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.

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.

Public use data set on new legal immigrants to the U.S. that can address scientific and policy questions about migration behavior and the impacts of migration. A survey pilot project, the NIS-P, was carried out in 1996 to inform the fielding and design of the full NIS. Baseline interviews were ultimately conducted with 1,127 adult immigrants. Sample members were interviewed at baseline, 6 months, and 12 months, with half of the sample also interviewed at three months. The first full cohort, NIS-2003, is based on a nationally representative sample of the electronic administrative records compiled for new immigrants by the US government. NIS-2003 sampled immigrants in the period May-November 2003. The geographic sampling design takes advantage of the natural clustering of immigrants. It includes all top 85 Metropolitan Statistical Areas (MSAs) and all top 38 counties, plus a random sample of other MSAs and counties. Interviews were conducted in respondents'' preferred languages. The baseline was multi-modal: 60% of adult interviews were administered by telephone; 40% were in-person. The baseline round was in the field from June 2003 to June 2004, and includes in the Adult Sample 8,573 respondents, 4,336 spouses, and 1,072 children aged 8-12. A follow-up was planned for 2007. Several modules of the NIS were designed to replicate sections of the continuing surveys of the US population that provide a natural comparison group. Questionnaire topics include Health (self-reports of conditions, symptoms, functional status, smoking and drinking history) and use/source/costs of health care services, depression, pain; background; (2) Background: Childhood history and living conditions, education, migration history, marital history, military history, fertility history, language skills, employment history in the US and foreign countries, social networks, religion; Family: Rosters of all children; for each, demographic attributes, education, current work status, migration, marital status and children; for some, summary indicators of childhood and current health, language ability; Economic: Sources and amounts of income, including wages, pensions, and government subsidies; type, value of assets and debts, financial assistance given/received to/from respondent from/to relatives, friends, employer, type of housing and ownership of consumable durables. * Dates of Study: 2003-2007 * Study Features: Longitudinal * Sample Size: 13,981

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows children by nativity of parents by age group. This is shown by tract, county, and state boundaries. 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. Vintage: 2010-2014ACS Table(s): B05009 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

Immigration to this country has increased significantly in recent years. While Mexican immigrants are the largest population of immigrants in the United States (39 percent), the rest of the population is widely varied, with no one nation accounting for more than 3 percent of all immigrants. Despite the significant benefits quality Early Childhood Education (ECE) programs offer to immigrant children, their rates of enrollment are significantly lower than for comparable children of United States-born parents. In order to better address the needs of these new American families, providers and state policymakers need more in-depth knowledge about the perceptions of these families and the factors that influence their choice of care. This study is an exploratory study in two cities which reflect the diversity of experience with immigration across the country: Denver, Colorado and surrounding areas, where the focus is on Mexican immigrants, and Portland, Maine and surrounding areas, where the focus is on three of the many refugee populations which have newly settled here. The contrasts, not only in the immigrant populations themselves, but also in the political and historical contexts of the communities in which they live, offer an opportunity to enrich the field of research on child care choices for this vulnerable population of children and families.Additional details about this study can be found on the New Americans Web site.

