You may have seen on your social media recently that the price of TVs has fallen 98% since 2020. That’s certainly what the data from the BLS says. This would seem to imply that a one-thousand dollar TV in the year 2000 would now be priced at $20. While we have seen amazing things in the market for TVs, we’re not seeing $20 TVs. One take away might be that the data is just wrong. But that data is always wrong. The question is how the data is wrong and whether it’s a problem.
The reason for the disagreement between the data and the price on the shelves is due to something called ‘Hedonic Adjustment’. The idea is that some goods have quality features that change over time, even if the price doesn’t change so much. In the case of TVs, we might see higher resolution, flatter screens, larger screen sizes, smart features, etc. TVs are not a stable set of qualities. They are a bundle of characteristics, and those characteristics have some wiggle room while still satisfying some sensible criteria for being a TV. In theory, every single good is a bundle of services that we value. The reason that the some CPI categories have fallen so much is not only because the price has fallen necessarily. Rather, the amount of services that we get from a TV has increased so that each dollar that we spend can purchase more of those TV features.
I’m told that Professor Erwin Blackstone died earlier this year, but I haven’t been able to find anything like an obituary online; consider this a personal memorial.
I knew Dr. Blackstone first as the professor of my Industrial Organization class at Temple University, where he taught since 1976. He was a model of how to take students seriously and treat them respectfully; he always called on us as “Mr./Ms. Last Name” and thought carefully about our questions.
Of course I learned all sorts of particular things about IO, especially US antitrust law and history- from Judge Learned Hand and baseball’s antitrust exemption to current merger guidelines and cases. I would later ask Dr. Blackstone to join my thesis committee, where he would heavily mark up my papers with comments and critiques.
He was a key part of how I was able to become a health economist despite the fact that Temple lacked a true health economist on the tenure-track economics faculty while I was there (as opposed to IO or labor economists who did some health). Blackstone’s coauthor Joseph Fuhr– a true health economist who also had Blackstone on the committee of his 1980 dissertation- came part-time to teach graduate health economics. Blackstone and Fuhr worked together to write the health economics field exam I took.
Finally, I learned from Blackstone by reading his papers. While he wrote many on health economics, my personal favorite was his work with Andrew Buck and Simon Hakim on foster care and adoption. It convincingly demonstrated the problems of having one fixed price in an area that most people don’t think about as a “price” at all- adoption fees. Having one fairly high fee for all children means the few seen as most desirable by adopting parents (typically younger, whiter, healthier) get adopted quickly, while those seen as less desirable by would-be adoptive parents linger in foster care for years. Like much of his work, it pairs a simple economic insight with a rich explanation of the relevant institutional details.
Academics hope to live on through our work- through our writing and the people we taught. Having taught many thousands of students at Cornell, Dartmouth, and Temple over 55 years, served on dozens of dissertation committees, and published over 50 papers and several books, I expect that it will be a long, long time before Erwin Blackstone is forgotten.
Source: Academic Tree. Charles Franklin Dunbar founded the Quarterly Journal of Economics in 1886.
Small, rural, private schools stand out to me as the most likely to show up on lists of closed colleges. This summer I discussed a 2020 paper by Robert Kelchen that identified additional predictors using traditional regression:
sharp declines in enrollment and total revenue, that were reasonably strong predictors of closure. Poor performances on federal accountability measures, such as the cohort default rate, financial responsibility metric, and being placed on the most stringent level of Heightened Cash Monitoring
Kelchen just released a Philly Fed working paper (joint with Dubravka Ritter and Doug Webber) that uses machine learning and new data sources to identify more predictors of college closures:
The current monitoring solution to predicting the financial distress and closure of institutions — at least at the federal level — is to provide straightforward and intuitive financial performance metrics that are correlated with closure. These federal performance metrics represent helpful but suboptimal measures for purposes of predicting closures for two reasons: data availability and predictive accuracy. We document a high degree of missing data among colleges that eventually close, show that this is a key impediment to identifying institutions at risk of closure, and also show how modern machine learning algorithms can provide a concrete solution to this problem.
The paper also provides a great overview of the state of higher ed. The sector is currently quite large:
The American postsecondary education system today consists of approximately 6,000 colleges and universities that receive federal financial aid under Title IV of the federal Higher Education Act…. American higher education directly produces approximately $700 billion in expenditures, enrolls nearly 25 million students, and has approximately 3 million employees
Falling demand from the demographic cliff is causing prices to fall, in addition to closures:
Between the early 1970s and mid-2010s, listed real tuition and fee rates more than tripled at public and private nonprofit colleges, as strong demand for higher education allowed colleges to continue increasing their prices. But since 2018, tuition increases have consistently been below the rate of inflation
Most college revenue comes from tuition or from state support of public schools; gifts and grants are highly concentrated:
Research funding is distributed across a larger group of institutions, although the vast majority of dollars flows to the 146 institutions that are designated as Research I universities in the Carnegie classifications…. Just 136 colleges or university systems in the United States had endowments of more than $1 billion in fiscal year 2023, but they account for more than 80 percent of all endowment assets in American higher education. Going further, five institutions held 25 percent of all endowment assets, and 25 institutions held half of all assets
Now lets get to closures. As I thought, size matters:
most institutions that close are somewhat smaller than average, with the median closed school enrolling a student body of about 1,389 full-time equivalent students several years prior to closure
As does being private, especially private for-profit (states won’t bail you out when you lose money):
As do trends:
variables measuring ratios of financial metrics and those measuring changes in covariates are generally more important than those measuring the level of those covariates
When they throw hundreds of variables into a machine learning model, it can predict most closures with relatively few false positives, though no one variable stands out much (FRC is Financial Responsibility Composite):
My impression is that the easiest red flag to check for regular people who don’t want to dig into financials is “is total enrollment under 2000 and falling at a private school”.
