US College Scorecard

An extensive survey of American colleges and universities, recording the demographics of their graduates, typical salaries, and much more.

Motivation

The United States Department of Education maintains a College Scorecard website, tracking thousands of colleges and universities across the US, their demographics, graduation rates, tuition costs, student loans, and much more, including average salaries after graduation. The data is presented on a handy website with search features, maps, comparison charts and graphs, and various other tools so students choosing between colleges can make an informed choice.

This dataset is a portion of the complete College Scorecard dataset, giving information about institutions, their costs, the federal financial aid received by students, and the debt those students have after college. Note that the financial data only includes students who receive federal grants or loans, because they’re the only students the government has full information for.

Data

The dataset includes about 6,500 rows, each row representing an American college or university. There are over 3,000 columns.

There are a lot of variables, so the data dictionary is provided as a separate CSV. The dictionary lists every column and its meaning. For columns that can be factors, it gives the complete meaning of every factor level.

Data preview

Because the dataset is so large, this preview shows only the dictionary describing the variables. The main dataset is linked to above, under “Data Files”.

college-scorecard-dictionary.csv

Variable descriptions

The dictionary contains the full list of variables. This is an extract of a few useful variables.

Variable	Description
INSTNM	Institution name
STABBR	Abbreviated name of the state the institution is in
PREDDEG	Predominant type of degree awarded. 0: not classified, 1: certificates, 2: associate’s degrees, 3: bachelor’s degrees, 4: entirely graduate degrees.
CONTROL	1: public school, 2: private nonprofit school, 3: private for-profit school.
SATVRMID	Midpoint of SAT critical reading scores of students
SATMTMID	Midpoint of SAT math scores of students
SATWRMID	Midpoint of SAT writing scores of students
ACTCMMID	Midpoint of the ACT cumulative score of students
ACTENMID	Midpoint of ACT English scores
ACTMTMID	Midpoint of ACT math scores
ACTWRMID	Midpoint of ACT writing scores
COUNT_ED	Number of students whose earnings data is represented here
UGDS	Total enrollment of undergraduate students
NPT4_PUB	Average net price (in-state tuition, fees, and living expenses, minus all financial aid) for public schools
NPT4_PRIV	Average net price (tuition, fees, and living expenses, minus all financial aid) for private schools
PCTPELL	Fraction of all undergraduates who received a federal Pell grant for tuition
PCTFLOAN	Fraction of all undergraduates receiving a federal student loan
MD_EARN_WNE_P10	Median earnings (dollars) of students working and not enrolled, 10 years after entry
GRAD_DEPT_MDN_SUPP	Median debt of those who completed their degrees
GRAD_DEPT_MDN10YR_SUPP	Median debt of those who completed their degrees, in terms of the monthly payments they’d have to make to pay it off over 10 years
RPY_3YR_RT_SUPP	Fraction of students who are actively repaying their loans, of those not granted deferment (e.g. because they have no job or entered graduate school), after three years

Variables starting with PCIP record the fraction of degrees awarded in each of a broad range of academic fields, from education to engineering; consult the data dictionary for the full listing. There are also a number of additional variables breaking down information by ethnicity, such as the UGDS variables. You should explore the data file and the data dictionary for other variables you might find interesting to use in your analyses.

Questions

1. Do some EDA. What range of earnings do students have after graduation? What range of debts? Does higher debt tend to pay off with higher earnings? Is higher cost associated with better earnings after graduation? Explore these questions, and others that seem interesting in the data, with charts and graphs.
2. Build a model to predict earnings of students after graduation (MD_EARN_WNE_P10) using incoming test scores, the type of school (public or private), total enrollment, net costs, and debt load. Do more expensive schools seem to pay off — that is, result in higher earnings — when controlling for the scores of the entering students? Is this relationship the same between public and private schools?
3. Private for-profit schools have been widely criticized for delivering poor educations with high tuition rates, leading students to frequently default on their debts. Are repayment rates (RPY_3YR_RT_SUPP) significantly different between public, private non-profit, and private for-profit schools? What about median earnings?
4. Devise a single “score”, which you can calculate using the data available, which measures the “cost-effectiveness” of each school, in terms of the earnings graduates get, adjusted for their incoming test scores, versus the price, debt rates, and financial aid amounts. You can calculate this score in any way that seems reasonable to you. Test it out by ranking the top ten or twenty most cost-effective schools, and see if the results seem reasonable.

References

College Scorecard Data, from the United States Department of Education. This is the April 25, 2023 version.