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

Children of Immigrants Longitudinal Study (CILS) was designed to study the adaptation process of the immigrant second generation which is defined broadly as United States-born children with at least one foreign-born parent or children born abroad but brought at an early age to the United States. The original survey was conducted with large samples of second-generation immigrant children attending the 8th and 9th grades in public and private schools in the metropolitan areas of Miami/Ft. Lauderdale in Florida and San Diego, California. Conducted in 1992, the first survey had the purpose of ascertaining baseline information on immigrant families, children's demographic characteristics, language use, self-identities, and academic attainment. The total sample size was 5,262. Respondents came from 77 different nationalities, although the sample reflects the most sizable immigrant nationalities in each area. Three years later, corresponding to the time in which respondents were about to graduate from high school, the first follow-up survey was conducted. Its purpose was to examine the evolution of key adaptation outcomes including language knowledge and preference, ethnic identity, self-esteem, and academic attainment over the adolescent years. The survey also sought to establish the proportion of second-generation youths who dropped out of school before graduation. This follow-up survey retrieved 4,288 respondents or 81.5 percent of the original sample. Together with this follow-up survey, a parental survey was conducted. The purpose of this interview was to establish directly characteristics of immigrant parents and families and their outlooks for the future including aspirations and plans for the children. Since many immigrant parents did not understand English, this questionnaire was translated and administered in six different foreign languages. In total, 2,442 parents or 46 percent of the original student sample were interviewed. During 2001-2003, or a decade after the original survey, a final follow-up was conducted. The sample now averaged 24 years of age and, hence, patterns of adaptation in early adulthood could be readily assessed. The original and follow-up surveys were conducted mostly in schools attended by respondents, greatly facilitating access to them. Most respondents had already left school by the time of the second follow-up so they had to be contacted individually in their place of work or residence. Respondents were located not only in the San Diego and Miami areas, but also in more than 30 different states, with some surveys returned from military bases overseas. Mailed questionnaires were the principal source of completed data in this third survey. In total, CILS-III retrieved complete or partial information on 3,613 respondents representing 68.9 percent of the original sample and 84.3 percent of the first follow-up.Relevant adaptation outcomes measured in this survey include educational attainment, employment and occupational status, income, civil status and ethnicity of spouses/partners, political attitudes and participation, ethnic and racial identities, delinquency and incarceration, attitudes and levels of identification with American society, and plans for the future.

CILS is a longitudinal study designed to study the adaptation process of the immigrant second generation, which is defined broadly as U.S.-born children with at least one foreign-born parent or child born abroad but brought at an early age to the United States. Immigrant families, children's own demographic characteristics, language use, self-identities, and academic attainment were key objectives. Questions about religion were asked only once, in Survey Wave 3 (variables V439 through V440).

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.

Many U.S.-born descendants of Mexican immigrants do not identify as Mexican or Hispanic in response to the Hispanic origin question asked in the Census and other government surveys. Analyzing microdata from the 2000 U.S. Census and the 2001-2019 American Community Surveys, we show that the age at arrival of Mexican immigrants exerts an important influence on ethnic identification not only for these immigrants themselves but also for their U.S.-born children. Among Mexican immigrants who arrived as children, the rate of “ethnic attrition”—i.e., not self-identifying as Mexican or Hispanic—is higher for those who migrated at a younger age. Moreover, the children of these immigrants exhibit a similar pattern: greater ethnic attrition among children whose parents moved to the United States at a younger age. We unpack the relative importance of several key mechanisms—parental English proficiency, parental education, family structure, intermarriage, and geographic location—through which the age at arrival of immigrant parents influences the ethnic identification of their children. Intermarriage turns out to be the primary mechanism: Mexican immigrants who arrived at a very young age are more likely to marry non-Hispanics, and the rate of ethnic attrition is dramatically higher among children with mixed ethnic backgrounds. Prior research demonstrates that arriving at an early age hastens and furthers the integration of immigrants. We show here that this pattern also holds for ethnic identification and that the resulting differences in ethnic attrition among first-generation immigrants are transmitted to their second-generation children.

