For months, I've mined longitudinal data to assess the financial health of US higher ed, focusing intensively on public and private not-for-profit, four-year institutions. Institutions are assessed with several dozen measures of financial and operational performance, benchmarked against peers, and evaluated against predictive patterns that the analytical engine finds in the data, learns from the next report and from trend data that precede an institution's experience of financial duress. Results continue to evolve as new institutions (e.g., community colleges) and measures are added into the mix. Still, it is time to start sharing – a process I am pleased to begin today with an overview of the industry’s risk profile as seen in the last available IPEDS data for a subset of public and private four-year institutions. The overview - shows how risk is distributed across industry segments (the percent of institutions within that segment that are considered at low, moderate, -explains distinctive risk patterns of individual segments, and - identifies patterns to watch for. I want to be careful and not overplay my hand. Measuring risk is an imprecise exercise, the results of which reflect the underlying methodology, and the assumptions used in its construction (and yes, a methodological treatise is forthcoming). The point of it - at least in this exercise - is to reveal how risk is distributed across institutions, how it varies from one institution (or group of institutions) to the next, and, going deeper, to characterize its architectures and locate predictors. This research is more than a labor of love and academic interest. It is tightly aligned with – indeed grows out of – my own experience, sadly one that seems to be repeated too often, of institutions waiting to address their financial challenges until they become acute. At that point, the institution has vastly less room for maneuver. It is often forced to take drastic actions that negatively impact students, faculty, staff, and communities that often depend so heavily on their local university or college. The work is borne out of a passion to find predictive measures or patterns that enable higher education leaders – boards, executive officers, senate faculty leadership, civic leaders, and policymakers – to intervene and course correct upstream, frankly, before it is too late. Finally, the data do not capture risks resulting from recent changes in the federal compact with US higher education. Those risks are real. For research and doctoral segments that have shown so much resiliency till now, they may be profound. They will also land on an industry that is, as the data reveal, already significantly challenged.
Jill Craven
6mo
It looks like the second sentence of the exec summary is missing a part. The “analytical engine is designed to -from”? Can you clarify? Thanks.
Chris Broughton
6mo
Thanks Daniel Greenstein for sharing this thoughtful analysis with the field. Really interesting to see how you approached the analysis and the signals that emerged from it.
This is fascinating and I'd love to learn more! Good we are talking next week - I have a few ideas!