Teaching

Race & Sex & College Major

Zachary BartschLeave a comment

There are some stories that we tell. These include that we have increasing non-white and non-male participation in many college majors. Here I examine the 25 most popular majors in the 2024 ACS data and I split it up into older and younger cohorts, delineated by age 40 (unweighted).

Race

Below-upper is the distribution of race by college major for people older than 40 years of age, or who mostly graduated more than 15 years ago.  It largely conforms to my biases. There were a higher proportion of Asian people in the natural sciences and engineering, but also in Economics. I didn’t really have strong priors about the proportions of black people, but they composed the highest proportions in Criminal Justice, Business, and Liberal arts. Contrary to my expectations, Elementary Education had the highest proportion of white people. Below-lower is the cohort that is age 40 and younger. The entire distribution is  similar, but less white.

Some of the differences between the two groups are hard to see, so I’ve included those below. What are some general takeaways? 1) There are lower proportions of white people among all degrees. 2) There are higher proportions of Asian people in every field except mechanical engineering, which is unchanged. 3) Whether black people compose a greater proportion depends on the major.

In the younger cohort, black people are now a greater share of bachelor’s in chemistry, Fine Arts, Physical Fitness, Biology, and Liberal Arts. But black people also now compose lower proportions of people with bachelor’s in business management, Criminal Justice, and Nursing.

Interestingly, the whitest college major, Elementary Education, has experienced the least change in racial diversity and has had a decline in the proportion of black degree holders. Economics ties with Sociology with for the 4th lowest proportion of white people among younger graduates at 61%. Most of the criticism in economics has not been due to racial imbalance, but rather due to sex imbalances.

Sex

Because sex has two categories in the census data, I can fit everything into the single graph below. It illustrates the sex split by major among people older than 40 vs the younger cohort. The distribution mostly conforms to my priors. The biggest surprises to me among the older cohort are that Finance and Math were as popular among females as they were. Those are two fields in which I though men would compose a higher proportion, but Math is within a stone’s throw of an even split. Among the younger cohort of majors, the vast majority of degrees are more female now, though Economics is still around 2/3 male.

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Iso-Standard Errors & Unemployment By Major

Zachary Bartsch2 Comments

Say that I want to calculate the unemployment rate by college major. That’s easy enough – we just divide the number of unemployed people by the number of people who are in the labor force. Repeat for each major.  The 2024 ACS includes all the variables we need, including employment status by week, but here I just calculate the annual average. I’ll calculate unemployment rates using the unweighted observations because I want to make a separate statistical point about simple standard errors.

Sample weights aside, we have some basic data maintenance to consider. The sample sizes for each major differ. Some majors include more than 15k people, while others have less than 100. Which majors should I include? All of them?

It depends on how precise I want the estimates to be – how small I want the standard errors to be. Below is the basic equation for the standard error (SE). Maybe I want my standard errors to be no more than 0.1%, since unemployment rates are on the order of 1-5%*. Then, it’s just a simple matter of setting the SE equation equal to 0.001 and solving…. Except that there are two variables. Getting a SE of 0.1% depends on both the sample size and the observed proportion. So, a basic rule such as ‘include only majors with at least 100 people won’t quite achieve what we want since the whole point of the exercise is that we suspect that the proportions of unemployed people differ among majors.

In order to get an idea of how many observations we need conditional on the proportion, we can use a popular tool in economics. Basically, adding the prefix “iso” to any term lets us see how two (or more!) other variables matter for the value of the term. We have iso-costs, iso-quants, iso-Sharpes, iso-retirement payments, etc. Here, we can have an iso-standard error by graphing the sample size on one axis, and the proportion on the other. With the desired SE as a constant, we can rearrange the SE equation so that  the sample size and proportion variables remain. They’re isolated below – solving for sample size is much simpler.  

The above equations let us see all of the sample size and proportion combinations that yield an arbitrary standard error. We’re basically doing the first steps of what’s known as a power calculation. We’re setting up some guard rails before doing the analysis so that we know ahead of time whether we’d even be able to tell the difference between proportions with the sample sizes that we have. Below is the graph of the iso-SE to illustrate the sample size & proportion combinations that yield some target SEs. Pictured is the left side of parabolas. What does it tell us? It tells us that shrinking the target SE increases the necessary sample size at each proportion REALLY FAST.

Since the most college majors have a sample size that is well below 15k, we basically know ahead of time that we won’t be able to tell the difference between their unemployment rates if they differ by a mere tenth of a percent. Given that the national unemployment rate in 2024 was around 4% and around 2.3% for college majors, there’s not much room for error – literally. Everything looks the same when proportions are similar and sample sizes are small.