They predict that the coming Demographic Cliff (the falling number of new 18-year-olds each year) will lead to many more closures, though nothing like the “half of all colleges” you sometimes hear:
The full paper is available ungated here. I’ll close by reiterating my advice from the last post: would-be students, staff, and faculty should do some basic research to protect themselves as they consider enrolling or accepting a job at a college. College employees would also do well to save money and keep their resumes ready; some of these closures are so sudden that employees find out they are out of a job effective immediately and no paycheck is coming next month.
Rosenberg: What impact do you foresee in your field due to the increasing sophistication of AI, and what kind of skills do you think your students will need to be successful?
Buchanan: AI will reshape economic analysis and modeling, making complex data processing and predictive analytics more accessible. This will lead to more sophisticated economic forecasting and policy design. Economists will become more productive, and expectations will rise accordingly. While some fields might resist change, economics will be at the forefront of AI integration.
For students aiming to succeed, it’s crucial to embrace AI tools without relying on them excessively during college. Strong fundamentals in economic theory and critical thinking remain essential, coupled with data science and programming skills.
Interdisciplinary knowledge, especially in tech and social sciences, will be valuable. Adaptability and lifelong learning are key in this evolving field. Human skills like creativity, communication, and ethical reasoning will remain crucial.
While AI will alter economics, it will also present opportunities for those who can adapt and effectively combine economic thinking with technological proficiency.
Public choice economists emphasize the process by which we select political leaders. Electoral and voting rules influence the type of leaders we get. Institutional economists agree and go one step further. Who we choose matters less than the environment we place them in. Leaders, regardless of their personal qualities, respond to the incentives that surround them. The ultimate policies, therefore, largely conform to those incentives. From this perspective, it’s important to adopt institutional incentives for leaders to promote policies oriented toward economic growth and provide the option to flourish.
The same principle applies to the private economy. Productivity is crucial, and higher IQ often correlates with greater productivity. Yet, genetic endowment—including IQ—is beyond individual control. Many other determinants of productivity are not exogenous when we can affect policy. Let’s adopt policies that allow individuals with lower IQ to act productively as if they had higher IQ. Protecting the freedom to contract and private property rights creates conditions whereby even those at the lower end of the cognitive ability distribution can thrive. These principles expand their opportunities. Market signals give them valuable feedback on their activities and enable them to contribute to the economy.
When MOOCs (Massive Open Online Courses) burst onto the education scene in the early 2010s, they were hailed as the future of learning. With the promise of democratizing education by providing free access to world-class courses from top universities.
Leading universities rushed to put their courses online, venture capital poured in, and platforms like Coursera and edX grew rapidly. Yet today, while MOOCs still exist, they’ve largely retreated to the margins of education. Meanwhile, long-form podcasts have emerged as a surprisingly powerful force in American intellectual life.
Is this ironic? I wanted to learn a bit about MOOCs while I took a walk before writing this blog post. I typed “MOOCs” into the Apple Podcasts search bar.
I learned about MOOCs from Russ Roberts at a reasonable pace (when I listen to podcasts, I do it at 1x speed but I’m almost always doing something like driving or folding laundry).
I consider myself a lifelong learner. I buy and read books. Like hundreds of millions of people around the world, I like podcasts. I will attend lectures sometimes, especially if I personally know someone in the room. I did sit in classrooms for course credit throughout college and graduate school. I took extra classes that I did not need to graduate purely out of interest, and yet I have never once been tempted to sign up for a MOOC.
Enough introspection from me. My viral “tweet” this week was: “MOOCs never took off, as far as I can tell, and yet long-form podcasts are shaping the nation.”
Did MOOCs fail? Many millions of people signed up for MOOCs. A much smaller percentage of people completed MOOCs. Some users find MOOCs worth paying for.
However, if you listen to the podcast with John Cochrane in 2014, you can see the promise that MOOCs failed to live up to. The idea was that many people who did not have access to a “top quality” education would get one through MOOCs. Turns out that access is not the bottleneck.
I am one of several founders of a club with the abbreviation F.E.W. for Finance and Economics Women. This is a student organization that we have at Samford and that Dr. Darwyyn Deyo runs at San Jose State University.
