Graph and download economic data for Employment Level - Foreign Born (LNU02073395) from Jan 2007 to Jun 2025 about foreign, household survey, employment, and USA.

Graph and download economic data for Civilian Labor Force Level - Foreign Born (LNU01073395) from Jan 2007 to Jun 2025 about foreign, civilian, 16 years +, labor force, labor, household survey, and USA.

This dataset is about book subjects. It has 1 row and is filtered where the books is Harvest of confusion : migrant workers in U.S. agriculture. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

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

Large inflows of less educated immigrants may reduce wages paid to comparably-educated, native-born workers. However, if less educated foreign- and native-born workers specialize in different production tasks, because of different abilities, immigration will cause natives to reallocate their task supply, thereby reducing downward wage pressure. Using occupational task-intensity data from the O*NET dataset and individual US census data, we demonstrate that foreign-born workers specialize in occupations intensive in manual-physical labor skills while natives pursue jobs more intensive in communication-language tasks. This mechanism can explain why economic analyses find only modest wage consequences of immigration for less educated native-born workers. (JEL J24, J31, J61)

Graph and download economic data for Unemployment Rate - Foreign Born (LNU04073395) from Jan 2007 to Jun 2025 about foreign, 16 years +, household survey, unemployment, rate, and USA.

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 dataset is about books. It has 1 row and is filtered where the book is Militants and migrants : rural Sicilians become American workers. It features 7 columns including author, publication date, language, and book publisher.

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

This dataset is about countries per year in the United States. It has 1 row and is filtered where the date is 2023. It features 4 columns: country, self-employed workers, and net migration.

ObjectivesThe SF-12 version 2 is a survey instrument for collecting data on subjective health. The US-based scoring method is the recommended standard for measuring subjective health with data collected with this instrument. The inadequacy of the US-based scoring method of the SF-12 version 2 instrument for non-US populations is widely documented. However, few studies systematically assessed relative performance of alternative scoring methods against the US-based method, our main objective in this paper. Through this investigation, we also intend to shed light on Filipina migrant workers’ subjective health in Hong Kong, our case study.MethodsThis study investigates the feasibility of eight such scoring methods—six latent-variable models, the raw score index, and the US-based method—for analyzing an SF-12 version 2 instrument via a range of bootstrapped samples of varying sizes and an empirical study of the original 2017 Hong Kong Domestic Workers survey data with a set of covariates associated with Filipina migrant domestic workers’ subjective mental and physical health in Hong Kong.FindingsOur analyses favor the latent-variable factor model with the normal distribution and the identity link for analyzing the SF-12 version 2 type of data. Our empirical study of the survey data provides evidence for the beneficial effects of education, social support, and positive working conditions on migrant domestic workers’ subjective physical health and especially subjective mental health, with these two types of health analyzed jointly on the same measurement scale.ConclusionFor studying non-US populations with the SF-12 version 2 instrument, we recommend using the latent confirmatory factor analysis model that assumes a normal distribution and an identity link function for analyzing the MCS and PCS dimensions simultaneously.

Following Grossman and Rossi-Hansberg (2008) we present a model in which tasks of varying complexity are matched to workers of varying skill in order to develop and test predictions regarding the effects of immigration and offshoring on US native-born workers. We find that immigrant and native-born workers do not compete much due to the fact that they tend to perform tasks at opposite ends of the task complexity spectrum, with offshore workers performing the tasks in the middle. An effect of offshoring and a positive effect of immigration on native-born employment suggest that immigration and offshoring improve industry efficiency.

The labor markets in the US and Mexico are closely linked through migrant workers and remittances and the changes in remittance flow may alter labor allocations in the origin households. In this paper, we investigate how the prevalence of the Covid-19 epidemic in the US affected the local labor market in Mexico. We construct a Mexican municipality-level measure of the exposure to Covid-19 in the US using data on Covid-19 prevalence across US states and data on migrants' destinations across the US states. We find a positive effect of Covid-19 exposure in the US on the hours worked among workers in Mexico yet no significant effects were found for the local wages. We also find that the effect varies across subgroups which indicates that the responses in hours worked depend on the household dynamics, the nature of the occupation-specific tasks, and the migration intensity. Finally, we document the potential mechanism behind the effect on the hours worked, which is through the changes in remittances sent to the origin municipalities in Mexico.

