Through exploratory data analysis and data visualization, our research hopes to observe whether obtaining a STEM (Science, Technology, Engineering and Mathematics) degree truly ensures that an individual will obtain a lucrative career, i.e. they are well compensated and not unemployed. From our previous research (see Gender Wage Inequality in STEM), we know that not all STEM degrees secure the same level of median earnings, so here we visualize levels of unemployment for the STEM major categories of Biology & Life Sciences, Computers and Mathematics, Engineering, Health, and Physical Sciences compared to all other major categories.
To address this problem, we used data sets behind the fivethirtyeight story The Economic Guide To Picking A College Major which can be found in the following GitHub repo: https://github.com/fivethirtyeight/data/blob/master/college-majors
All data is from American Community Survey 2010-2012 Public Use Microdata Series.
Our variables are as follows:
| Header | Description |
|---|---|
Rank |
Rank by median earnings |
Major_code |
Major code, FO1DP in ACS PUMS |
Major |
Major description |
Major_category |
Category of major from Carnevale et al |
Total |
Total number of people with major |
Sample_size |
Sample size (unweighted) of full-time, year-round ONLY (used for earnings) |
Men |
Male graduates |
Women |
Female graduates |
ShareWomen |
Women as share of total |
Employed |
Number employed (ESR == 1 or 2) |
Full_time |
Employed 35 hours or more |
Part_time |
Employed less than 35 hours |
Full_time_year_round |
Employed at least 50 weeks (WKW == 1) and at least 35 hours (WKHP >= 35) |
Unemployed |
Number unemployed (ESR == 3) |
Unemployment_rate |
Unemployed / (Unemployed + Employed) |
Median |
Median earnings of full-time, year-round workers |
P25th |
25th percentile of earnings |
P75th |
75th percentile of earnings |
College_jobs |
Number with job requiring a college degree |
Non_college_jobs |
Number with job not requiring a college degree |
Low_wage_jobs |
Number in low-wage service jobs |
- Libraries:
dplyr,ggplot2,readr,forcats - Functions:
read_csv,inner_join,select,summarize,group_by,pivot_longer,mutate,ggplot