A college degree has become increasingly necessary to enjoy a middle-class lifestyle. However, higher tuition rates and a poor labor market have caused students to increasingly feel the burden of educational debt in recent years. Let’s take a look at the student debt characteristics of borrowers in the four large metropolitan statistical areas (MSA) in the Federal Reserve Bank of St. Louis’ district—Little Rock, Louisville, Memphis and St. Louis—focusing on differences between low- and moderate-income (LMI) and middle- and upper-income (MUI) areas.
One way to analyze how LMI communities are faring with respect to educational attainment and debt financing is to geographically partition the population based on income. Using standard practice, census tracts are classified as LMI if the median income within the census tract was below 80 percent of the MSA median income. If the tract median income was greater than or equal to 80 percent of the area median income, it was classified as MUI.
Income data is based on the five-year 2009 American Community Survey estimates. Regional data on student debt was obtained using the Federal Reserve Bank of New York Credit Panel based on Equifax credit reports. This individual-level data allows for rich geographical analysis of student loans. It tracks the liabilities of a random sample of the United States population over time.
Much of the difference in student debt levels between LMI and MUI census tracts may be due to the differences in educational levels. Figure 1 shows the percentage of those aged 25 to 44—living in any of the four St. Louis Fed MSAs—with various levels of educational attainment. Of note, people living in LMI tracts are less likely to have attended a postsecondary institution. Further, of those who attended college at some point, those living in LMI tracts are less likely to have finished college and more likely to have attained two-year degrees than those living in MUI tracts. These discrepancies will have implications for debt levels of those who borrow for education.
Educational Attainment by Census Tract Classification in 2012, Ages 25–44
Little Rock–North Little Rock, AR
St. Louis, MO–IL
Source: American Community Survey
Table 1 shows the average student debt across the four metropolitan areas in the fourth quarter of 2005 and the fourth quarter of 2013. The percentage growth in debt per borrower has been larger in all LMI tracts except Louisville. This might be because LMI tracts were starting from a lower base. The growth in debt—in absolute terms—was relatively similar across the LMI and MUI tracts. More interestingly, borrowers in LMI tracts tend to have lower levels of debt than those in MUI tracts. This difference is arguably uniform across the four MSAs. This may seem counterintuitive. Borrowers from LMI tracts likely had fewer resources to attend college so would need to take on more debt and, by definition, likely have less income to make repayments. However, there are several reasons why debt levels might be lower.
Average Debt per Borrower Ages 25–44
Source: FRBNY Credit Panel / Equifax based on author's calculations
Part of this result is likely due to the institutions attended by students from LMI tracts and the level of education they seek. In LMI tracts, many choose community colleges with lower tuition levels. Conditional on having a degree, a higher percentage of students have an associate’s degree. This effect may be attenuated by the fact that borrowers from LMI tracts are also more likely than those from MUI tracts to go to more expensive for-profit schools. (See IHEP 2011 or Deming Goldin and Katz 2011.) Additionally, since borrowers from LMI tracts are less likely to finish college, they may not take on as much debt if they are not financing as much education. However, this is likely a net negative for repayment since they don’t earn the wage premium that comes with graduation. Conversely, a higher percentage of students in MUI tracts attend private institutions and are more likely to attain bachelor’s and even graduate degrees. While these degrees demand higher wages, they tend to have higher educational costs.
Another potential driver of this seemingly contradictory result is that while students from less affluent backgrounds may have fewer resources for education, colleges charge different prices based on income. In what is called price discrimination, colleges subsidize students from lower-income families with financial aid. Hence, the actual price of education tends to differ by family income. (See Wolla, 2014 for more on this phenomenon.)
Finally, students are tracked only after they incur debt. Perhaps students sort where they live after college based on some factor in common with their level of student debt. For example, maybe those with graduate degrees—who likely have the highest levels of debt—move to high-income areas after graduation. Along the same line, perhaps borrowers who perceive lower future income, on average, do not incur as much debt.
The fact that LMI tracts have lower per-borrower balances of student debt does not necessarily mean that they don’t have trouble making payments. Since borrowers from LMI tracts tend to have lower income, they may have problems paying back seemingly reasonable levels of debt. Figure 2 shows the delinquency rates in the four MSAs for both LMI and MUI tracts. Consistently, borrowers from LMI tracts have much higher levels of delinquency than borrowers from MUI tracts. This was true even before the recession. At the end of 2005, borrowers in LMI tracts had much higher delinquency rates than those in MUI tracts. The growth in delinquency has been substantial across cities and income classifications, increasing an average of 5.8 percentage points across both.
Delinquency Rate by Census Tract Classification in Q4 2013
Source: FRBNY Credit Panel / Equifax based on author's calculations
Delinquencies are a concern because of the negative effects they can have on borrowers (e.g., lower credit scores, reduced access to future credit, wage garnishments, etc.). Further, this means that a large portion of borrowers either are not receiving the income they anticipated from their training, are underestimating their debt burden, or a combination of both.
So far, we have only discussed those who have borrowed for school. Another point of interest is the percentage of individuals who had student debt on their credit report. Between 2005 and 2013, this percentage increased across all of the MSAs, but did not differ significantly across income classification. This is a significant result given that those living in LMI census tracts are much less likely to have attended college.
In conclusion, LMI communities are feeling the effects of the increasing reliance on student debt. Lower income makes repayment of loans more difficult, even though borrowers’ debt burdens may be lower. Popular press stories abound with anecdotes about borrowers with six-figure debts. While these borrowers do merit concern, they only represent approximately four percent of borrowers. (See Dai 2013.) It might be fruitful for policymakers to also focus on borrowers with seemingly manageable levels of debt who might still have problems making payments.
- Census tracts are geographical areas that usually contain around 4,000 people. 
- A single year (2009) was used for several reasons. First, there was a revision to the tracts in 2010, making geographical matching based on later tract definitions difficult. Second, using one year allows for the same tracts to be tracked over time. Third, ACS estimates are not available prior to 2009, making earlier income estimation difficult. 
- There is evidence that the delinquency rate is underestimated because many students may enter deferral or forbearance. (See Brown et al 2012.) 
- This result becomes a bit murkier when partitioning on age. No clear trend emerges across MSAs with respect to age. 
- Brown, Meta; Haughwout, Andrew; Lee, Donghoon; Mabutas, Maricar; and van der Klaauw, Wilbert. (2012). “Grading Student Loans.” Liberty Street Economics Blog, March 5.
- Dai, Emily. (2013). “Student Loan Delinquencies Surge.” Inside the Vault, Spring.
- Deming, David J.; Goldin, Claudia; and Katz, Lawrence F. (2011). “The For-Profit Postsecondary School Sector: Nimble Critters or Agile Predators?” NBER Working Paper 17710, December.
- Institute for Higher Education Policy. (2011). “Initial College Attendance of Low-Income Young Adults.” Portraits, June.
- Wolla, Scott A. (2014). “The Rising Cost of College: Tuition, Financial Aid, and Price Discrimination.” Page One Economics Newsletter, January.