Our short paper is mostly a how-to guide including a draft of a club charter document. We describe our institutions and how we use this group to engage and encourage students. Please read it for more details on how to start a club.
Like most student groups, the FEW model relies on student leaders who take initiative. Having done this for more than 6 years, we have a growing network of alumni and local business partners who connect to current students through FEW events. Personally, I am lucky that 3 faculty members total support the club at my school.
Women are often minorities in upper-division econ and finance classes. Women also have some unique challenges when it comes to choosing career paths and navigating the workplace. These events (e.g. bringing in a manager from a local bank to talk with student over lunch) allow a space for students to ask questions they might not normally ask in a classroom setting or in a standard networking environment.
We report the results of a small survey in our paper. We can’t infer causality, nor did we run any experiments. However, we did find that women were more likely to report that a role model in their chosen profession influenced their choice of major. Part of the purpose of the FEW model is to expose students to a variety of role models who they might not otherwise connect with.
Here’s a news article with a picture of the founding group at Samford. I have great appreciation and respect for our student leaders who keep it going, and I am grateful to the graduates who stay in contact with us.
If you didn’t know already, the past five years has been a whirl-wind of new methods in the staggered Differences-in-differences (DID) literature – a popular method to try to tease out causal effects statistically. This post restates practical advice from Jonathan Roth.
The prior standard was to use Two-Way-Fixed-Effects (TWFE). This controlled for a lot of unobserved variation over individuals or groups and time. The fancier TWFE methods were interacted with the time relative to treatment. That allowed event studies and dynamic effects.
“Why do our students (even the ones paying a jillion dollars!) *want* to skip their lessons?”
“You give us work fit for machines. You want rote answers.”
He asks why students want to cheat and what is wrong with education. Why did this tweet take off? This is obvious.
I’m not of the opinion that education is entirely signaling (see Bryan Caplan). However, anyone can see that education is partly signaling. It’s difficult to get good grades. Good grades is a noisy signal of excellence. Students want to cheat so that they can obtain the good grades and signal to employers that they are excellent. There is nothing mysterious about that.
Part of a professor’s job is to make it hard to cheat and costly if you are caught.
Now we get to the “rote answers” part. How is a professor who has over 100 students every semester supposed to monitor the students’ performance and make it hard to cheat and be fair to every student? The “rote answers” part is a technology called the multiple-choice test with auto or semi-auto (e.g. Scantron machine) grading. Multiple choice tests serve an important role in our society, and they aren’t going anywhere.
A professor who has only 10 students per semester could give personalized assignments and grade oral exams and be an Oxford tutor for the students hand-written essays or whatnot. However, that kind of education would be extremely expensive/exclusive and does not scale.
Readers are more scarce than writers. AI’s can read now. The implications that will have for education and assessment have yet to be seen.
This week the University of the Arts in Philadelphia announced they were closing effective immediately, leaving students scrambling to transfer and faculty desperate for jobs. U Arts now joins Cabrini University and Birmingham-Southern as some the 20 US colleges closing or being forced to merge so far this year. This trend of closures is likely to accelerate given falling birth rates that mean the number of college-age Americans is set to decline for decades; short-term issues like the FAFSA snafu and rising interest rates aren’t helping either.
All this makes it more important for potential students and employees to consider the financial health of colleges they might join, lest they find themselves in a UArts type situation. But how do you predict which colleges are at significant risk of closing? One thing that jumps out from this year’s list of closures is that essentially every one is a very small (fewer than 2000 undergrad) private school. Rural schools seem especially vulnerable, though this year has also seen plenty of closures in major cities.
There appear to be a number of sourcestracking the financial health of colleges, though most are not kept up to date well. Forbes seems to be the best, with 2023 ratings here; UArts, Cabrini, and Birmingham-Southern all had “C” grades. If you have access to them, credit ratings would also be good to check out; Fitch offers a generally negative take on higher ed here.
In a 2020 Brookings paper, Robert Kelchen identified several statistically significant predictors of college closures:
I used publicly available data compiled by the federal government to examine factors associated with college closures within the following two to four years. I found several factors, such as sharp declines in enrollment and total revenue, that were reasonably strong predictors of closure. Poor performances on federal accountability measures, such as the cohort default rate, financial responsibility metric, and being placed on the most stringent level of Heightened Cash Monitoring, were frequently associated with a higher likelihood of closure. My resulting models were generally able to place a majority of colleges that closed into a high-risk category
The Higher Learning Commission reached similar conclusions. Of course, there is a danger in identifying at-risk colleges too publicly:
Since a majority of colleges identified of being at the highest risk of closure remained open even four years later, there are practical and ethical concerns with using these results in the policy process. The greatest concern is that these results become a self-fulfilling prophecy— being identified as at risk of closure could hasten a struggling college’s demise.
Still, would-be students, staff and faculty should do some basic research to protect themselves as they considering enrolling or accepting a job at a college. College employees would also do well to save money and keep their resumes ready; some of these closures are so sudden that employees find out they are out of a job effective immediately and no paycheck is coming next month.
Economist Writing Every Day 
