A Look at Visa Applications Through the Years
Introduction
The United States is one of the most culturally diverse countries in the world. People from all backgrounds immigrate to the U.S. in search of new opportunities and the chance for a better life. As a country, the United States welcomes these individuals through the H-1B Visa application. This visa allows people to apply for work in the United States through a “specialty occupation.” A “specialty occupation” is any profession that requires a minimum of a bachelor’s degree within numerous fields like architecture, engineering, education, etc.
The H-1B Visa also aids people in applying for a green card an eventually citizenship. In some instances, companies have sponsored green card applications for the employees who have demonstrated profound understanding in their respective fields. The H-1B Visa is an important first step in attaining the long-sought after “American dream.”
A dataset consisting of all Visa applications between 2011 and 2016 was acquired from Kaggle (https://www.kaggle.com/nsharan/h-1b-visa). The goal of this Shiny app was to be exploratory. It allows users to see trends in the visa status for the careers they wish to pursue.
Data Summary
The raw dataset includes the following categories:
- Case Status
- Employer Name
- SOC Name
- Job Title
- Prevailing Wage
- Year
- Location
Observations
The first step was to look at the changing number of applicants each year. After applying this data to a line graph, you can see that the number of applicants drastically increases each year. Between 2011 and 2016, the number of applicants more than doubled from 200K to 424K in only five years.
Furthermore, using another line graph, we can also visualize the status of applicants as they changed over the years. In 2011, there we approximately 191K certified applicants, while in 2016 there are approximately 371K certified applicants. Surprisingly, the number of denied applicants has decreased over the years but the number of certified-withdrawn applicants have increased. Finally, we took a look at the relative frequency of the applicants by their case status. This showed that no matter the number, there is always an approximate 86% acceptance rate.
Specific Jobs
The second page in the application allows us to see information about specific jobs whether it be through a standard occupation classification (SOC) or through an actual job title. The data shows that the most numerous visa applications for SOC was a database administrator shown below. The graph also depicts the minimum, maximum, average, and median salary in addition to the total number of awarded full time positions. The final tab allows users to search for a specific job title and see the same information.
Locations
The last page shows a google map with markers indicating where the applicants, selected via the SOC title in the second page, who were certified and given a full-time position currently work. Based on a few sample entries, one can see that the most positions applied to and given were located on the east and west coast.
Conclusion
This app is intended to be exploratory in order to help individuals search for particular positions. When complementary datasets are acquired, we can improve the app by creating better visualizations on the google map as well as establishing a numerical analysis on specific job acceptance percentages.
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