In 2015, the Administration for Children and Families funded a new study - the Migrant and Seasonal Head Start Study (MSHS Study) - to focus on MSHS programs and the families they serve. The MSHS Study was designed to closely match the characteristics of the whole population of MSHS programs, centers, families, and children across the United States (a "nationally representative study"). Since the last nationally representative study of MSHS was conducted almost 20 years ago, this study provided an update on MSHS programs and centers, as well as the migrant and seasonal farmworker families they serve. The MSHS Study included data from programs and centers (collected from surveys of program and center directors), classrooms (collected through classroom observations and from surveys of teachers and assistant teachers), families (collected from interviews with parents), and children (collected from direct assessments, assessor ratings, and parent and teacher ratings of children). Although the study gathered a range of program, practice, and family information, a central theme of the data collection focused on language practice and the language skills and abilities of the children served. The study examined the following research questions: What are the characteristics of MSHS programs, centers, staff, families, and children? What services does MSHS provide, and what are the instructional practices and general classroom quality of MSHS classrooms? What are the associations between MSHS characteristics and child/family well-being? The MSHS Study methodology, sample, and measures were all developed (or selected) in collaboration with MSHS stakeholders and experts in MSHS programs and early childhood research. The study was conducted by Abt Associates and its partners - the Catholic University of America and Westat - under contract to the Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services. This collection is organized into 18 data parts: 4 files with data from MSHS staff surveys, including surveys with program directors (DS2), center directors (DS4), teachers (DS7), and assistant teachers (DS8). All staff surveys collected information on the respondent's background and experience and then focused on questions relevant to each respondent. For example, the Program Director Survey collected information on issues such as enrollment, program policies, and approaches to hiring, communication, and supervision. The Center Director Survey focused on characteristics of the center, such as staffing, enrollment, family engagement, and instructional practices. The Teacher and Assistant Teacher Surveys gathered information on topics at the classroom level, such as classroom composition and language(s) of instruction, and also included the 12-item version of the Center for Epidemiologic Studies Depression Scale. 1 file with data from classroom observations (DS6), including items from the Classroom Assessment Scoring System (CLASS) Pre-K, Early Language and Literacy Classroom Observation-Dual Language Learners (ELLCO-DLL), and the MSHS Cultural Items and Language Use (CILU) Checklist. 5 direct child assessments, including height and weight measurements (DS10), the Leiter-3 Examiner Rating Scale (DS11), the Preschool Language Scales Fifth Edition (PLS-5) - English (DS12), the PLS-5 - Bilingual (DS13), and the Woodcock Mu?oz Language Survey (DS14). 1 file with data from the Ages and Stages Questionnaire (DS15) completed by teachers for infants and young children to assess children's nonverbal and verbal communication skills. 1 file for Teacher Report of Child (DS16), including data from children's language dominance and proficiency, questions about delays and disabilities, the MacArthur-Bates Communicative Development Inventory (CDI-English)/Inventario del Desarrollo de Habilidades Comunicativas (IDHC-Spanish), Early Childhood Longitudinal Study (ECLS)-Birth Cohort questions on counting in English and Spanish, and ECLS-Kindergarten Approaches to Learning. 1 file for Parent Report of Child (DS17), including data from the MacArthur-Bates CDI-English/IDHC-Spanish, Brief Infant-Toddler Social and Emotional Assessment, and the ECLS-B Socioemotional Scale. 1 file with data from the Parent Interview (DS18) that focused on characteristics of the household and focal child.

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.

To supplement the Migrant and Seasonal Head Start Study (MSHS) main data files (Migrant and Seasonal Head Start Study, United States, 2017-2018 (ICPSR 37348)) a new data file is available to users on the Virtual Data Enclave. This file contains information on the location of centers (region, state, zip code, and operational period) in the MSHS Study. The MSHS Study was conducted by Abt Associates and its partners--The Catholic University of America and Westat--under contract to the Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.

This study was designed to systematically examine the similarities and differences of experience among four groups of adolescents: Mexicans (born of Mexican parents and residing in Mexico), Mexican immigrants (born of Mexican parents in Mexico and now residing in the United States), second-generation Mexican Americans (born and raised in the United States of Mexican immigrant parents), and White Americans (born and raised in the United States of white, non-Hispanic, U.S.-born parents). Specifically, the study explores how family orientation (i.e., familism and family conflict) and achievement orientation differ among these groups. The participants were 189 adolescents (96 girls and 93 boys) between the ages of 13 and 18 who were attending public middle and high schools. The participants were equally divided among the four groups. Data for the Mexican sample were gathered in 1991 and 1992 in Guanajuato, one of three Mexican states from which a majority of emigrants to the United States originate. Data for the other three groups were gathered in 1992 from public schools in California. The data collection methods consisted of classroom observations, ethnographic interviews, and tests which were conducted in either English, Spanish, or both according to the students' preference and proficiency. The interviews covered demographic, life-history, and migration-related information as well as issues related to their experiences at school and with their families and peers. The interview included a number of psychological tests: Familism Scale (Sabogal et al.,1987), Family Conflict Scale (Beavers, Hampson, and Hulgus, 1985), Sentence Completion Test (De Vos, 1973), Problem Situation Test (De Vos, 1973), and Thematic Apperception Tests (Murray, 1943). The Murray Research Archive holds the completed interview booklets as well as audiotapes of interviews. A follow-up of the study is possible with the collaboration of the contributor. Audio Data Availability Note: This study contains audio data that have been digitized. There are 284 audio files available.

