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.

US Naturalizations Statistics

Trends and statistics on US naturalizations from 1999 to 2017

By Throwback Thursday [source]

About this dataset

The dataset US Naturalizations 1999-2017 provides information on the naturalization process of immigrants in the United States during the period from 1999 to 2017. The dataset includes various features or columns, capturing valuable insights into trends and statistics related to immigrants becoming US citizens.

Firstly, there is a column that specifies the year in which each naturalization case occurred, allowing for analysis and comparison over time. Additionally, there is a column indicating the country of birth of each individual who went through the naturalization process. This information allows for an exploration of patterns and trends based on country of origin.

The dataset also includes columns providing details about gender and age groups. By examining the distribution of naturalized individuals across different genders and age ranges, one can gain insights into demographic patterns and changes in immigration over time.

Furthermore, this dataset features columns related to occupation and educational attainment. These variables contribute to understanding the socio-economic characteristics of immigrants who became US citizens. By analyzing occupational trends or educational levels among naturalized individuals, researchers can gain valuable knowledge regarding immigrant integration within various industries or sectors.

Moreover, this dataset contains data on whether an applicant had previous experience as a lawful permanent resident (LPR) before being granted US citizenship. This variable sheds light on pathways to citizenship among those who have already obtained legal status in the United States.

Finally, there are columns providing information about processing times for naturalized cases as well as any special exemptions granted under certain circumstances. These details offer insights into administrative aspects related to applicants' journeys towards acquiring US citizenship.

In summary, this comprehensive dataset offers a wide range of variables that capture important characteristics related to immigrants becoming US citizens between 1999 and 2017. Researchers can use this data to analyze trends based on year, country of origin, gender/age groups, occupation/education levels,and pathways to citizenship such as previous LPR status or special circumstances exemptions

How to use the dataset

• Understand the columns: Familiarize yourself with the different columns available in this dataset to comprehend the information it offers. The columns included are:

  • Year: The year of naturalization.
  • United States: The number of individuals naturalized within the United States.
  • Continents:
    • Africa: Number of individuals born in African countries who were naturalized.
    • Asia: Number of individuals born in Asian countries who were naturalized.
    • Europe: Number of individuals born in European countries who were naturalized.
    • North America (excluding Caribbean): Number of individuals born in North American countries (excluding Caribbean nations) who were naturalized.
    • Oceania: Number of individuals born in Oceanian countries who were naturalized, including Australia and New Zealand.
    • South America: Number of individuals born in South American countries who were naturalized.

• Overview by year: Analyze the total number of people being granted US citizenship over time by examining the United States column. Use statistical methods like mean, median, or mode to understand trends or identify any outliers or significant changes across specific years.

• Continent-specific analysis:

  a) Identify patterns among continents over time by examining each continent's respective column (Africa, Asia, Europe, etc.). Compare growth rates and determine any regions experiencing higher or lower rates compared to others.

  b) Determine which continent contributes most significantly to overall US immigration by calculating continent-wise percentages based on total immigrants for each year.

• Identify region-specific trends:

  a) Analyze immigration patterns within individual continents by dividing them further into specific regions or countries. For example, within Asia, you can examine trends for East Asia (China, Japan, South Korea), Southeast Asia (Vietnam, Philippines), or South Asia (India, Bangladesh).

  b) Perform comparative analysis between regions/countries to identify variations in immigration rates or any interesting factors influencing these variances. ...

Debates about migration are often in the news. People quote numbers about how many people are entering and leaving different countries. Governments need to plan and manage public resources based on how their own populations are changing.

Informed discussions and effective policymaking rely on good migration data. But how much do we really know about migration, and where do estimates come from?

In this article, I look at how countries and international agencies define different forms of migration, how they estimate the number of people moving in and out of countries, and how accurate these estimates are.

Migrants without legal status make up a small portion of the overall immigrant population. Most high-income countries and some middle-income ones have a solid understanding of how many immigrants live there. Tracking the exact flows of people moving in and out is trickier, but governments can reliably monitor long-term trends to understand the bigger picture.

Who is considered an international migrant? In the United Nations statistics, an international migrant is defined as “a person who moves to a country other than that of his or her usual residence for at least a year, so that the country of destination effectively becomes his or her new country of usual residence”.1

For example, an Argentinian person who spends nine months studying in the United States wouldn’t count as a long-term immigrant in the US. But an Argentinian person who moves to the US for two years would. Even if someone gains citizenship in their new country, they are still considered an immigrant in migration statistics.

