Introduction

Data is provided by Fivethirtyeight and is accessed via the fivethirtyeight library.

The dataset was used for the article “The Economic Guide To Picking A College Major” found here: https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/

The dataset can also be found here:

https://github.com/fivethirtyeight/data/blob/master/college-majors/recent-grads.csv

About the Data

The data was originally pulled from American Community Survey 2010-2012 Public Use Microdata Series. The article utilizes several datasets but this anaylsis will focus on recent graduates. The data includes a graduate’s major, the field that their major falls into, median income for the major, and numbers of graduates in jobs that utilize their college degree versus number of graduates in low-wage jobs.

Of interest is which fields contain majors with the most economic return both in terms of income and proportion of graduates utilizing their college degrees in their current employment.

Load libraries and view dataframe

The dataframe is accessed via the fivethirtyeight library.

library(fivethirtyeight)
library(tidyverse)
library(ggplot2)
head(college_recent_grads)
## # A tibble: 6 x 21
##    rank major_code major major_category total sample_size   men women sharewomen
##   <int>      <int> <chr> <chr>          <int>       <int> <int> <int>      <dbl>
## 1     1       2419 Petr… Engineering     2339          36  2057   282      0.121
## 2     2       2416 Mini… Engineering      756           7   679    77      0.102
## 3     3       2415 Meta… Engineering      856           3   725   131      0.153
## 4     4       2417 Nava… Engineering     1258          16  1123   135      0.107
## 5     5       2405 Chem… Engineering    32260         289 21239 11021      0.342
## 6     6       2418 Nucl… Engineering     2573          17  2200   373      0.145
## # … with 12 more variables: employed <int>, employed_fulltime <int>,
## #   employed_parttime <int>, employed_fulltime_yearround <int>,
## #   unemployed <int>, unemployment_rate <dbl>, p25th <dbl>, median <dbl>,
## #   p75th <dbl>, college_jobs <int>, non_college_jobs <int>,
## #   low_wage_jobs <int>

Subset and Transform Data

Transformations applied to the dataset:

  1. Variables relevant to the analysis were selected. These include major, field the major falls into, total number of recent graduates in the major, unemployment rate, median income, number in jobs requiring a college education, and number in low-wage jobs.

  2. Data is grouped by the field that the major falls under.

  3. Median income is computed by field that major falls under. In addition, variables for percent of graduates in jobs requiring a college degree by field, and percent of graduates in low wage jobs by field.

recent_grads <- college_recent_grads %>% 
  select("major", "total", "major_category", "unemployment_rate", "median", "college_jobs", "low_wage_jobs")

recent_grads <- recent_grads %>% group_by(major_category)

recent_grads_bycategory <- recent_grads %>% filter(!is.na (total)) %>% summarise(median_cat = mean(median), total_cat = sum(total), collegjobs_cat = sum(college_jobs), lowwage_cat = sum(low_wage_jobs)) %>% mutate(percentcollege = collegjobs_cat / total_cat * 100, percentlowwage = lowwage_cat / total_cat * 100)

Here we look at the new computed variables:

head (recent_grads_bycategory)
## # A tibble: 6 x 7
##   major_category median_cat total_cat collegjobs_cat lowwage_cat percentcollege
##   <chr>               <dbl>     <int>          <int>       <int>          <dbl>
## 1 Agriculture &…     35111.     75620          18677        7414           24.7
## 2 Arts               33062.    357130          94785       60116           26.5
## 3 Biology & Lif…     36421.    453862         151233       42742           33.3
## 4 Business           43538.   1302376         148538      126788           11.4
## 5 Communication…     34500     392601          86556       49595           22.0
## 6 Computers & M…     42745.    299008         137859       16136           46.1
## # … with 1 more variable: percentlowwage <dbl>

Visualizing the Data

Here we take a look at the median income for graduates by the field that they majored in and see that engineering is the most lucrative field for undergraduates to major in. However, psychology and social work, humanities, and education do not provide immediate return.

ggplot(recent_grads_bycategory, aes(x = reorder(major_category,-median_cat), y = median_cat, fill = "red")) + geom_bar(stat="identity") +
coord_flip() + ggtitle("Median Income by Field of Major") + ylab("Median Income (USD)") + xlab("Field of Major") + scale_y_continuous(labels = scales::dollar_format(), limits=c(0,62000)) + theme(legend.position = "none")

Conclusions

With the current job market uncertainty, this article from 2014 can provide helpful guidance to young adults embarking upon their college studies. One area that the article mentions that I think can be further explored with young adults is the number of people who take jobs not requiring a college degree following graduation. Another complication to these figures as mentioned in the article is that the figures only account for those right out of college and many in psychology will go on to graduate school. This is still important for students to factor in when taking on debt or possibly asking for familial assistance to pay for school. In the context of larger discussions about student loan debt and the ethical issues that higher education must confront about what large price tags mean for undergraduates today.