The New Immigrant Survey (NIS) was a nationally representative, longitudinal study of new legal immigrants to the United States and their children. The sampling frame was based on the electronic administrative records compiled for new legal permanent residents (LPRs) by the U.S. government (via, formerly, the U.S. Immigration and Naturalization Service (INS) and now its successor agencies, the U.S. Citizenship and Immigration Services (USCIS) and the Office of Immigration Statistics (OIS)). The sample was drawn from new legal immigrants during May through November of 2003. The geographic sampling design took advantage of the natural clustering of immigrants. It included all top 85 Metropolitan Statistical Areas (MSAs) and all top 38 counties, plus a random sample of MSAs and counties. The baseline survey (ICPSR 38031) was conducted from June 2003 to June 2004 and yielded data on: 8,573 Adult Sample respondents, 810 sponsor-parents of the Sampled Child, 4,915 spouses, and 1,072 children aged 8-12. This study contains the follow-up interview, conducted from June 2007 to October 2009, and yielded data on: 3,902 Adult Sample respondents, 351 sponsor-parents of the Sampled Child, 1,771 spouses, and 41 now-adult main child. Interviews were conducted in the respondents' language of choice. Round 2 instruments were designed to track changes from the baseline and also included new questions. As with the Round 1 questionnaire, questions that were used in social-demographic-migration surveys around the world as well as the major U.S. longitudinal surveys were reviewed in order to achieve comparability. The NIS content includes the following information: demographic, health and insurance, migration history, living conditions, transfers, employment history, income, assets, social networks, religion, housing environment, and child assessment tests.

The American public has overwhelmingly supported the Development, Relief, and Education for Alien Minors (DREAM) Act since 2001. The support is widespread and cuts across race, ethnic, and party lines. Given the United States’ anti-immigrant/immigration sentiment in the wake of the September 11, 2001 terrorist attacks, support for the DREAM Act is perplexing. To that end, political scientists, sociologists, and education scholars among others, have pointed to the exceptional framing of the DREAM Act as the primary predictor of support. However, a significant portion of non-Hispanic white Americans who support the DREAM Act also support restrictive and often punitive immigration policies. What influences most white Americans to support DREAM Act legislation? And what leads a subset of these same individuals to simultaneously support restrictive immigration policies that hurt DREAMers and their families? I argue that predispositions explain these two contradictory policy preferences. Data from the 2012 American National Election Studies (ANES) and the 2018 Cooperative Congressional Election Studies (CCES) demonstrates that white Americans use racial resentment and egalitarianism as justifications to support both policies, however, the effects are conditioned on partisanship.

BackgroundPostpartum contraception is essential to sexual and reproductive health (SRH) care because it encourages healthy spacing between births, helps women avoid unwanted pregnancies, and lessens the risks of health problems for mothers and babies. Sub-Saharan African immigrant and refugee populations are rapidly increasing in the United States, and they come from a wide range of cultural, linguistic, religious, and social origins, which may pose challenges in timely access to culturally acceptable SRH care, for preventing mistimed or unwanted childbearing. The objective of this scoping review is to assess the extent of the available literature on postpartum contraception among sub-Saharan African immigrant and refugee women living in the United States.MethodsWe developed preliminary search terms with the help of an expert librarian, consisting of keywords including birth intervals, birth spacing, contraception, postpartum contraception or family planning, and USA or America, and sub-Saharan African immigrants, or emigrants. The study will include the following electronic databases: PubMed/MEDLINE, PsycINFO, CINAHL, EMBASE, and the Global Health Database. The sources will include studies on postpartum care and contraceptive access and utilization among sub-Saharan African immigrants living in the US. Citations, abstracts, and full texts will be independently screened by two reviewers. We will use narrative synthesis to analyze the data using quantitative and qualitative methods. Factors associated with postpartum contraception will be organized using the domains and constructs of the PEN-3 Model as a guiding framework.ConclusionThis scoping review will map the research on postpartum contraception among sub-Saharan African immigrant and refugee women living in the US. We expect to identify knowledge gaps, and barriers and facilitators of postpartum contraception in this population. Based on the findings of the review, recommendations will be made for advocacy and program and policy development toward optimizing interpregnancy intervals in sub-Saharan African immigrants living in the US.Trial registrationReview registration Open Science Framework: https://osf.io/s385j.