The same applies in reverse for emigrants: someone leaving their home country for more than a year is considered a long-term emigrant for the country they’ve left. This does not change if they acquire citizenship in another country. Some national governments may have definitions that differ from the UN recommendations.

What about illegal migration? “Illegal migration” refers to the movement of people outside the legal rules for entering or leaving a country. There isn’t a single agreed-upon definition, but it generally involves people who breach immigration laws. Some refer to this as irregular or unauthorized migration.

There are three types of migrants who don’t have a legal immigration status. First, those who cross borders without the right legal permissions. Second, those who enter a country legally but stay after their visa or permission expires. Third, some migrants have legal permission to stay but work in violation of employment restrictions — for example, students who work more hours than their visa allows.

Tracking illegal migration is difficult. In regions with free movement, like the European Union, it’s particularly challenging. For example, someone could move from Germany to France, live there without registering, and go uncounted in official migration records.2 The rise of remote work has made it easier for people to live in different countries without registering as employees or taxpayers.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study is an analysis of 13 waves of data retrieved from the National Longitudinal Survey of Youth 1997 survey (NLSY97) in order to examine the influence of marriage on immigrant offending trajectories from adolescence to young adulthood. There were three specific research questions considered: Are second generation immigrants entering into marriage at a slower pace than their first generation immigrant peers? What role does marriage play in understanding immigrant offending? Is the relationship between marriage and offending affected by immigrant generation or country/region of birth (i.e., nativity)? Distributed here is the code used for the secondary analysis and the code to compile the datasets.

Content

This report provides statistics for the number of illegal immigrants arrested or apprehended by the border patrol in each division (or sector) of the United States borders with Canada, Mexico, and Caribbean islands; this data is a partial measure of the flow of people illegally entering the United States.

Acknowledgements

Data was compiled and published by the US Border Patrol on the Customs and Border Protection webpage.

We apply standard demographic principles of inflows and outflows to estimate the number of undocumented immigrants in the United States, using the best available data, including some that have only recently become available. Our analysis covers the years 1990 to 2016. We develop an estimate of the number of undocumented immigrants based on parameter values that tend to underestimate undocumented immigrant inflows and overstate outflows; we also show the probability distribution for the number of undocumented immigrants based on simulating our model over parameter value ranges. Our conservative estimate is 16.7 million for 2016, nearly fifty percent higher than the most prominent current estimate of 11.3 million, which is based on survey data and thus different sources and methods. The mean estimate based on our simulation analysis is 22.1 million, essentially double the current widely accepted estimate. Our model predicts a similar trajectory of growth in the number of undocumented immigrants over the years of our analysis, but at a higher level. While our analysis delivers different results, we note that it is based on many assumptions. The most critical of these concern border apprehension rates and voluntary emigration rates of undocumented immigrants in the U.S. These rates are uncertain, especially in the 1990’s and early 2000’s, which is when—both based on our modeling and the very different survey data approach—the number of undocumented immigrants increases most significantly. Our results, while based on a number of assumptions and uncertainties, could help frame debates about policies whose consequences depend on the number of undocumented immigrants in the United States.

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.

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 vary...

United States Immigrants Admitted: Ethiopia data was reported at 14,637.000 Person in 2017. This records an increase from the previous number of 13,232.000 Person for 2016. United States Immigrants Admitted: Ethiopia data is updated yearly, averaging 6,359.500 Person from Sep 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 16,152.000 Person in 2006 and a record low of 2,156.000 Person in 1987. United States Immigrants Admitted: Ethiopia 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.

Users can download data and reports regarding the experience of Latinos in the United States. Users can also interact with maps to view population trends over time. Background The Pew Hispanic Center website contains reports and datasets regarding the experience of Latinos in the United States. Topics include, but are not limited to: homeownership, elections, criminal justice system, and education. User Functionality Users can view and download reports. Users can also interact with maps to obtain demographic information and view population trends from 1980 to 2010. Datasets are also available to download directly into SPSS stat istical software. Surveys administered by the Pew Hispanic Center include: Hispanic Health Care Survey, National Survey of Latinos, Hispanic Religion Survey, Survey of Mexicans Living in the U.S. on Absentee Voting in Mexican Elections, Survey o f Mexican Migrants, and the Survey of Latinos on the News Media. Demographic information is available by race/ethnicity. Data Notes Report information is available on a national and county level and is indicated with the report or dataset. Demographic trends in population growth and dispersion are available for 1980 through 2010. Each report and dataset indicate years in which the data were collected and the geographic unit.

