## Thursday, April 28, 2016

### Measuring Risk of too Much Debt in College

Measuring Risk of too Much Debt in College

Situation:   The official College Scorecard provides information on the median debt of student borrowers who finished college.   The median provides information on a typical person in the middle of the distribution.   It would be interesting to know what percent of student borrowers at a school borrowed more than a certain amount, perhaps \$40,000 or \$50,000.   The official web site of the college scorecard does not contain such information.

The college scorecard web site has a section with data files that contains additional information, which is not used in the on-line statistics page available to most users.   The link to the additional data is presented below.

A file called Most Recent Data has a variable called CUML_DEBT_P90.   This variable is the 90th percentile of debt for people. (I believe the debt percentile pertains to people who finish school but the documentation is not clear on this point.)

Question:  Consider 4-year public universities with more than 5,000 students.    (I found 324 such universities.) Compare the median debt levels and the 90th percentile of student borrowers at these universities.  Why is the 90th percentile a better measure of the ability of universities to control debt of their students than the median?

Analysis: Statistics on the median debt levels and the 90th percentile debt levels for student borrowers at 324 large 4-year public universities are presented below.

Statistics on the median reflect the debt accumulated by a typical student at a particular school.

Statistics on the 90th percentile reflect the experience of a student borrower who borrows more than 90% of all student borrowers at a school.

The typical or middle of the distribution experience at a school that has typical debt over 75 percent of schools is \$18,298.

The above 90% of debt experience at a school that has a 90th percentile greater than 75% of schools is \$37,309.

 Median Debt and 90th Percentile of Debt at 324 Public Universities Median Debt 90th Percentile of Debt n 324 324 Mean 15896 34478 Standard Error 177 274 LB 95% CI 15549 33939 UB 95% CI 16244 35017 Min 5500 13750 25th 14177 31049 75th 18298 37309 Max 24250 49400

Discussion:   Why should a prospective student be concerned about the 90th percentile of debt at a university, perhaps more so than the median debt level at the university?

The median debt does not consider risk.    The 90th percentile of debt incurred by borrowers at a school provides more information about what can go wrong because of risk.

Some students come from a household where a parent might be able to contribute more even if the aid package requires substantial debt.   Perhaps students from households with modest means should shun universities where a large number of people exit school with a lot of debt.   The 90th percentile is a better measure of a lot of debt than the median.

There are other sources of risk. Some schools do not maintain a full financial aid package over all four years.

In addition, many students take more than four years to complete their degree.  It would be useful to know whether some schools are better than other schools at getting students done on time.

In any event, I believe the 90th percentile of debt figures provide additional information on risk and that these 90th percentile debt figures should be provided in the main College Scorecard web site and not be relegated to an obscure file in the data section of the web page.

Notes:

Note One:  The statistics presented in the chart above are not weighted by the size of the institutions.   All institutions in this particular sample are public universities offering four-year degrees with at least 5,000 students.

Note Two:  The 90th percentile statistics by institution cannot be used to measure the number of students in the country with high debt levels.    Even weighting by number of students would not resolve this issue.  The 90th percentile of the total population of students can be disproportionately impacted by a relatively small number of bad institutions.  The table below illustrates that the mean of schools may be inconsistent with the 90th percentile for the entire populations

 Example Showing Weighted Average of 90th Percentile is not Equal to 90th Percentile of Total School One School Two Total Borrowers 5,000 10,000 15,000 Number of people with \$30,000 in Debt 2,000 500 2,500 Number of people with \$10,000 in Debt 3000 9500 12,500 90th Percentile \$30,000 \$10,000 \$30,000

Note Three:  The data files on the Department of Education web page are not easily analyzed.   Many of the variables in the file have alphabetic entries for some observations and these string variables are not easily read.  It would be highly useful if the Department of Education created a SAS Export file with data that was readable in SAS and other packages including STATA.

Readers of this post may want to learn more about President Obama's effort to rank colleges.   See the post below.

#### 1 comment:

1. I agree. The focus on the 50% quantile is convention. One can regress on any quantile, as long as there is sufficient data to support it.

This principle can be generalized to other contexts. For instance, it is probably much more appropriate to plan based upon the 90% quantile of sea level rise than it is upon the 50% quantile (or some approximation, like the mean) because those risks are what want to be minimized. There is probably a methodical way of picking the quantile point based upon relative losses.