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.
Say that there is a labor market and that there is no income tax. If an income tax is introduced, then what should we expect to happen? Specifically, what will happen to employment, the size of the labor force, and the number of people unemployed? Will each rise? Fall? Remain unchanged? Change ambiguously? Take a moment and jot down a note to test yourself.
As it turns out, what your answer is depends on what your model of the labor market is. Graphically, they are all quantities of labor. The size of the labor force is the quantity of labor supplied contingent on some wage that workers receive. It’s the number of people who are willing to work. Employment is the quantity of laborers demanded by firms contingent on to wage that they pay. Finally, the quantity of people unemployed is the difference between the size of the labor force and the quantity of workers employed (Assuming that the labor force is greater than or equal to employment).
I was writing up something for my graduating seniors about how to keep learning economics after school, and realized I might as well share it with everyone. This may not be the best way to do things, it is simply what I do, and I think it works reasonably well.
Blogs by Economists: There are many good ones, but besides ours Marginal Revolution is the only one where I aim to read every post
Podcasts on the Economy: NPR’s The Indicator (short, makes abstract concepts concrete), Bloomberg’s Odd Lots (deeper dives on subjects that move financial markets)
Podcasts by Economists: Conversations with Tyler and Econtalk (note that both often cover topics well outside of economics). Macro Musings goes the other way and stays super focused on monetary policy.
Twitter/X: This is a double-edged sword, or perhaps even a ring of power that grants the wearer great abilities even as it corrupts them. The fastest way to get informed or misinformed and angry, depending on who you follow and how you process information. Following the people I do gives you a fighting chance, but even this no guarantee; even assuming you totally trust my judgement, sometimes I follow people because they are a great source on one issue, even though I think they are wrong on lots of other things. Still, by revealed preference, I spend more time reading here than other single source.
Finance/Investing: Making this its own category because it isn’t exactly economics. Matt Levine has a column that somehow makes finance consistently interesting and often funny; unlike the rest of Bloomberg, you can subscribe for free. He also now has a podcast. If you’d like to run money yourself some day, try Meb Faber’s podcast. If you’d like things that touch on finance and economics but with more of a grounding in real-world business, try the Invest Like the Best podcast or The Diff newsletter.
Economics Papers: You can get a weekly e-mail of the new papers in each field you like from NBER. But most econ papers these days are tough to read even for someone with an undergrad econ degree (often even for PhDs). The big exception is the Journal of Economic Perspectives, which puts in a big effort to make its papers actually readable.
Books: This would have to be its own post, as there are too many specific ones to recommend, and I don’t know that I have any general principle of how to choose.
This is a lot and it would be crazy to just read all the same things I do, but I hope you will look into the things you haven’t heard of, and perhaps find one or two you think are worth sticking with. Also happy to hear your suggestions of what I’m missing.
Last weekend brought me back to Temple University, ten years after graduating, for a conference of econ PhD alums. I had so many reactions:
Mixing a research conference with what is effectively a reunion or homecoming is a great idea for a PhD program, and more schools should do it. It brought together alumni from all different years, but it especially felt like a reunion to me since it’s been ten years since I graduated (not that I really know about reunions; I’ve never been to a high school or college one).
Philadelphia in general and Temple University in particular have gotten much nicer (though still gritty). Some of this I expected; the country is getting steadily richer, and it seems like every college is always on a building spree. But as with New Orleans, it is a city still well below its peak population that I first got to know in the aftermath of the great recession. Unemployment in Philly is now well under half what it was the whole time I lived there, and it shows.
Life is short. I was saddened, but not shocked, to hear that one of my professors had died. I was saddened and shocked to hear that one of my fellow students had.
As a kid, whenever I went back to one of my old schools, I usually felt nostalgia mixed with the feeling that everything seemed small. Then I thought this smallness was only about me having grown taller, but now I wonder. At Temple the economics department has changed buildings, but when I went back to the old building everything seemed small, despite me being the same size I was in grad school. But at the time the building loomed so large in my mind; I was so focused on the things that happened there, the classes and tests, the study sessions and writing in the computer lab, what the professors thought, and everything that it all represented. All that apparently made the rooms seem physically larger in a way they now don’t once I have graduated and the professors moved.
Temple PhDs are much more successful than I would have guessed at the time. It was hard for students attending what was then a bottom-ranked program during the Great Recession to be optimistic about our job prospects, especially when we worried we might fail out of the program (a valid concern when, afaik, only 4 of the 11 students in my year finished their PhDs). But things turned out great; just in the past 10 years from a small program there are many people who are tenured or tenure track at decent schools, who have research or important supervisory positions at the Fed, or who are making a name for themselves in the private sector (like Adam Ozimek).