European Countries have seen a surge in immigrants in the last 2 decades. These immigrants are impacting the Social, Political, and Economic dynamics of European countries. According to studies: - 2.3 million immigrants entered the EU from non-EU countries in 2021, an increase of almost 18% compared with 2020. - 1.4 million people previously residing in one EU Member State migrated to another Member State, an increase of almost 17% compared with 2020. - 23.8 million people (5.3%) of the 446.7 million people living in the EU on 1 January 2022 were non-EU citizens. - In 2021, EU Member States granted** citizenship to 827 300** persons having their usual residence on the EU territory, an increase of around 14% compared with 2020.

This Dataset explores the number of immigrants in each European country along with other categories. The data is arranged by European statistics organization Eurostat [https://ec.europa.eu/eurostat]

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?

Social connection is increasingly recognized as a critical determinant of health, yet psychology lacks an understanding of how this relationship operates among immigrants in the United States. Intersecting challenges related to migration-related stressors, discrimination, and exclusionary policies amplify risk and contribute to persistent mental and physical health disparities. To examine the experiences of social connection among first-generation U.S. immigrants and their influence on immigrants’ health and well-being across the lifespan, we reviewed 144 articles published between 2014 and September 2025. Across studies, we observed seven overlapping but inconsistently defined constructs of connection (social networks, social capital, belonging, connectedness, community support, social cohesion, and social ties), highlighting the need for a multidimensional framework. We synthesized study findings into five overarching themes that elucidate how and why social connection matters for immigrants: 1) positive mental and physical health outcomes; 2) access to resources and opportunities; 3) identity affirmation and cultural networks; 4) stress and coping; and 5) immigrant context. To interpret findings, we propose a Social Connection in Immigrants Framework, which integrates constructs and themes and suggests pathways linking social connection to health and well-being. Taken together, our results position social connection as a lever for advancing psychological theory and guiding equity-promoting, context-sensitive interventions that respond to the realities of immigration in the U.S. We identify opportunities for psychology to lead in advancing immigrant well-being by designing multidimensional measures and mechanism-focused studies, embedding connection assessments in clinical practice, and shaping policies that build connection infrastructure and promote belonging within immigrant communities.

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

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

ABSTRACT

Statements about building walls, deportation and denying services to undocumented immigrants made by the US president may induce fear in Latino populations and create barriers to their health care access. To assess the impact of these statements on undocumented Latino immigrants' (UDLI) and Latino legal residents/citizens' (LLRC) perceptions of safety and their presentations for emergency care, we conducted surveys of adult patients at three county emergency departments (EDs) in California from June 2017 to December 2018. Of 1,684 patients approached, 1,337 (79.4%) agreed to participate: 34.3% UDLI, 36.9% LLRC, and 29.8% non-Latino legal residents/citizens (NLRC). The vast majority of UDLI (95%), LLRC (94%), and NLRC (85%) had heard statements about immigrants by President Trump. Most UDLI (89%), LLRC (88%), and NLRC (87%) either thought that these measures were being enacted now or will be enacted in the future. Most UDLI and LLRC reported that these statements made them feel unsafe living in the US, 75% (95% CI 70 to 80%) and 51% (95% CI 47 to 56%), respectively. More UDLI reported that these statements made them afraid to come to the ED (24% 95% CI 20 to 28%) vs LLRC (4.4% [95% CI 3 to 7%]) and NLRC (3.5% [95% CI 2 to 6%]); 55% of UDLI with this fear stated it caused them to delay coming to the ED (median delay 2-3 days). The vast majority of patients in our California EDs have heard statements about immigrants by the US president, which have induced worry and safety concerns in both UDLI and LLRC patients. These statements may also act as a barrier to some UDLI's access of emergency care. Given California's sanctuary state status, these safety concerns and ED access fears may be greater in a nationwide population of Latinos.

