Name: H-1B Non-Immigrant Labour Visa
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

H-1B Non-Immigrant Labour Visa

Investigating Impact on Job Market, Salary, & Approval Rate 2011-2018

By [source]

About this dataset

This dataset contains information about the H-1B visas obtained by US employers to employ foreign workers in specialised roles, such as engineers, scientists and software developers. This data covers multiple aspects of the visa petition process, such as detailed information regarding the petitioners, their job titles and salaries and whether their applications were successful or not. With this wealth of data at one's fingertips, we have a unique opportunity to explore the impact that these visas have on employment opportunities for skilled workers in America - from prevailing wage levels to success rates for visa applications. By studying this dataset we can gain deeper insight into labour dynamics that are often regulated by government policies surrounding immigration laws and regulations

More Datasets

For more datasets, click here.

Featured Notebooks

🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to investigate the impact of H-1B visas on the US job market, salary and approval rate. This dataset provides detailed information about all filed petitions for H-1B visas in the US between 2011 to 2018. It includes relevant information such as petitioners' names, job titles, countries of origin, salary information and respective case outcomes.

To use this dataset effectively you will need some basic understanding of SQL queries and Excel functions such as SUM(), COUNTIF() and AVERAGE(). Below are some steps to help you get started:

Identify which questions you would like to answer using this data (e.g., What is the overall approval rate for H-1B visa applications?)

Select relevant columns from the dataset based on your question (e.g., if looking at salaries select ‘prevailing_wage’)

Use SQL queries or Excel functions to filter, group by or summarize your results (e.g., group payments into salary levels using ‘CASE statement’ in an SQL query)

Plot results using a chart or graph as visual representation
5 Analyze your results and draw conclusions

Research Ideas

Analyzing the impact of visa duration on job performance: By tracking the number of H-1B visas granted in a given year and then cross-referencing it with employer reviews, it will be possible to gain insight into employee performance over time as well as how long they remain employed by their sponsoring organization. This would allow employers to better understand which factors impact employee retention and productivity, giving them the tools they need to adjust policies accordingly.

Investigating wage disparities: Taking a look at H-1B visas which are rejected or accepted with different salary levels is an exciting opportunity for research. It will provide us with insight into wage discrepancies between domestic workers and foreign workers, allowing policy makers to better understand why certain industries may be having difficulty filling posts domestically or attracting foreign talent base on wage levels

Assessing education requirements for specific occupations: Examining the qualifications required for specific job title within this dataset can give industry leaders and recruiters valuable information about what type of training or educational requirement is best suited for each position based on data from successful applications in recent years. This could prove extremely useful when making hiring decisions or when setting up company apprenticeship programs that target new college graduates entering the workforce

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: h1b_data.csv

Column name	Description
case_year	The year in which the case was submitted. (Integer)
case_status	The status of the case, either approved or denied. (String)
case_submitted	The date on which the case was submitted. (Date)
decision_date	The date on which the decision was made. (Date)
emp_name	The name of the employer. (String)
emp_city	The city in which the employer is located. (String)
emp_state	The state in which the employer is located. (String)
emp_zip	The zip code of the employer. (Integer)
emp_country	The country in which the employer is located. (String)
job_title	The title of the job for which the visa is being applied. (String)
soc_code	The Standard Occupational Classification code for the job. (Integer)
soc_name	The name of the Standard Occupational Classification for the job. (String)
full_time_position	Whether the position is full-time or not. (Boolean)
prevailing_wage	The prevailing wage for the job. (Integer)
pw_unit	The unit of the prevailing wage. (String)
pw_level	The level of the prevailing wage. (String)
wage_from	The minimum wage for the job. (Integer)
wage_to	The maximum wage for the job. (Integer)
wage_unit	The unit of the wage. (String)
work_city	The city in which the job is located. (String)
work_state	The state in which the job is located. (String)
emp_h1b_dependent	Whether the employer is H-1B dependent or not. (Boolean)
emp_willful_violator	Whether the employer is a willful violator or not. (Boolean)
lng	The longitude of the job location. (Float)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

H1b Data

@kaggle.thedevastator_h_1b_non_immigrant_labour_visa.h1b_data

78.98 MB
3360810 rows
25 columns


CREATE TABLE h1b_data (
  "case_year" BIGINT,
  "case_status" VARCHAR,
  "case_submitted" TIMESTAMP,
  "decision_date" TIMESTAMP,
  "emp_name" VARCHAR,
  "emp_city" VARCHAR,
  "emp_state" VARCHAR,
  "emp_zip" VARCHAR,
  "emp_country" VARCHAR,
  "job_title" VARCHAR,
  "soc_code" VARCHAR,
  "soc_name" VARCHAR,
  "full_time_position" VARCHAR,
  "prevailing_wage" DOUBLE,
  "pw_unit" VARCHAR,
  "pw_level" VARCHAR,
  "wage_from" DOUBLE,
  "wage_to" DOUBLE,
  "wage_unit" VARCHAR,
  "work_city" VARCHAR,
  "work_state" VARCHAR,
  "emp_h1b_dependent" VARCHAR,
  "emp_willful_violator" VARCHAR,
  "lat" DOUBLE,
  "lng" DOUBLE
);