Why have we so exceeded our low expectations? The improving economy helped. Economics PhDs from anywhere turned out to be a valuable degree. Perhaps our training was stronger than we gave it credit for at the time. I see two main tracks for success coming out of a lower-ranked program, where the school’s name alone might not open doors:
find some way to get your foot in the door of a major institution like the Fed system or a major bank, then work your way up. The initial way in could be something less competitive, like an internship or a job you don’t necessarily need a PhD for. But once you are in you will be judged mostly on your performance within the institution, not your credentials. In a panel on non-academic jobs, several alums emphasized that conditional on having enough technical skills to get hired, at the margin people/communication skills are much more important to advancement than further technical skills.
Temple’s economics PhD program paused admissions back in 2020, but is aiming to restart with a redesigned program in 2025.
I teach one hour-forty minute classes on Tuesdays and Thursdays. And I allot only sixty minutes for exams. While student enjoy having the unexpected spare time after an exam, that’s a lot of learning time to miss. Therefore, after my midterms, we do an in-class activity that is a low-stakes, competitive game (and, entirely voluntary).
I call this game “The Extent of the Market” and it has three lessons. Here’s how the game works:
I have a paper handout, a big bag of variety candy, and a URL. The handout is pictured below-left and lists the types of candy. Each student rates their preference with zero being the least preferred candy. Whether they keep their preferences a secret is up to them. Next, I distribute two pieces of candy to each of them. Importantly, their candy endowment is random and they don’t get to choose or trade (yet). Finally, the URL takes them to a Google sheet pictured below-right where they can choose an id and enter there ‘value score’ under Round 0 by summing the candy ratings of their endowment.
Round 1 is where they get to make choices. I tell students that their goal is to maximize their score and that there is a prize at the end. They are now permitted to trade with anyone at their table or in their row. It doesn’t take long since their candy preferences compose of only the short list, their endowments are small, and the group of potential trade partners is small. When trading is finished, they enter there new scores under round 1.
Lesson #1: Voluntary trade makes people better off.
For each transaction that occurred, someone’s score increased. And in most cases two people’s scores increased. Not everyone will have traded and not everyone will have a higher score. But no one will have a lower score, given the rules and objective of the game. Importantly, the total amount and variety of candy in the little classroom economy hasn’t changed. But the sum of the values in Round 1 increased from Round 0. Trade helps allocate resources where they provide the most value, even if the total amount of physical stuff remains fixed. If it’s a microeconomics class, then this is where you mention Pareto improvements.
Round 2 follows the same process, but this time they may trade with anyone in their quadrant or section of the room. After trading concludes, they enter their scores at the URL under round 2.
Lesson #2: More potential trade partners increases the potential gains from trade.
Again, the variety and total amount of candy in the room remains constant. The only thing that increased was the size of the group of people with whom students could trade. And, they again earn higher scores or, at least, scores that are no lower. People have diverse resources and diverse preferences, and the more of them that you can trade with, the more opportunities to find complementary gains. Clearly, this means that increasing the size of the pool of trading partners is beneficial. One among the many reasons that the USA has had great economic success is that we are a large country geographically with diverse resources and a population of diverse preferences. This means that we have a large common market with many opportunities for mutually beneficial trade. The bigger that we make that common market, the better. Clearly, the implications run afoul of buy-local and protectionist inclinations.
Round 3 proceeds identically with students able to trade with anyone in the room and they enter their scores. At this time the game is finished. It’s important to identify the cumulative class scores across time and to reemphasize lessons #1 & #2. Often, the cumulative value-score will have doubled from Round 0, despite the fixed recourses, making no one worse off. If trading with a row, and then a section, and then the whole class results in gains, then there is an analogy to be drawn to a state, country, and the globe.
Lesson #3: Trade changes the distribution of resources.
Despite an initial distribution of resources, voluntary trade changed that distribution. While no one is worse off and plenty of students are better off, measured inequality may have been affected. Regardless, once a voluntary trade occurs, the distribution of candy and of scores changes. This has implications for redistributive policies. If income or wealth is redistributed in order to achieve some ideal distribution, then the ability to freely trade alters that distribution. The only way to achieve it again would be for another intervention to change the candy distribution by force or threat thereof. Consider that sports superstar Lebron James became rich by playing basketball for people who like to watch him. If we redistribute his income, and then permit him the freedom to voluntarily play basketball again, then the income distribution will change as he again trades and increases his income. Similarly, giving money to a low marginal product worker can provide some short-term relief. But, if the worker resumes their prior behavior and productivity, then the same determinants and resulting income persist.
