DC US Attorney Sends Query Letters to Academic Publishers about Research Practices

Given our current political environment this outreach by the US Attorney for DC has been widely anticipated by many in the academic publishing community. Unknown is how many similar letters have been sent (if any).  What this requests suggests is that, in the view of the DC court, publishers have been tipping the scales in the conduct and publication of research such that they have a bias contrary to the views and assumptions of the republican administration and/or those on the right generally.

On the surface, there isn't much illegitimate about these questions: they are valid; however, the purpose for asking them and the interpretation of the answers is where academic and association publishers may have legitimate concerns.

 

Federal Reserve Report on High Ed Institution Viability - It is Bleak.

There has been a decades long (at least) discussion about the viability of a certain segment of the higher education institution market and assumptions there would be a significant contraction in the number of schools. Now, things may have taken a turn for the worst. This report by the Federal Reserve (published before trumpocomics) anticipated an acceleration in the closure of 'at risk' schools. These are primary schools with small enrollments, a dependency on tuition for revenue and with limited or no endowment.

I used Copilot to create this summary:

1. Enrollment Declines and Financial Distress: The report highlights that enrollment declines, particularly due to the "demographic cliff" and the COVID-19 pandemic, are significant predictors of financial distress and college closures. ​ Enrollment fell by 15% from 2010 to 2021, and the trend may continue, exacerbating financial challenges for institutions. ​
2. Predictive Models for College Closures: The authors developed predictive models using machine learning techniques, which significantly outperform traditional linear probability models and existing federal accountability metrics. ​ The preferred model, using the XGBoost algorithm, showed an 84% accuracy rate in predicting closures within three years for the riskiest institutions. ​
3. Impact of Missing Data: A high degree of missing data among colleges that eventually close is a key impediment to identifying at-risk institutions. ​ Machine learning models, particularly XGBoost, are more effective at handling incomplete data and improving predictive accuracy compared to traditional methods. ​
4. Financial Metrics and Predictive Accuracy: Key financial metrics such as operating margin, debt levels, and changes in revenue and expenses are strong predictors of college closures. ​ The machine learning models demonstrated that ratios of financial metrics and changes in these metrics are more important than absolute levels for predicting closures.
5. Future Closure Predictions: Simulations based on the predictive models suggest that the demographic cliff could significantly increase annual college closures. ​ Under worst-case scenarios, there could be up to 80 additional closures annually, affecting over 100,000 students and 20,880 staff members, highlighting the potential widespread impact on local communities and economies.

According to Best Colleges, over 75 institutions have closed, merged or announced closure since the start of COVID and they estimate this had impacted almost 50,000 students.

From the report:

These financial pressures on higher education have elevated financial distress — up to and including closures — as a major higher education policy issue. While there have been predictions of a wave of closures for the last decade (e.g., Eide, 2018; Horn, 2018), most colleges survived the pandemic thanks to timely federal support and emergency actions taken to freeze or reduce personnel costs (Natow, 2021). However, the withdrawal of pandemic-era federal funding, along with existing stressors, likely resulted in an increase in closures during 2023 (Sanchez, 2024) and into 2024. There has also been a wave of colleges declaring financial exigency, eliminating academic programs and employees in an effort to cut costs and to avoid potential closures (Ambrose & Nietzel, 2024). Even flagship universities such as West Virginia University and Pennsylvania State University have pursued sizable reductions in the number of academic programs as they face budget deficits (Burke, 2024; Povich, 2023).

Unmentioned in this report is the negative impact of international students which typically pay much higher tuition fees. While international students may not be a target cohort for the schools in this 'at risk' group it is possible that an overall decrease in international students could expand this 'at risk' group (make it larger). Additionally, smaller schools often rent out their campus to foreign student programs for English and cultural programs during summer recess but it is entirely possible that these programs will dry up if they haven't already done so. The forecast may be bleak.