Employee Visa

The H1B dataset offers quarterly insights into foreign hiring trends, job details, and wages for informed decision-making.

Data arrives late Friday night 11 pm - 12 as new quarterly data becomes available.

Tutorials are the best documentation — Employee Visa Tutorial

Input Datasets

Government Data

Models Used

Parsing, Regex

Model Outputs

Standardized Rows

Description

The Employee Visa dataset provides quarterly insights into foreign hiring trends among U.S. companies, offering detailed information on job positions, wages, and visa applications across various temporary work visa categories.

This data can serve as a valuable tool for investors to analyze labor market trends, assess company growth strategies, and gauge the impact of immigration policies on different sectors and businesses.

Data Access

H1B Table: This table offers quarterly data to track foreign hiring patterns for publicly traded companies.

from sovai import sov
df_visa = sov.data("visas/h1b")

This dataset encompasses detailed records from labor condition applications, which are indicative of employment patterns within companies that hire foreign workers under various temporary visa categories in the United States. It provides a comprehensive view of job positions, wages, and employment periods.

Data Dictionary

Name	Description	Type	Example
predicted_pay	Predicted salary for the job position	float64	190195
case_status	Status of the case/application	object	certified
case_number	Unique identifier for the case/application	object	i-203-17089-869756
decision_date	Date on which the decision was made	object	2017-04-06 0:00:00
visa_class	Type of visa applied for	object	e-3 australian
begin_date	Start date of employment	object	2017-07-02 0:00:00
end_date	End date of employment	object	2019-07-01 0:00:00
employer_name	Name of the employer	object	apple inc.
employer_address1	Address line of the employer	object	one infinite loop
employer_city	City where the employer is located	object	cupertino
employer_state	State where the employer is located	object	ca
employer_postal_code	Postal code of the employer	object	95014
soc_code	Standard Occupational Classification code	object	11-3021
soc_title	Title associated with the SOC code	object	computer and information systems managers
job_title	Title of the job	object	sw develop mgr 3
wage_rate_of_pay_from	Starting wage rate	float64	190195
wage_rate_of_pay_to	Ending wage rate	float64	190195
wage_unit_of_pay	Unit for the wage rate	object	year
full_time_position	Whether the position is full-time	object	y
worksite_address2	Secondary worksite address	object	None
worksite_state	State of the worksite	object	ca
prevailing_wage	Standard wage for the position	float64	190195
pw_unit_of_pay	Unit for the prevailing wage	object	year
pw_survey_name	Name of the prevailing wage survey	object	None
pw_other_source	Other source for prevailing wage	object	oflc online data center
pw_oes_year	Year of the OES prevailing wage	float64	2016
pw_survey_publisher	Publisher of the prevailing wage survey	object	None
naics_code	North American Industry Classification System code	float64	334111
unique_id	Unique identifier combining case number, soc_code, and employer state	object	i-203-17089-869756_11-3021_ca
wage_potential_increase	Potential increase in wage	float64	0
similarity	Similarity score	float64	1
bloomberg_share_id	Bloomberg's share identifier	object	BBG001S5N8V8
total_worker_positions	Total number of worker positions	float64	1
new_employment	Indicates new employment	float64	0
continued_employment	Indicates continued employment	float64	1
change_previous_employment	Indicates a change in previous employment	float64	0
new_concurrent_employment	Indicates new concurrent employment	float64	0
change_employer	Indicates a change of employer	float64	0
amended_petition	Indicates if there is an amended petition	float64	0

The dataset includes 39 columns, each representing a specific attribute related to labor condition applications. Key attributes include:

• Case status and number, providing insight into the application's outcome and unique identification.

• Visa class, which distinguishes between different types of temporary work visas.

• Employment period, outlined by the begin and end dates of employment.

• Employer information, including name, address, city, state, and postal code.

• Job details, such as job title, Standard Occupational Classification (SOC) code and title.

• Wage information, including the rate of pay and prevailing wage details.

• Worksite information, offering location details where the employment takes place.

• Additional attributes related to the petition's nature, like new employment, continued employment, and any amendments.

---

Use Cases

1. Labor Market Analysis: Investors can identify trends in employment such as demand for specific roles or average wages offered across sectors, informing investment strategies.

2. Compliance Monitoring: Companies can ensure their wage offerings are competitive and compliant with prevailing wage standards for different visa categories.

3. Immigration Impact Assessment: Policy analysts can evaluate the effects of visa policies on workforce composition and availability in various industries.

4. Strategic Planning: Businesses can plan recruitment strategies based on the availability of talent within certain visa classes and adjust their workforce accordingly.

5. Investment Decision Making: Investors can gauge the health and growth potential of sectors by analyzing the number of new and continuing employment positions.

---