It’s a fund game and students enjoy it. There are some important limitations. #1: There is no production in this game nor incentives for production. This is a feature for the fixed resources aspect of the game. But this is a bug insofar as students think about US jobs vs international jobs. I can assert that the supply side works similarly to the demand side, but students see it less clearly (it helps to draw these parallels throughout the semester). #2: While there is a maximum possible score in the game, the value created in reality is unbounded. There is no highest possible score IRL. #3: There are no feedback dynamics. Taxes associated with income redistribution cause workers to require higher pay, worsening pre-tax inequality. People respond to incentives, and the tax/subsidy component that determined the initial distribution of candy is absent.
It’s a fun game. If you try it, then please let me know how it goes or leave suggestions in the comments.
*By default, Google Sheets anonymizes users. You could have them sign in or use an institutional cloud drive to remove problems that might be associated anonymity.
**If your student can’t handle choosing their own id, then you can just list your students.
***Ideally, each increased trade-group is a superset of the prior round’s potential trading partners.
****You can do more than 3 rounds, but the principle doesn’t change
*****More trade will occur with more students, a greater variety of possible candies, and with more candies endowed per person. You can alter these as needed depending on the classroom limitations.
Elementary school kids can miss a day of school. If they are doing something wholesome and constructive on their day off, no one would claim that it hurts the child who is doing the alternate activity.
Does it hurt other people? There is an ungated section of this Matt Yglesias post concluding that when rich people pull their kids out of school it “… ultimately harms less-privileged children.” For now, assume that is true. We could internalize the externality, like surge pricing on toll roads. Let parents pay a fine to take their kids out of school. The fine would fund programs that help everyone. Let parents pay back into the public good. Charge $25/day which could go toward buying classroom supplies for the inconvenienced teacher.
This flexibility might lead to richer families keeping their kids in conventional schools, which seems like a good thing. No one would have to pay the fine. There is and would still be a system for excusing absences due to unavoidable things like surgery.
Requiring a doctor’s note for excused absences is already a tax. Requiring a parent to miss half a day of work to go take a child to the doctor is more punishing than paying a $25 fine, for many families.
The fine could even increase with the number of missed days. Only super rich families would be able to afford to take 2 children on a 3-week trip. I wouldn’t be able to afford it. But I wouldn’t mind if our school generated revenue off of those who can. Those people would probably donate a new playground in exchange for a plaque.
Is another example where it would be reasonable to charge people to not use something? In a way, insurance companies try to fine people for not using the gym. Running with this example, paid private schools could easily call this a tuition reimbursement for high attendance. Unfortunately, I think it would be politically impossible to implement in public schools.
The government is unique among economic institutions insofar as it can use coercion legally. But not all activities are coercive. Clearly, taxation is overwhelmingly coercive. Some people say that they are happy to pay taxes, but the voluntary gifts to the US Treasury are itsy-bitsy (just over $1m for FY 2023). Most regulations also include the threat of fines or jail time for non-compliance.
But once the government has the money in their coffers, there is plenty that they can do consensually. Once they have the resources, they are often just another potential transactor in the markets for goods and services. While the government can transact as well as anyone else, there is a fundamental theoretical difference for how we should interpret those transactions. Specifically, there is a principal-agent problem such that we can’t quite identify the welfare that is enjoyed by consumers when the government makes purchases. We really have very little idea.
Garett Jones uses the analogy of the government confiscating potatoes. The worst use would be for the government to throw the valuable resources into the river. Those resources help no one. Improved welfare would be yielded if the government just transferred those potatoes back to people. Sure, there’s the transaction cost of administration, but people get their potatoes back. Finally, the great hope is that the government takes the potatoes and makes tasty potato fritas such that they return to the public something more valuable than they took. These might be things that fall into the public goods category or solving collective action problems generally.
The above examples illustrates that how the government spends matters a lot for the welfare implications of the newly purchased government resources. But, we need to recall that there is an entire private segment of the market that is affected by the government transactions.
Short-Run Analysis
In a competitive market, firms face increasing marginal costs and make decisions about their levels of output. When the government makes purchases, it’s simply acting as another demander. How does the entry of a larger demander affect everyone else in the market? See the below GIF.
Economist Writing Every Day 
