Beware OPEIDs and Super OPEIDs

In higher education discussions, everyone wants to know how a particular college or university is performing across a range of metrics. For metrics such as graduation rates and enrollment levels, this isn’t a big problem. Each freestanding college (typically meaning that they have their own accreditation and institutional governance structure) has to report this information to the U.S. Department of Education’s Integrated Postsecondary Education Data System (IPEDS) each year. But other metrics are more challenging to use and interpret because they can cover multiple campuses—something I dig into in this post.

In the 2015-16 academic year, there were 7,409 individual colleges (excluding administrative offices) in the 50 states and Washington, DC that reported data to IPEDS and were uniquely identified by a UnitID number. A common mistake that analysts make is to assume that all federal higher education (or even all IPEDS) data metrics represent just one UnitID, but that is not always the case. Enter researchers’ longtime nemesis—the OPEID.

OPEIDs are assigned by the U.S. Department of Education’s Office of Postsecondary Education (OPE) to reflect each postsecondary institution that has a program participation agreement to participate in federal student aid programs. However, some colleges within a system of higher education share a program participation agreement, in which one parent institution has a number of child institutions for financial aid purposes.

Parent/child relationships can generally be identified using OPEID codes; parent institutions typically have OPEIDs ending with “00,” while child institutions typically have OPEIDs ending in another value. These reporting relationships are fairly prevalent, with there being approximately 5,744 parent and 1,665 child institutions in IPEDS in the 2015-16 academic year based on OPEID values. For-profit college chains typically report using parent/child relationships, while a number of public college and university systems also aggregate institutional data to the OPEID level. For example, Penn State and Rutgers have parent/child relationships while the University of Missouri and the University of Wisconsin do not.

In this case of a parent/child relationship, all data that come from the Office of Federal Student Aid or from the National Student Loan Data System are aggregated up across a number of colleges. This includes all data on student loan repayment rates, earnings, and debt from the College Scorecard as well as student loan default rates that are currently used for accountability purposes. Additionally, some colleges report finance data out at the OPEID level on a seemingly chaotic basis—which can only be discovered by combing through data to see if child institutions do not have values. For example, Penn State always reports at the parent level, while Rutgers has reported at the parent level and the child level on different occasions over the last 15 years. Ozan Jaquette and Edna Parra have pointed out in some great research that failing to address parent/child issues can result in estimates from IPEDS or Delta Cost Project data being inaccurate (although trend data are generally reasonable).

If UnitIDs and OPEIDs were not enough, the Equality of Opportunity Project (EOP) dataset added a new term—super-OPEIDs—to researchers’ jargon. This innovative dataset, compiled by economists Raj Chetty, John Friedman, and Nathaniel Hendren, uses federal income tax records to construct social mobility metrics for 2,461 institutions of higher education based on pre-college family income and post-college student income. (I used this dataset last month in a blog post looking at variations in marriage rates across four-year colleges.) However, the limitation of this approach is that the researchers have to rely on the names of the institutions on tax forms, which are sometimes aggregated beyond UnitIDs or OPEIDs. Hence, the super-OPEID.

The researchers helpfully included a flag for super-OPEIDs that combined multiple OPEIDs (the variable name is “multi” in the dataset, for those playing along at home). There are 96 super-OPEIDs that have this multiple-OPEID flag, including a number of states’ public university systems. The full list can be found in this spreadsheet, but I wanted to pull out some of the most interesting pairings. Here are a few:

–Arizona State And Northern Arizona University And University Of Arizona

–University Of Maryland System (Except University College) And Baltimore City Community College

–Minnesota State University System, Century And Various Other Minnesota Community Colleges

–SUNY Upstate Medical University And SUNY College Of Environment Science And Forestry

–Certain Colorado Community Colleges

To get an idea of how many colleges (as measured by UnitIDs) have their own super-OPEID, I examined the number of colleges that did not have a multiple-OPEID flag in the EOP data and did not have any child institutions based on their OPEID. This resulted in 2,143 colleges having their own UnitID, OPEID, and super-OPEID—meaning that all of their data across these sources is not combined with different institutions. (This number would likely be higher if all colleges were in the EOP data, but some institutions were either too new or too small to be included in the dataset.)

I want to close by noting the limitations of both the EOP and Federal Student Aid/College Scorecard data for analytic purposes, as well as highlighting the importance of the wonky terms UnitID, OPEID, and super-OPEID. Analysts should carefully note when data are being aggregated across separate UnitIDs (particularly when different types of colleges are being combined) and consider omitting colleges where aggregation may be a larger concern across OPEIDs or super-OPEIDs.