Project Overview The “Montagnards” (“mountain people” in the French language) represent a diverse array of cultures originating in the highlands of Vietnam. Largely isolated farmers or hunter-gather communities, the Montagnards were recruited by, and fought with, the American Special Forces throughout the Vietnam War. When the war ended with the fall of Saigon in 1975, the Montagnards were especially persecuted in the new regime. Montagnard individuals began arriving in the US as refugees in the mid-1980’s and family reunification efforts have continually brought more refugees here to the present day. There are over 12,000 Montagnards living in Greensboro, North Carolina, representing several cultures and distinct languages, with a majority of them in Guilford County. This makes the Piedmont the largest Montagnard community outside of southeast Asia. This study aims to document access to mental health care across four distinct generations of Montagnard community members, in an effort to identify potential mental health concerns that may be unique to each generation. When considering the overall health of Montagnards, both physical and mental, it is important to consider former experiences in Vietnam like starvation, trauma, and chemical exposure, and also the experience of being a refugee and an immigrant living in the United States. The immigrant health paradox is the idea that oftentimes, even if a migrant arrives to the United States relatively healthy, their health tends to get poorer the longer they remain in the U.S. Prior studies looking at the immigration experience of Vietnamese found them to be disadvantaged in several indicators of mental health, and refugees in the U.S. have been observed to have an elevated burden of chronic disease. The first generation Montagnard elders (born by 1970), spent the most time in Vietnam and experienced trauma and persecution firsthand. Many are preoccupied by concerns of family members that got left behind in Vietnam. The second generation of Montagnards (born 1971-1985) directly experienced the trauma of Montagnard life post-1975, but unlike the first generation, they were young children when these events unfolded. The third generation (born 1985-1995) is, in many ways, in between. They are the link between the young and the old, and both Montagnard and American cultures. The fourth generation (born after 1995), or the youngest of the Montagnards, have a radically different experience and perspective from those of the older generations. Many members of this generation speak fluent English and were born and educated in the United States. Montagnard researchers have concerns about suicide in this population. The youngest Montagnards are faced with the challenge of reconciling their Montagnard and American identities. Health access is a known issue in the Montagnard community, and it is not hard to imagine how sociocultural, political, and economic variables can help to further compound and explain negative health outcomes. Five aspects of health access are studied in this project via a framework analysis of five dimensions of health services provision: approachability, acceptability, availability/accommodation, affordability, and appropriateness. Data Collection Overview This data are from the results of a qualitative research study about access to mental health care in the Montagnard population in North Carolina. Semi-structured interviews were conducted with Montagnard individuals, and interviews were then transcribed and analyzed using Dedoose software. The study included 26 participants, with 2 participants in the first generation, 3 in the second generation, 12 in the third generation, and 9 in the fourth generation. The participants had to be at least 18 years old to participate in the study. For participants born in the US, age was determined by official US-issued government documents, such as a driver’s license or government ID. For individuals born in Vietnam, particularly in the oldest generation, birth dates given on governmental identification (i.e., immigration documents or driver’s licenses) are often incorrect since their birth dates were never known or documented officially. In these cases, the placement of an individual in a particular generation depended on their memories of the pivotal year (1975) and what they were doing at that time (i.e., were they a young child, or a soldier, etc.). All participants had to speak a language that can be translated by one of the available translators. There are many distinct languages within the Montagnard communities and we were only able to interview those individuals with whom we can be confident of the verbal and later transcribed translation. Due to the COVID-19 pandemic, we shifted data collection to a virtual format. All interviews beginning with the third participant were conducted virtually. Data collection occurred from March 2020 through August 2020. The virtual data collection consisted of two...

This map shows where native children where one or both parents are immigrants. Data is from the American Community Survey (ACS). These are 5-year estimates shown by tract, county, and state centroids. Arcade was used to calculate the percentage of children in immigrant families (native children who live with at least one parent who is foreign born).This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.