H-1B visa is a visa in the United States under the Immigration and Nationality Act, section 101(a)(15)(H) that allows U.S. employers to temporarily employ foreign workers in specialty occupations. A specialty occupation requires the application of specialized knowledge and a bachelor’s degree or the equivalent of work experience. (source Wikipedia)

Data

The H-1B Dataset selected for this project contains data from employer’s Labor Condition Application and the case certification determinations processed by the Office of Foreign Labor Certification (OFLC). The Labor Condition Application (LCA) is a document that a perspective H-1B employer files with U.S. Department of Labor Employment and Training Administration (DOLETA) when it seeks to employ non-immigrant workers at a specific job occupation in an area of intended employment for not more than three years. The datasets are from the Department of Labor's website.

Context:

A permanent labor certification issued by the Department of Labor (DOL) allows an employer to hire a foreign worker to work permanently in the United States. In most instances, before the U.S. employer can submit an immigration petition to the Department of Homeland Security's U.S. Citizenship and Immigration Services (USCIS), the employer must obtain a certified labor certification application from the DOL's Employment and Training Administration (ETA). The DOL must certify to the USCIS that there are not sufficient U.S. workers able, willing, qualified and available to accept the job opportunity in the area of intended employment and that employment of the foreign worker will not adversely affect the wages and working conditions of similarly employed U.S. workers.

Content:

Data covers 2012-2017 and includes information on employer, position, wage offered, job posting history, employee education and past visa history, associated lawyers, and final decision.

Acknowledgements:

This data was collected and distributed by the US Department of Labor.

Inspiration:

• Can you predict visa decisions based on employee/employer/wage?
• How does this data compare to H1B decisions in this dataset?

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

Graph and download economic data for Employment Level - Native Born (LNU02073413) from Jan 2007 to Jun 2025 about native born, 16 years +, household survey, employment, and USA.

The purpose of this study aimed to explain the flow of communication in Indonesian female migrant workers’ protection by the government through the Migrant Worker Family Community (KKBM). The method used in this study was qualitative with a case study approach. The existence of Indonesian female migrant workers has contributed to state revenues of up to US$ 10.97 billion in 2018. This income is higher than the state income of the mining sector. The amount of income provided by female migrant workers to the country is not proportional to the protection services they receive. Most female migrant workers and families preferred to keep the case they face and would report it when it becomes a crisis. Some claims faced by female migrant workers conflict with a employer because of unpaid salaries and inappropriate work. It happens because female migrant workers and families do not know the flow of the case reporting scheme. The pathway for protecting female migrant workers involves a variety of government agencies and private companies. Each institution has its protection program, such as the National Agency for Placement and Protection of Indonesian migrant workers (BNP2TKI), the Ministry of Manpower, the Ministry of Foreign Affairs, and the Indonesian Migrant Workers Distribution Company, without explicit coordination between each institution. One of the aspects of protection strategies through the BNP2TKI is to create the Migrant Worker Family Community (KKBM) as a bridge in providing protection services directly to female migrant workers and families. The program is run bottom-up by involving Ex-migrant workers as the driving force (CO); one of them is like in the Cirebon district. Government communication with female migrant workers and families through Cirebon KKBM is carried out in a multi-step flow communication by combining direct communication and media. The KKBM communicated directly to female migrant workers (candidates) and families to socialize programs supported by pamphlets regarding job vacancy, people's business credit submission procedures, information about the BP2TKI's regulation, and other supporting information. The results of the assistance are then reported by the provincial government (BP3TKI) via telephone or WhatsApp Group and required meetings. Unfortunately, the KKBM socialization process was hampered by the busy activities of the KKBM activists and the government, which focused on the success of this program on the KKBM's CO with a minimal budget and supporting facilities

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.