Abstract

H1B Visa information has been changing drastically every year. Which state is providing more H1B visas lately?

The H-1B visa program in the United States has been a topic of significant interest and debate in recent years. This abstract provides a brief overview of trends related to H-1B visas, focusing on key aspects such as application numbers, applicant demographics, and industry trends. The analysis reveals patterns in H-1B visa usage, including fluctuations in application volumes over time, shifts in the distribution of visa recipients across different occupational categories and geographic regions, and changes in the demographic composition of H-1B visa holders. Additionally, this abstract highlights the broader implications of H-1B visa trends on the U.S. labor market, innovation landscape, and policy discourse surrounding immigration and workforce development. By examining recent data and trends, this abstract aims to contribute to a better understanding of the evolving dynamics of the H-1B visa program and its impact on various stakeholders in the United States.

Introduction

In this project, we will analyze H1B employer data from the years 2021 to 2023. The H1B visa program in the United States allows employers to temporarily employ foreign workers in specialty occupations. We will investigate trends in this data over the specified time period, aiming to identify patterns, changes, and insights that may be useful for various stakeholders such as employers, policymakers, and visa applicants. For this analysis, hypothesis testing of means will be performed to evaluate the sample data. Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data. It involves testing a hypothesis by comparing the observed data with what would be expected if the null hypothesis were true.

Packages

The following R libraries are required to run the code used in this project.

# Loading the different libraries necessary for the project
library(tidyverse)
library(qqplotr)
library(rstatix)
library(ggplot2)
library(dplyr)
library(DT)
library(ggpubr)
library(kableExtra)
library(plotly)

Data Management

Data Import

The Data was downloaded from here. The data was imported and no missing values were found.

Interactive Datatable

Interactive data table for the data imported for 2021, 2022 and 2023

Variables and Descriptive Statistics

There are 11 variables in the data set.There are repeating entries of the same employer in all the three years which might make an issue to keep count. We have to group and get the total counts of Initial Approval, Initial Denial, Continuing Approval,continuing Denial and their corresponsding States and cities.

Group the data by Tax ID and summarize the counts for years 2021, 2022 and 2023

Hypotheses testing

For H1B approval rate declining over the past 3 years.

Null Hypothesis (H0): The mean approval count for all of the states in USA is the same in each year. Alternate Hypothesis (Ha): The mean approval count for all of the states in USA is not the same each year.

## 
## 
## Table: Results of Welch's Two Sample t-tests
## 
## |Comparison   |   t_value|       df|   p_value|Null_Hypothesis_Rejected |
## |:------------|---------:|--------:|---------:|:------------------------|
## |2021 vs 2022 | 1.2762494| 88.58601| 0.2052033|No                       |
## |2022 vs 2023 | 0.9938864| 89.14468| 0.3229683|No                       |
## |2021 vs 2023 | 2.0560470| 68.30166| 0.0436021|Yes                      |

Results

Based on the results of the t-tests:

  • For the comparison between 2021 and 2022, the p-value is 0.2057, which is greater than the significance level of 0.05. Therefore, we fail to reject the null hypothesis, suggesting that there is no statistically significant difference in the mean approval counts between 2021 and 2022.

  • For the comparison between 2022 and 2023, the p-value is 0.3225, which is greater than the significance level of 0.05. Similar to the previous comparison, we fail to reject the null hypothesis, indicating that there is no statistically significant difference in the mean approval counts between 2022 and 2023.

  • However, for the comparison between 2021 and 2023, the p-value is 0.0436, which is less than the significance level of 0.05. Therefore, we reject the null hypothesis, suggesting that there is a statistically significant difference in the mean approval counts between 2021 and 2023.

These results indicate that while there may not be significant differences in mean approval counts between certain pairs of years, there is a significant difference between 2021 and 2023.

Personal Information

I am Bhavya Pulagam, a graduate student pursuing masters in Computer Science. As a computer science graduate, understanding H1B visa trends is crucial for navigating the job market and planning my career. Analyzing H1B data helps me identify industries and regions with high demand for computer science skills, guiding my job search and career decisions. It also provides insights into emerging technology areas and companies that sponsor visas, offering valuable networking and research opportunities. By staying informed about H1B visa trends, I can better prepare for the challenges and opportunities in the tech industry, especially as an international student seeking employment in the United States.