I know it has been awhile since the last post but this one will hopefully be worth the wait. Assigning the article, The Student Debt Problem is Worse Than We Imagined, was a great way to start the semester with my QR class and get them thinking about the social construction of statistics a la Joel Best in Damned Lies and Statistics (DLS). On the surface, it would seem that keeping track of the number of students in default on their student loan debt would be straightforward; and provide a means for holding institutions of higher education accountable for the education they are providing using taxpayer money. My students definitely approached this issue from the vantage point of schools overcharging and being too expensive, but struggled a bit with the idea of for-profit schools preying on vulnerable students. They (my students) hadn’t really thought through who was holding the debt, basically assuming the school was lending the money to the students. The fact that the schools were simply cashing checks from the federal government, and had no liability in terms of debt repayment was surprising to my students.
So the federal government needs to ensure that students taking out loans are being provided an education that will generate a livable income enabling debt repayment. They do this by tracking student loan defaults for 3 years and penalizing schools with high default rates. Clearly we need to define “high”, but we also need to consider the “3 years”. Why 3 years? Who do they track for 3 years? How is “default” defined versus “delinquency” or “serious delinquency?” All of these definitions and organizational practices play a role in constructing the statistics about and scope of the student loan problem. The graphic above shows only 2.1% of schools having “high” “default” rates after three years, but after five years there are 13.1% of schools in this category! How do they magically avoid being sanctioned during the three year window? By encouraging students to take advantage of deferments provided by the government, which sounds altruistic until you realize that often they are just prolonging the inevitable default once the student has passed safely beyond the three year tracking window.
This article is loaded with subtle quantitative arguments for your students to grapple with and weigh in on. Also basic quantitative literacy questions abound, like trying to determine in the graphic above what the 15.5% represents, “15.5% of what group?” stumped most of my students. They simply responded it represented the share of students defaulting on loans as the graphic states, but that does not answer the question. Which schools see the highest increase in defaults from year 3 to year 5?
Obviously the for-profit schools are the most egregious but the private nonprofit schools have an identical percentage increase over this two year period! I highly recommend the article, my students were engaged in the topic and enjoyed the discussions. Here is the take home quiz.
Q2 Student Loans
- Lots of good graphics and statistics in this article :O)
- Whenever we encounter a percentage like the 15.5% in the graphic (2nd one above) we need to make sure we know what it is a percentage of. So 15.5% of what group?
- The article states this 15.5% represents 841,000 people from the group you identified in part a. How large is this group?
- If each of the people from part b owes $22,000 by graduation what is the total amount of debt?
- If there is one thing Joel Best is going to tell us it’s that: Definitions matter!
- Go online and find the difference between being “delinquent” and being in “default”.
- In what way is the problem of student loan defaults socially constructed by these definitions?
- What is the definition of a school with a “high default rate” and how does this impact whether or not a school can participate in the federal student loan program?
- Joel says there is often a hidden purpose to social statistics like the student default rates. What is this hidden purpose and who would benefit from having the student default rate be reported as low?
- Ok I can’t resist, use the following graphic (last one above) and compute the percentage change in average default rates for each of the three types of schools from 2014 to 2016. Who sees the biggest increase in percentage change? Remember that percentage change is the total change (in ppts) divided by the original value.