For example, earnings data from the College Scorecard would be fine for the University of Maryland-College Park (as the dataset just reflects those earnings), but social mobility data would include a number of other institutions. Users of these data sources should also describe their strategies in their methods discussions to an extent that would allow users to replicate their decisions.

Thanks to Sherman Dorn at Arizona State University for inspiring this blog post via Twitter.

How Acela Corridor Educational Norms Look to an Outsider

Education policy discussions in the United States tend to be dominated by people living in the Acela Corridor—the densely-populated, highly-educated, and high-income portion of the United States that is served by Amtrak’s fast train from Boston to Washington, DC. Since moving from the Midwest to New Jersey four years ago to start on the tenure track at Seton Hall, I have probably logged 50 trips to Washington via Amtrak’s slower (and less-expensive) Northeast Regional train. (It sure beats driving, and Amtrak’s quiet car is a delight!)

Many of the suburban communities in northern New Jersey have median household incomes of well over $100,000 per year, which is roughly the top 20% of American families. The top 20% is notable because that is the cutoff that the Brookings Institution’s Richard Reeves uses in his new book, Dream Hoarders, to highlight how upper-income individuals have taken steps to make sure their children have every opportunity possible—typically at the expense of other families. The sheer concentration of high-income families within much of the Acela Corridor has created a powerful set of social norms regarding education that can leave outsiders flabbergasted.

Yet in spite of having two parents with bachelor’s degrees, a PhD in education, and being one half of a two-income professional household, I find myself confused by a number of practices that are at least somewhat common in the Acela Corridor but not in other parts of the country. This was highlighted by a piece in Sunday’s New York Times on affirmative action. The reporter spoke with two students at private boarding schools in New Jersey, of which there are apparently a fair number. My first reaction, as a small-town Midwesterner, was a little different than what many of my peers would think.

Here are some other things that have surprised me in my interactions with higher-income families in the Acela Corridor:

  • K-12 school choice debates. Unlike some people in the education world, I don’t have any general philosophical objections to charter schools. But in order for school choice to work (barring online options), there needs to be a certain population density. This is fine in urban and suburban areas, but not so great in rural areas where one high school may draw from an entire county. A number of Republican senators from rural states have raised concerns about school choice as a solution for this reason.
  • SAT/ACT test preparation. I attended a small-town public high school with about 200 students in my graduating class. The focus there was to get students to take the ACT (the dominant test in America as a whole, with the coasts being the exception), while also encouraging students to take the PLAN and PSAT examinations. But I never saw a sign advertising ACT prep services, nor was I even aware that was I thing people do. (I took the practice ACT that came with the exam the night before the test—that was it.) In the Northeast, there seem to be more signs on the side of the road advertising test prep than any other product or service.
  • The college admissions process. Going to a four-year college is the expectation for higher-income families in the Acela Corridor, and families treat the college choice process as being incredibly important. Using private college counselors to help manage the process, which often includes applying to ten or more colleges, is not uncommon. A high percentage of students also leave the state for college, which is quite expensive. (In New Jersey, about 37% of high school graduates head to other states to attend college.) Meanwhile, in much of the country, the goal is to get students to attend college at all rather than to get students to attend a slightly more prestigious institution. I can think of just one of my high school classmates who went out of state, and a large percentage of the class did not attend college immediately after high school.
  • Private tutoring while in college. I supplemented my income in graduate school by tutoring students in economics, typically charging between $25 and $40 per hour to meet with one or two students to help them prepare for exams. (I paid for an engagement ring using tutoring income!) I was never aware of anyone paying for private tutoring when I was an undergraduate at Truman State University, but this was a common practice at the University of Wisconsin-Madison. Nearly all of these students came from the suburbs of New York City or Washington, DC and were used to receiving private tutoring throughout their education. I got very few tutoring requests from in-state students, but they were typically paying for their own college (and thus got a substantial discount from my normal rates).

I worry about education policy discussions being dominated by the Acela Corridor regulars because their experiences are so different than what how most Americans experience both K-12 and higher education. If education committee staffers, academic researchers, and think tankers all share similar backgrounds, the resulting policy decisions may not reflect the needs of rural and urban lower-income individuals. It is important to seek out people from other walks of life to make sure policies are best for all Americans.