Introduction

The dataset used was titled ‘Recent_Grads’. This dataset includes 173 majors or observations that are based on recent college graduates, the elements of the dataset, and identifies their major category, gender, employment status, income, and whether or not their jobs are related to their college degrees. The dataset came from Georgetown University Center on Education and the Workforce for Recent Grads.

This dataset was chosen because we wanted to answer a few questions we have had about college majors. We come into college with a brief sense of what we want to do “when we grow up.” Our dreams are often crushed by the realization that a college degree is not the direct equivalent of a job. Furthermore, the major that you spent 4 years working towards may not be needed for your job in the future. We found this study interesting because it opened up the opportunity for us to analyze the data and answer questions that we have wondered since before our freshman year of college.

Research Questions

Which of the top 3 major categories produces graduates with the highest unemployment rate?

Were the unemployment rates higher for males or females in each of the Top 3 major categories?

In order to analyze this dataset, we loaded the following packages:

library(fivethirtyeight)
library(ggplot2)
library(dplyr)

Next we loaded the dataset for ‘Recent_Grads’:

Recent_Grads <- read.csv(url("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/recent-grads.csv"))

The variables in the ‘Recent_Grads’ dataset that will be analyzed are:

Data & Analysis

# Barplot with Color for Top 3 Major Categories
Top_Major_Cat <- c("Engineering" = 29, "Education" = 16, "Humanities & Liberal Arts" = 15)
barplot(Top_Major_Cat, main="Top 3 Major Categories", xlab="Major Categories", ylab="Count of Majors within Major Category", col=c("darkblue", "red"))

#Male & Female Presidence within the Lowest Unemployment Rate of the Top 3 Major Categories

TMC_LUR <- c("Eng. M" = 3526, "Eng.F" = 795, "Edu.M" = 280, "Edu. F" = 524, "H & LA M" = 1756, "H & LA F" = 1323)
barplot(TMC_LUR, main="M & F Presidence w/in the Lowest UR of the Top 3 Major Categories", xlab="Male & Female in Major Categories", ylab="Count of Graduates", col=c("darkblue", "red"))

#Male & Female Presidence within the Highest Unemployment Rate of the Top 3 Major Categories

TMC_HUR <- c("Eng. M" = 2200, "Eng.F" = 373, "Edu.M" = 119, "Edu. F" = 699, "H & LA M" = 3472, "H & LA F" = 7732)
barplot(TMC_HUR, main="M & F Presidence w/in the Highest UR of the Top 3 Major Categories", xlab="Male & Female in Major Categories", ylab="Count of Graduates", col=c("darkblue", "red"))

In the ‘Barplot with Color for Top 3 Major Categories’, we have visually shown the top 3 major categories amongst the recent graduates from this dataset. The y-axis represents the number of majors falling within each category.

In the ‘Barplot with Color for Top 3 Major Categories’, the most popular major category, engineering, presents the lowest unemployment rate at 0.01, corresponding to the Engineering Mechanics Physics and Science major. The highest unemployment rate for the engineering major category was presented at 0.18, corresponding to the Nuclear Engineering major. In the ‘Barplot Male & Female Presidence within the Lowest Unemployment Rate of the Top 3 Major Categories’, it can be seen that there are significantly more male college graduates than female in the Engineering Mechanics Physics and Science major from the engineering category. In the ‘Male & Female Presidence within the Highest Unemployment Rate of the Top 3 Major Categories’, it can be seen that there are significantly more male college graduates than female in the Nuclear Engineering major from the engineering category.

In the ‘Barplot with Color for Top 3 Major Categories’, the second most popular major category, education, presents the lowest unemployment rate at 0.00, corresponding to the Educational Administration and Supervision major. The highest unemployment rate for the education major category was presented at 0.11, corresponding to the School Student Counseling major. In the ‘Barplot Male & Female Presidence within the Lowest Unemployment Rate of the Top 3 Major Categories’, it can be seen that there are significantly more female college graduates than male in the Educational Administration and Supervision major from the education category. In the ‘Male & Female Presidence within the Highest Unemployment Rate of the Top 3 Major Categories’, it can be seen that there are more significantly female college graduates than male in the School Student Counseling major from the education category.

In the ‘Bar Plot with Color for Top 3 Major Categories’, the third most popular major category, humanities & liberal arts, presents the lowest unemployment rate at 0.05, corresponding to the United States History major. The highest unemployment rate for the humanities & liberal arts major category was presented at 0.11, corresponding to the Other Foreign Languages major. In the ‘Barplot Male & Female Presidence within the Lowest Unemployment Rate of the Top 3 Major Categories’, it can be seen that there are significantly more male college graduates than female in the United States History major from the humanities and liberal arts category. In the ‘Male & Female Presidence within the Highest Unemployment Rate of the Top 3 Major Categories’, it can be seen that there are significantly more female college graduates than male in the Other Foreign Languages major from the humanities and liberal arts category.

Conclusion

After analyzing the data regarding unemployment rates and gender in the top 3 major categories for college graduates we were able to identify answers to our research questions.

Of the top 3 major categories, humanities and liberal arts produced the highest unemployment rates for recent graduates in the Other Foreign Languages major. Of the top 3 major categories, education produced the lowest unemployment rates for recent graduates in the Educational Administration and Supervision major.

The unemployment rates were found to be higher for females in both the Humanities and Liberal Arts major category and Education major category. The Engineering major category was the only one out of the three that produced the highest unemployment rates for men. This could be because it is a predominantly male-dominated field but it would be unfair to speculate that this is the only reason why.

The analysis of the data was able to give us sufficient information to come to a conclusion regarding our research questions. It did, however, beg other questions left unanswered about gender in the workplace. Are women more likely to be unemployed due to their major chosen, inequality in the workplace, choice or, by mere chance?