Contributed by Spencer James Stebbins. He takes the NYC Data Science Academy 12 week full time Data Science Bootcamp program from July 5th to September 22nd, 2016. This post is based on their first class project – the Exploratory Data Analysis Visualization Project, due on the 2nd week of the program. You can find the original article here.
Are Student Loans a Subprime Time-bomb for the US Government?
There is overwhelming concern among politicians, professionals, and students that the current student loan market may be the next soaring hot air ballon primed to run out of gas and collapse. For students facing rising tuition costs, increased competition among peers, and a still uncertain job market, they are concerned about whether they will have the ability to pay off the increasing amount of debt they have taken on in order to pursue an education believed necessary to stay competitive. On the other hand, the US government in the wake of the 2008 recession needed to get people back to work or at least back to school and increasing student loan offerings afforded a way to accomplish this goal. Although the current student loan market is only 1/10 the size the mortgage loan market, many articles have illuminated similar trends between the two claiming that student loans may be the next mortgage crisis, but are these headlines valid? In order to attempt to answer this question, lets first investigate some existing student loan trends…
Increasing total outstanding student loan debt
Many readers may be aware that there is an outstanding student loan debt problem in the United States. As of 2015, there was 1.3 trillion dollars in outstanding student loan debt and this number has increased steadily over the past 10 years.
Source: QUANDL:FRED-MDOAH – St.Louis Federal Reserve Bank
#load data
student_loans_outstanding_df <- read.csv("https://fred.stlouisfed.org/data/SLOAS.csv")
#change column name
student_loans_outstanding_df <- transmute(student_loans_outstanding_df, Date=DATE, student_loans_outstanding=VALUE)
#convert to dollars
student_loans_outstanding_df$student_loans_outstanding = student_loans_outstanding_df$student_loans_outstanding * 100000
#convert to Date
student_loans_outstanding_df$Date = as.Date(student_loans_outstanding_df$Date)
#plot
ggplot(student_loans_outstanding_df, aes(x=Date,y=student_loans_outstanding)) +geom_line( color='red') + ggtitle("Outstanding Student Loan Debt") + scale_x_date( labels = date_format("%Y")) + scale_y_continuous( labels = comma) + theme_fivethirtyeight() + theme(legend.title=element_blank()) + theme(axis.title = element_text(), axis.title.x = element_blank()) + ylab('Dollars')
More students take on more debt
Over the same period, the average debt of graduates has increased to almost $27,000 per graduate and the percentage of graduates with student loan debt has increased to nearly 60%.
Source: TICAS – The Institute for College Access & Success
Source: QUANDL:FRED-MDOAH – New York Federal Reserve Bank
Source: TICAS – The Institute for College Access & Success
#select data from main dataframe
percent_federal_debt <- select(data,Date, Federal.debt.of.graduates..as.percent.of.total.debt)
#melt to single column
percent_federal_debt <- melt(percent_federal_debt ,id = 'Date', value.name='Percent')
#plot
ggplot(percent_federal_debt , aes(x=Date,y=Percent,group=variable,color=variable)) + geom_line() + ggtitle('Federal Borrowing as Percent of Total Debt') + theme_fivethirtyeight() + theme(legend.title=element_blank()) + theme(axis.title = element_text(), axis.title.x = element_blank()) + ylab('Percent')+ theme(legend.position="none")
Student loans make up a large, increasing portion of the US Govt.’s assets
Student loans comprise nearly 27% of the US federal government’s total assets as reported in the 2015 US Treasury’s financial statements. ($845.1B of the $3,229.8B). This is a 3.5% increase since 2014 ($731.2B of $3,065.3B). Although this pales in comparison to the over 21 trillion dollar US deficit, student loans are becoming an ever growing part of the US governments’ assets since 2009/10.
Source: TICAS – The Institute for College Access & Success
#manually input data, student loan asset, and total federal asset data
student_loan_asset <- c(84.5,92.1,97.7,101.0,108.0,124.4,157.8,231.3,356.1,495.5,613.9,731.2,845.1)
total_federal_asset <- c(1405.4,1397.3,1447.9,1496.5,1581.1,1974.7,2667.9,2883.8,2707.3,2748.3,2968.3,3065.3,3229.8)
date <- c('2003','2004','2005','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015')
#create dataframe
student_loan_federal_asset_data <- data.frame(Date=date,student_loan_asset=student_loan_asset, total_federal_asset=total_federal_asset)
#create percent column
student_loan_federal_asset_data$Percent= student_loan_asset/total_federal_asset
#remove other columns
student_loan_as_percentage_federal_assets <- select(student_loan_federal_asset_data, Percent, Date)
#convert percent to integers
student_loan_as_percentage_federal_assets$Percent <- as.numeric(student_loan_as_percentage_federal_assets$Percent) * 100
#convert data
student_loan_as_percentage_federal_assets$Date =as.Date(student_loan_as_percentage_federal_assets$Date)
#plot
ggplot(student_loan_as_percentage_federal_assets , aes(x=Date,y=Percent,group='',color='red')) + geom_line() + ggtitle('Student Loans as Percent of Federal Assets') + theme_fivethirtyeight() + theme(legend.title=element_blank()) + theme(axis.title = element_text(), axis.title.x = element_blank()) + ylab('Percent')+ theme(legend.position="none")
What changed in 2009/10 to prompt increasing student loans?