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College Major & Income Sources

Zachary Bartsch4 Comments

We already know that economists earn more income on average. But when and how one earns income matters for how you spend your time both now and in the future. Being more productive affords the option to earn more money by working. For that matter, it also affords the option of staying home or pursuing passion projects at work or elsewhere.  Earning more money earlier in life also has implications for how you spend your time later in life.

Specifically, given the choice, you may choose to work less as a young adult so that you can spend more time with your family. The tradeoff isn’t just whether to work now or spend more quality time with others. After all, money can be saved for the future. Choosing to work less (or for a lower salary) today means that you may choose to work more in the future in order to achieve your desired standard of living. Personally, assuming I make it to old age, I would very much like to afford spending time with my family.

The more that you earn earlier in life, the more that you can save and invest for the future. The more that you save, the more that you can enjoy the fruits of compound interest. It’s not just a matter of earning more now rather than later. If you work and save now, then your future income can be passive. That is, your future earnings won’t require you to spend your time in an office or otherwise employed. You can still do that if you want, but you wouldn’t *need* to.  By having more retirement, investment, and social security income, your future self will earn plenty of income without spending as much time formally working.  You can instead spend time with loved ones or on other pursuits.

Below is the stacked bar graph of average income sources over each decadal age cohort. All data is from the 2024 ACS, so it’s just a snapshot in time rather than following individuals over the course of their life. I singled out people with Economics, Finance, and other 4-year college degrees. Economists make the most lifetime income if we count salary and other compensation alone. But if we look at the older cohorts, economics majors also earn more passive income. You’d think that Finance majors would earn more from investments. But among people in their 70s, economics majors earn more investment and retirement account income. Finance majors do earn more social security in that cohort, however.

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Income By Major (ACS 2024)

Zachary Bartsch9 Comments

A chart showing the average income by major was recently making the rounds on social media. So, I tried to replicate it. It turned out that some of the college majors were omitted. That part actually makes sense. The 2024 American Community Survey includes 174 degree fields – which is way too many for a clearly labeled bar chart. So, for local advertisement, I used only the majors and their equivalents that are offered at my university.  That chart is below (unweighted).

These are just raw average earnings by college major for employed adults. They all have decent sample sizes. Below is the cumulate distribution of sample size for each major. The smallest sample size is 45 (Military Technologies) and only 3% have sample sizes below 100. Only 34% have sample sizes below 1k.

You better believe that my colleagues and I show this chart to every single one of our classes. Obviously, it’s truncated from the full 174 majors, but it’s the relevant chart for us. If we use the full sample of college majors, Economics ($170k) drops to 3rd highest income, behind “Petroleum Engineering” ($173k) and “Health and Medical Preparatory Programs” ($183k). To be perfectly honest, those latter two sound a lot more difficult and have surprisingly little pay bump in compensation. Being more difficult is also consistent with the smaller sample size Economics=13k, Petroleum Engineering=343, and Health and Medical Preparatory Programs=1,099.  

One challenge that I’ve heard about the chart is that top business schools, such as Wharton, have Economics majors and various business concentrations. So, those top performing financiers are getting categorized as Economics in a way that is a bit misleading to young students elsewhere who are trying to decide on a major. If that’s true, then we should see Economics drop in the rankings if we omit the top-most earners.  After all, the criticism is that they’re pulling up the average.

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High Income Rentals are Low Income Rentals

Zachary BartschLeave a comment

Have you heard about the abundance movement? It basically says that we should enact a mix of regulatory and supply side reforms in order to produce more for everyone, especially the least economically advantaged. The reforms extend to the housing market and ensuring adequate housing.

There’s an argument that building any housing, even at the high end, can reduce the cost of shelter for everyone – even people who would never live in the newly built housing. The idea is that high income people switch to the newly built housing and leave less attractive housing. Someone else in that high income bracket snatches up the older place, leaving their prior housing vacant. The vacancy shuffles around high priced rentals until, ultimately, the vacant rental price must fall in order to attract a renter, such as someone further down the income distribution. Then the entire process continues, with the game of vacancy musical chairs working its way down the renter income distribution.

The more overlap that there is between housing consumption choices the quicker there is an impact on lower priced housing.   If you think that high income people consume higher priced housing, then you might think that there is a substantial difference between housing consumption choices and that it will take a long time for this ‘trickle down’ to get to the people who need it most. If income groups compete more for the same housing, then the effects on price will occur sooner for the lower income people.

How much Rental Overlap is there?

