The College Scorecard is an effort
to increase transparency in post-secondary education, measuring how
well individual schools perform at preparing students to be successful.
The data is provided to help students and families compare college costs and outcomes, arming them to make informed decisions about the future and goals.
The data in this project is not actually surveyed by the project itself, but is instead drawn from a number of different data collections from other federal entities. The Scorecard combines the data from the following federal collections:
Students are grouped into cohorts based on their entry year into higher education using the date they are first shown to be receiving financial aid as well as their self-reported grade level on their FAFSA documentation. Students' earnings and debt metrics in the Scorecard are then based on their performance within these cohorts.
Because the Scorecard is simply a conglomeration of a few different datasets, the contents frequently overlap with our imported primary sources. At Ididio, our main use of the Scorecard is to evaluate outcome measures for students using Title IV funding, and these measures are originally from a combination of NSLDS and IRS data which we only access through the Scorecard. The NSLDS data contains information about where economically a student and their family lies prior to their education, while the IRS tax records show subsequent financial information for graduates. Earnings information is not available on a program level, so there is no differentiation between, for example, a student in a medical program and a student in a business program. We primarily value the data for its window into student debt repayment.
College Scorecard data are downloaded from the Department of Education. We last retrieved this data on November 21, 2019, which includes data for the 2017-18 academic year as well as the new program-level loan and starting earnings data.
In November 2019, College Scorecard released program-level loan and earnings data at the CIP-4 (see CIP Codes) level for most institutions. Ididio shares this data in two ways. First, we aggregate the program-level data to create a robust picture of debt and starting salaries for each program to help people broadly which choosing which degree or certificate to pursue. Second, we share the data directly in our program listings for each institution. In both cases, but especially in the latter case, there are issues with the way this data is collected that warrant a cautious interpretation of the values shared:
The loan and earnings data are created using student lists from the National Student Loan Data System (NSDL). That means that any earnings from students who did use federal loans are omitted from the reporting.
There are unreleased suppression rules that cause many institutions' data from being fully shared. In particular, there are many cases in which we have some loan or earnings data, but no information to allow us to determine the percentage of program graduates for whom the earnings or loan data applies. Adding to this lack of clarity regarding inclusion is the fact at a student who received federal loan aid as an undergraduate will be in the NSDL system and would then have data reported for any subsequent graduate work, even if further loans are not required.
While the institution-level earnings data from College Scorecard is based on all students with federal loans who attended, regardless of completion status, this program-level earnings data is based only on students who actually finished a degree. Therefore, a program at a particular school that has a high rate of non-completions could have earnings data that makes that school's program look appealing. Unfortunately, we have no data to track completion success at the program level.
This data provides the earnings for students who completed a degree in the 2014-15 or 2015-16 year, with earnings measured in 2016 or 2017, respectively. Students who do not work in the measurement year, regardless of the reason, are not included in the data. Therefore, programs that have a high unemployment rate could simultaneously reflect high income if the small percentage who are working have high-paying jobs. We have no data to suggest the unemployment rate by program and school. Additionally, this early data cannot suggest earnings potential over time.
The data is reported at the CIP-4 level which may combine several institution degree fields.