar 1991 (Median) to 2001, with 2 observations. The data reached an all-time high of 314,541,350.000 Person in 2001 and a record low of 232,112,973.000 Person in 1991. Census: Number of Migrants: All India data remains active status in CEIC and is reported by Census of India. The data is categorized under Global Database’s India – Table IN.GAG001: Census of India: Migration: Number of Migrants: by States.

Projected Deaths by Single Year of Age, Sex, Race, and Hispanic Origin for the United States: 2016-2060 // Source: U.S. Census Bureau, Population Division // There are four projection scenarios: 1. Main series, 2. High Immigration series, 3. Low Immigration series, and 4. Zero Immigration series. // Note: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. // For detailed information about the methods used to create the population projections, see https://www2.census.gov/programs-surveys/popproj/technical-documentation/methodology/methodstatement17.pdf. // Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. The Population Estimates and Projections Program provides additional information on its website: https://www.census.gov/programs-surveys/popproj.html.

Name searchable index to Series S5631 Card Register of Immigrants and Missing Immigrants. This series is a card register of immigrants and missing immigrants dealt with in various correspondence although most cards contain very little information. The cards mainly register the administrative file movement/s of the Immigration Department's correspondence regarding a particular immigrant, but may include the name of the immigrant/missing immigrant, a file number, i.e. 269/24, name of ship, date of arrival, movements of File (dates and file numbers), the name of (correspondence) writer, subject and action. Some cards contain annotations such as "file returned to (other state)" or details concerning an immigrant, for example, age at date of departure etc. There are stamp marks on many of the cards, for example, "Domestic", Decontrolled", "Final", "Decentralised", "Salvation Army", etc

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards: Immigrants, Refugees, Muslims, Hispanics, Venezuelans News Media Consumption Trust in News Media and Societal Institutions Frequency and Valence of Intergroup Contact Realistic and Symbolic Intergroup Threat Right-wing Authoritarianism Social Dominance Orientation Political Efficacy Personality Characteristics Perceived COVID-threat, and Socio-demographic Characteristics For the adult population aged 25 to 65 in seven European countries: Austria Belgium Germany Hungary Italy Spain Sweden And for ages ranged from 18 to 65 for: United States of America Colombia The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections. Probability Web-based interview

Global matrices of bilateral migrant stocks spanning the period 1960-2000, disaggregated by gender and based primarily on the foreign-born concept are presented. Over one thousand census and population register records are combined to construct decennial matrices corresponding to the last five completed census rounds.For the first time, a comprehensive picture of bilateral global migration over the last half of the twentieth century emerges. The data reveal that the global migrant stock increased from 92 to 165 million between 1960 and 2000. South-North migration is the fastest growing component of international migration in both absolute and relative terms. The United States remains the most important migrant destination in the world, home to one fifth of the world™s migrants and the top destination for migrants from no less than sixty sending countries. Migration to Western Europe remains largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East, North Africa and South and South-East Asia. Finally, although the global migrant stock is still predominantly male, the proportion of women increased noticeably between 1960 and 2000.

Most countries still have a significant gender gap in labor force participation, and this gap is especially large for immigrants. Despite this gap, Germany introduced various forms of home care allowances in the last decade. Parallel to the extension of early child care and the inclu-sion of a legal claim for it, from 2013 to 2015, a nationwide home care allowance existed for parents who did not use public child care for children aged one or two years. After 2015, home care allowances continued to exist in several German federal states. Some politicians strongly criticized this transfer for allegedly decreasing work incentives, particularly for moth-ers with lower labor market integration, such as immigrant mothers. Using federal state differ-entiated data obtained from the German Socio-Economic Panel (doi: 10.5684/soep.v34), we investigate the impacts of a home care allowance on the labor market participation of mothers. For both native-born and especially immigrant mothers, the effects are significantly negative. We conclude that a home care allowance has negative effects on the labor force participation of mothers of young chil-dren, irrespective of the legal claim for and the extension of public child care. Non-probability Sample Interview

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

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 report reviews recent trends in international migration, describing the size of current foreign-born populations across countries and analysing factors associated to the size and nature of these populations, reviews a set of important differences and similarities across educational systems and gives a brief description of population sizes across countries. It also provides an overview of the evidence emerging from PISA 2009 on the performance and socio-economic background of children of immigrants. Who are the children of immigrants? What do they know and what can they do? How do they differ from other students? Do they approach school and learning in a different way? It examines more closely the issue of assessment language proficiency among immigrant students and its possible impact on cognitive outcomes in PISA. It explores the effect of age at arrival on the performance of immigrant students in the PISA tests of literacy.Selective migration policies of certain countries and the attractiveness of these countries generally to highly educated migrants is also explored. It also discusses the future educational and professional career of the children of immigrant related to their performance in PISA. Does the skill and knowledge disadvantage at age 15 translate into a disadvantage in later educational outcomes? For example, are those children of immigrants less likely to access a post-secondary educational institution?

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.

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C0) and chemical properties. The most predictive variables in the resulting model were C0, molecular weight, log Kow, and food type (R2=0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C0 based on the functional role of chemical in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R2=0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations. While these HT methods have large uncertainties (and thus may not be appropriate for assessments of single chemicals), they can provide critical refinement to aggregate exposure predictions used in risk-based chemical priority–setting. This dataset is associated with the following publication: Biryol, D., C. Nicolas, J. Wambaugh, K. Phillips, and K. Isaacs. High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 108: 185-194, (2017).

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

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

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

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

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