Miami, Florida has some of the highest housing costs in the US. Below is a histogram of annual rental costs in Miami by household income quartile (ACS 2024).   I restricted the data to positive incomes and rents and the highest rents are censored down to $98.4k annually. First, we can definitely see that the highest incomes (quartile 4) have the most censored annual rents and that the 1st income quartile (lowest) has the most annual rent payments nearer to zero. So, the histograms make sense in that way. But I was surprised by how much overlap there is. Different income quartiles are consuming many units in the same price range!

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Announcing the Disability Records Project

Zachary Bartsch1 Comment

Did you know that we have access to digital copies of the historical US census rolls? You can also find the digitized data at IPUMS. However, the data for people with disabilities is not great. It depends on the year, but those data have error rates on the order of 20% or higher.  We have the digital census rolls, the data just doesn’t match them.

So, I created a non-install windows computer application that lets people identify disabled people on those digital census rolls. Complemented with machine learning, my goal is to improve the accuracy of historical records about people with disabilities. Historical and quantitative research about disabled populations is relatively thin. We can do better. If you have students who would benefit from this research experience, then do please let me know! I can approve your institution’s email domain and we can get started.

The application is really straightforward with basically two user-facing features.

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Uncertainty Increases Profits?

Zachary BartschLeave a comment

Most people have an intuition that uncertainty can harm economic outcomes. Baker, Bloom, & Davis (2016) and Bloom (2009) demonstrated that industrial production and manufacturing decline in the face of policy uncertainty. The typical mechanism that people suggest is that uncertainty about the future causes people to engage in precautionary saving, resulting in fewer sales.

The theory continues that firms consequently decrease production as demand for their output declines. Firms aren’t interested in causing the quantities supplied and demanded to be equal. Rather, they don’t want to produce too many goods that don’t get sold or don’t get sold at an adequate markup. Production is costly.  A related theory is that more persistent or longer-run uncertainty can also depress investment, since the riskier future increases the tail risk of losses.

Rather than make a risky investment, one could instead just hold off and wait for some of that uncertainty to get resolved. There’s tradeoffs to this, of course. As future costs and benefits become clearer, they also get priced-in to asset values. So, there is an optimization problem. The possible downside outcome is big and uncertain. If the risk of the investment gets resolved and the downside outcome is still too likely or harmful, then a project manager did the ex-post ‘right thing’ by waiting.

But, if the downside risk disappears or is found to be very small, then waiting to invest in the project incurs an economic cost. Either 1) the profitable project and its associated profits will occur later and less valuably, or 2) other firms also resolve their uncertainty and bid up the price of the project’s inputs. Invest too early, and the downside is large and uncertain. Invest too late, and you may lose the potential upside partially or entirely.

But can uncertainty systematically increase profits?

Walter Oi said yes.

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Joy Explains how to integrate AI into a statistics class

Joy BuchananLeave a comment

I have put a working paper on SSRN describing three ways I incorporated AI into my Business Statistics 200-level class for undergraduates at Samford University in Spring 2026. Read the paper at the link.

Connecting Classroom Econometrics and Excel Training with Large Language Models (SSRN)

Abstract
Economics education almost always includes a component of statistics. Most undergraduate economics curricula require an upper-division econometrics course, and many economists teach data analysis. These courses help students become conversant with major contributions in economics research, but they can be challenging to teach. On its own, the mathematics of regression, error minimization, and statistical software may not feel exciting to students, especially compared with the intuitive economic reasoning that often draws them into the discipline. At the same time, students are increasingly interested in artificial intelligence and large language models. This note describes three practical teaching exercises designed to connect ordinary least squares, Excel training, and LLMs. The goal is to use students’ curiosity about AI to motivate classic statistical reasoning and practical spreadsheet skills.

The image is from ChatGPT 5.5 Thinking mode and it took over a minute to generate. The fact that their laptop screen is pointing away from them is funny (unrealistic). The portrayal of Tom Holland and Zendaya is good, which is what the audience cares about. So, this seems like a case of AI hallucinating up the thing that people want.

I posted back in February about the LLM Telephone game: Telephone Classroom Game for Teaching Large Language Models

Citation:
Buchanan, Joy, “Connecting Classroom Econometrics and Excel Training with Large Language Models” (May 27, 2026). Available at SSRN: https://ssrn.com/abstract=6839039

Note: I have been posting my papers to SSRN for a long time as a way to distribute them faster and more widely. I have heard rumors that SSRN might stop working, for my purposes. If anyone has suggestions for what I should do about the papers I have up there, please let me know! Or, if you are readying this post-summer-2026 and want a copy, send me a message at my Samford email so I can send you a copy.