The Health Care and Education Reconciliation Act of 2010 was signed into law by President Barack Obama on March 30, 2010. Many people have heard of “Obamacare”, which is the nickname this program took on, but what they may not know is that in addition to the major changes implemented in the Healthcare sector, this act also implemented some major student loan reforms as well.
Here are some of the changes that were made by President Obama:
- The federal government will no longer give subsidies to private lending institutions for federally backed loans.
- Borrowers of new loans starting in 2014 will qualify to make payments based on 10% of their discretionary income.
- New borrowers would also be eligible for student loan forgiveness after 20 years instead of 25 on qualifying payments.
- Money will be used to fund poor and minority students and increase college funding.
It is highly probable that the increase in student loan borrowing was promoted by the 2008 recession and the ensuing high unemployment rate which prompted many to return to school, but the changes implemented by the Health Care and Education Reconciliation Act of 2010 allowed many people the means to return to school and consequently the government now owns 90% of all student loans.
More unemployed & underemployed graduates
Although unemployment rates have been stabilizing since the recession in 2008, an increasing number of graduates are still unemployed or underemployed. Underemployment includes three groups of people: unemployed workers who are actively looking for work; involuntarily part-time workers who want full-time work but have had to settle for part-time hours; and so-called marginally-attached workers who want and are available to work, but have given up actively looking. Additionally, the percentage of employed college graduates under age of 27 who were working in a job that did not require a college degree increased from 38 to 46 percent from 2007 to 2014 (Abel and Deitz 2014).
#load data from csv
grad_unemployment_data <- read.delim("https://raw.githubusercontent.com/sjstebbins/ExploratoryDataViz/master/data/graduateunemployment.csv")
#convert date
grad_unemployment_data$Date = as.Date(grad_unemployment_data$Date)
#filter for after 2003
grad_unemployment_data <- filter(grad_unemployment_data, Date >= "2003-01-01")
#melt to single column
grad_unemployment_data <- melt(grad_unemployment_data, id = 'Date',measure.vars = names(select(grad_unemployment_data,-Date)))
#plot
ggplot(grad_unemployment_data, aes(x=Date,y=value, group=variable, color=variable)) + geom_line() + ggtitle('Graduate Unemployment & Underemployment') + theme_fivethirtyeight() + theme(legend.title=element_blank()) + theme(axis.title = element_text(), axis.title.x = element_blank()) + ylab('Percent')
Graduate earnings are flat In addition to the large number of graduates who are unemployed or underemployed, initial young graduate earnings are not increasing along with increasing debt they are taking on. On average, young college graduates have an hourly wage of $17.94, which translates to an annual salary of roughly $37,300 for a full-time, full-year worker. This is a decline of 2.5 percent from what a typical college graduate would have made in 2000 ($38,300).
* Data reflects 12-month moving averages; data for 2015 represent 12-month average from April 2014 to March 2015. Note: Data are for college graduates age 21–24 who do not have an advanced degree and are not enrolled in further schooling, and high school graduates age 17–20 who are not enrolled in further schooling. Shaded areas denote recessions. Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata
#load data
earnings_data <- read.delim("https://raw.githubusercontent.com/sjstebbins/ExploratoryDataViz/master/data/earnings.csv")
#convert date
earnings_data$Date = as.Date(earnings_data$Date)
#filter data for after 2003
earnings_data <- filter(earnings_data, Date >= "2003-01-01")
#rename columns
earnings_data <- rename(earnings_data, college_grads=All, high_school_grads=All.1)
#melt data to single column
earnings_data <- melt(earnings_data, id = 'Date',measure.vars = names(select(earnings_data, -Men,-Women,-Men.1,-Women.1,-Date)))
#plot
ggplot(earnings_data, aes(x=Date,y=value, group=variable, color=variable)) + geom_line() + ggtitle("Graduate Earnings") + theme_fivethirtyeight() + theme(legend.title=element_blank()) + theme(axis.title = element_text(), axis.title.x = element_blank()) + ylab('Hourly Wage')
Rising cost of attending college Between 2003 and 2013, the average cost of attending college increased over 30%. This rising cost of college combined with the failure of wages to grow for young college graduates signals that a college education is becoming a more uncertain investment. Universities have taken advantage of the influx of students and their accessibility to loans and have increased tuition at a rate that far outweighs unemployment recovery and graduate earnings, signaling that college is becoming an increasingly difficult investment.
Source: TICAS – The Institute for College Access & Success
#select data from main data frame
increasing_college_costs <- select(data,Date,Total.cost.of.attendance..on.campus.)
#melt to single column
increasing_college_costs <- melt(increasing_college_costs,id = 'Date', value.name='Amount.in.Thousands')
#plot
ggplot(increasing_college_costs, aes(x=Date,y=Amount.in.Thousands,group=variable,color=variable)) + geom_line() + ggtitle('Average College Costs') + theme_fivethirtyeight() + theme(legend.title=element_blank()) + theme(axis.title = element_text(), axis.title.x = element_blank()) + ylab('Dollars')+theme(legend.position="none")