Abstract

As of July 2019, it is estimated that over 4,054,000 Venezuelans have left the country and approximately 168,357 have either requested asylum or temporary residency in Brazil, mainly in Roraima state and progressively in the city of Manaus in Amazonas state. Utilising an Area-Based Approach, REACH collected localised information on the situation of Venezuelan asylum seekers and migrants living in host communities and abrigos managed by humanitarian actors in city neighbourhoods across Boa Vista, Pacaraima and Manaus. The aim was to increase the understanding of humanitarian actors of the living conditions, primary needs, vulnerabilities and coping strategies of the asylum seekers and migrants. This study aims to provide a representative overview of the profiles of Venezuelan asylum seekers and migrants living in different geographic locations and shelter settings in Brazil, for the purpose of increasing the understanding of humanitarian actors as to the extent to which the living conditions, needs, and vulnerabilities of Venezuelan households vary between households living in abrigos and those living in host communities, across three cities that are relevant nodes in the Brazilian refugee response: Pacaraima, Boa Vista, and Manaus. The findings indicate that challenges related to accessing services are relatively similar across different locations and shelter settings. The findings indicate that challenges related to accessing services are relatively similar across different locations and shelter settings. Of all services, Venezuelans seem to face the most challenges regarding access to education; findings suggest that a lack of required documents and a limited local capacity are constraining the enrolment of Venezuelan children into local schools. These two factors were also the most likely to pose barriers to accessing social services and healthcare facilities. Difficulties in speaking the local language and long distances to facilities were found to further constrain households' access to services, albeit to a lesser extent.

Geographic coverage

Pacaraima, Boa Vista, and Manaus.

Analysis unit

Household

Universe

Households living in shelters.

Kind of data

Sample survey data [ssd]

Sampling procedure

A master list of households resident within each abrigo was requested from the relevant site manager. The requested dataset required the following fields:

• Tent location (Sector / Tent Number) to facilitate locating selected households;
• Household composition (number of household members, age, sex, focal point y/n);
• Individual and/or Group ID to facilitate secondary data verification to ProGress dataset (if necessary).

The dataset from each abrigo was merged into one master list. Each household within the master dataset was allocated with a consecutive number and households were selected using a random number generator. A total of 1119 households were interviewed.

Mode of data collection

Face-to-face interview

About This Dataset

This dataset provides a comprehensive record of Labor Condition Application (LCA) disclosures for H1B visa petitions filed with the U.S. Department of Labor (DOL) from 2020 to 2024. It has been cleaned and prepared for public analysis to offer valuable insights into employment trends, job categories, salaries, and geographic distribution of H1B workers.

What is H1B?

The H1B visa is a non-immigrant visa that allows U.S. companies to employ foreign workers in specialty occupations requiring theoretical or technical expertise. These roles typically include fields such as IT, engineering, finance, healthcare, and more. The H1B program is critical for addressing skill gaps in the U.S. workforce and supporting economic growth.

What is LCA?

The Labor Condition Application (LCA) is a prerequisite for filing an H1B visa petition. Employers submit the LCA to the DOL to ensure compliance with wage and working condition requirements. The LCA process protects both U.S. workers and foreign employees by enforcing: - Payment of prevailing wages. - Assurance that hiring foreign workers will not adversely affect local labor conditions.

Each LCA disclosure contains information about the employer, job title, job location, wages, and visa classification.

Why Analyze LCA Disclosure Data (2020-2024)?

The dataset spans a crucial period (2020-2024) characterized by: - Pandemic Impact: Changes in employment patterns and visa policies due to COVID-19. - Remote Work Trends: Shifts in work location dynamics for H1B visa holders. - Tech Layoffs and Restructuring: Evolving job roles and industry demands, especially in tech. - Economic Recovery: Insights into how industries and geographic regions rebounded post-pandemic.

Analyzing this data can provide: 1. Employment Trends: Discover trends in job roles, industries, and geographic locations hiring H1B workers. 2. Wage Comparisons: Compare wages across job titles, industries, and states. 3. Policy Insights: Assess the impact of government policies on foreign employment. 4. Geographic Distribution: Identify areas with the highest demand for H1B workers. 5. Industry Insights: Explore the reliance of various industries on foreign talent.

Key Features of This Dataset

• Years Covered: 2020 to 2024.
• Number of Records: Includes all LCAs filed during this period.
• Attributes: Comprehensive fields such as case number, job title, employer name, wage data, work location, and visa classification.
• Source: Data is based on public disclosures from the U.S. Department of Labor.

Who Can Use This Dataset?

• Researchers: To study the economic impact of H1B workers and labor trends.
• Policymakers: To analyze the effects of visa policies on labor markets.
• Job Seekers: To explore prevailing wages and in-demand job roles for H1B petitions.
• Businesses: To understand competitive trends in hiring foreign talent.

This data collection supplies standard monthly labor force information for the week prior to the survey. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Supplemental items pertain to immigrant women. Information provided includes date of birth, country of birth, citizenship status, year entered the United States, number of children born, date of birth of the most recent child, total number of children born in countries outside American jurisdiction, and number of children born in countries outside American jurisdiction currently living in the household. Information on demographic characteristics such as, age, sex, race, marital status, veteran status, household relationship, educational background, and Hispanic origin, is available for each person in the household enumerated. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08265.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.