The Empire Strikes Back against AI Cheating

Joy Buchanan2 Comments

People are considering whether university evaluations can survive in the AI age. Hollis Robbins wrote on Substack: “How to limit unauthorized AI use in the classroom“

Robbins emphasizes class size and teaching load against the time of an instructor.  An instructor teaching 4 sections with 100 students each is very limited in their ability to monitor and prosecute AI teaching. It’s worse if this instructor is on a temporary contract.

Limited eyes and hands and human attention really are a constraint here, at least for now. Some people see AI tools in the hands of students as the end of education itself.

I have been tweeting my replies to this:

I don’t do remote exams, but I hear about improvements to remote proctoring technology. The arms race is not over.

Technology goes both ways. The phone students were using to cheat are now being marshalled as a “second camera” for remote test proctoring. Instructors are going to largely win this year if they take current technology seriously, for multiple choice and short answer evaluations.

The commercial Respondus program has just added Word extensions. This technology already exists and can run on the students’ laptops.

Right now, a clever student might still be able to shift their carbon-based eyes to a direction where the answer is displayed illicitly. And the instructor’s eyes can only monitor so many eyes. This is all so 2024. This conversation may be over soon. Human students can be placed under the supervision of machine eyes. Right now, we are still dealing with issues of false positives when machines flag students for cheating, but the machines are improving.

I believe that the roads will eventually be dominated by machine drivers and their unblinking eyes. Humans might drive cars for fun in the hinterlands, but it will no longer be considered a serious thing humans to do for work. Monitoring student cheating will become like truck driving. Human eyes are on the way out. We are going to become more cheat-proof than college has ever been before.

As a college professor, that will have implications for my job, although I can imagine a not-completely-negative future. Maybe I could do more fun work with students because the work of proctoring will be handled automatically. I have spent many many hours constructing tests that would be hard to cheat on and watching students take them. I take cheating seriously, and all the faculty at my business school work hard to protect the value of our degree. I predict that this will become a trivial part of teaching within 10 years.

Will students respond with various forms of hacking and deep fakes against such a system? Maybe. So far, in any arms race, Uncle Sam has been winning in the end for a century now.

If there is a will to do so, we could even bring back the research paper by having students work on a monitored computer that does not let them use AI to write. (We could almost do that already, but perhaps the true limiting factor is that, as I like to say, readers are that which is scarce.)

[Credit to my colleagues Art Carden and Anna Leigh Stone who have talked with me about test proctoring this semester.]

Oil Price Lesson Plan for Economic Principles

Joy BuchananLeave a comment

Alex Tabarrok noted in Oil versus Ice Cream that he and Tyler, as textbook authors, “chose the oil market as our central example. Oil is always in the news…”

when a student sees that the price of crude has surged past $100 a barrel because Iran closed the Strait of Hormuz—choking off 20% of the world’s oil supply—they have the framework to understand what is happening. Supply shock, inelastic demand, expectations and speculation, the macroeconomic transmission to GDP—it’s all right there in the headlines.

In a classroom, a good way to begin is to ask the students to tell you what they have noticed recently about oil or gas prices. Having the students obtain the oil price data themselves could be fun, if you are in an environment with screens/computers.

A data source for undergrads is the FRED chart for WTI crude oil prices. It is clean and easy to explain in class. An instructor with slides could pull this up in real time. https://fred.stlouisfed.org/series/DCOILWTICO

Ask students: “Is this price change primarily explained by

  1. Increase in demand
  2. Decrease in demand
  3. Increase in supply
  4. Decrease in supply

Correct answer: d. Decrease in supply

If you cover elasticity, this is especially helpful as an example. “Why would the price jump more when demand is inelastic?”

It’s not too late to work this into a lesson plan for the Spring 2026 semester, economic teachers. I might use it to illustrate supply shocks next week.

This event is a classic example of a negative supply shock: a disruption in the Strait of Hormuz would reduce the amount of oil reaching world markets, pushing energy prices sharply upward. Because oil is an important input for transportation, manufacturing, and heating, higher oil prices raise costs across much of the economy. Firms may cut production, households may spend more on gasoline and utilities and less on other goods, and overall economic activity can slow. That is why economists worry that large oil supply shocks can contribute to recessions. They do not just make one product more expensive; they can ripple outward, reducing real income, lowering consumer confidence, and weakening GDP growth while inflation rises.

Related posts. The whole crew showed up this month:

James from March 12: Is a US Oil Export Ban Coming?

Jeremy from March 18: Gasoline Prices Have Increased at Record Rates, but Remain At About Average Levels of Affordability

Tyler from March 22: How much more will oil prices have to go up?

MattY from March 24: Why hasn’t oil gotten even more expensive?

Austin Vernon: https://www.austinvernon.site/blog/thestrait.html