The youngest economist was born this week! So far we’ve graded intermediate Micro exams, Western Economic History literature reviews, and we’ve read some ‘Order without Design’. Next week maybe we’ll dive into the ACS data on graduate degrees.
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).
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.
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.
Continue readingSay 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.
Continue readingI’ve written about Economics major incomes before. The consistent empirical fact is that they earn more than most other college majors. But why? My working theory is that’s it’s due to human capital differences.
“The highest ranked business schools offer economics majors with various business concentrations instead of separate business majors. So, the high average income of econ majors is due to those top tier finance concentrations and the like.”
This challenge doesn’t hold water. If the high average income were just due to top performers, then omitting them would break the pattern of high economic major compensation. But it doesn’t. Trimming the top and bottom 10% of incomes for each major doesn’t cause economics to fall much in the ranking.
“Economists aren’t especially productive. They merely choose higher pay occupations. Other majors could achieve the same thing if they wanted to.”
This challenge is partially true. Economics majors do choose higher paying occupations. The Bureau of Labor Statistics has an extensive list of occupation categories and codes that are linked to the American Community Surveys. I examine the broadest categories that have sample sizes of at least 40 for each economics and other majors. The below scatter plot shows the relationship between average income by occupation and the proportion of economics majors who chose to work in those occupations. There is clearly a positive relationship. Economics majors do choose higher paying occupations.
But the claim about productivity isn’t quite right. If economics majors were just as productive as other majors within their occupational category, then they would earn around the average income within each occupational category. But they don’t! Below is a chart that plots the average income premium over non-economics majors within each occupational category (error bars are one standard error). The occupations to the left are more abstract or even social in nature. That’s where economics majors earn their big income premium. Further to the right are occupations that are more ‘hands-on’. Economics majors earn about the same as non-econ majors in those categories.
The one interesting case is ‘Computer and Mathematical’ occupations, which are abstract in nature and yet economics majors have no better earnings on average. Those occupations have a higher than typical proportion of Computer Engineering, Computer Science, Computer Information Systems, and Mathematics majors. Given that those majors 1) also have training in abstract theory and 2) are highly specialized, it’s impressive to me that economists can keep up.
Additionally, economics majors are not uniformly distributed across occupational categories. They tend to pursue occupations in which they have an advantage as indicated by their wage premium. The below chart has the same horizontal axis as the one above and has more mass further to the left. A higher proportion of economics majors are in the occupations where they outperform others in the same occupation.
Continue readingWe 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.
Continue readingA 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.
Continue readingHave 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.
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!
Continue readingDid 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.
Continue readingThere has been a lot of shade thrown at the United Kingdom recently from economists and political scientists. Economic growth has gone down the tubes and there have been six prime ministers over the past decade. As an American, I didn’t really know if that was a lot. The social media says that we’ve had a lot of turnover recently. Is six prime ministers in ten years a lot in the UK’s parliamentary system? I grew up watching Tony Blair on TV for a ten-year stretch. But I had no context for the historical norm or whether there is any precedent. Here I look at the data.
Right now, there are a record number of former prime ministers still living (PM). Prior to the recent spike, the maximum number of living people who had left office was five. Right now in 2026, there that number is nine! And if the current PM, Andy Burnham, follows the recent trend of short stints in office, then they’ll hit ten. As an American, it’s hard for me to imagine having 10 living former presidents. According to the below charts, the British are probably a bit jarred too!
In last week’s post I noted that we’re tied for the most living former presidents. But truly, the UK’s numbers are what inspired me to look at this topic in the first place. To recap, the number of living ex-executives can be caused by 1) Longer lifespans, 2) Leaving office at a younger age, and 3) More unique executives. In the US, being currently tied for the record is overwhelmingly driven by longer lifespans. What about the UK?
Continue readingIf you count president Trump, the number of living former presidents is at a historic high of five (Clinton, G.W. Bush, Obama, Trump, Biden). The number of living ex-presidents can increase for three reasons. 1) More people becoming president, 2) ex-presidents having longer lifespans, and 3) presidents leaving office earlier in life. Why do we have so many right now?
The historical maximum number of people who both 1) leave office and 2) live simultaneously with others is five. It first happened in 1861 when Abraham Lincoln (16th) was president for just under a year before John Tyler (10th) died in 1862. Since then, the number of living ex-presidents has been mostly below four if not below 3. The figures below graph the number of people living who have been US president. The left graph uses daily data and the right uses the annual average (weighted by day).
In fact, besides Washington, we’ve had four other periods when there were ZERO ex-presidents living. The first was under Grant (18th). This changes my perspective of that period. Living ex-presidents provide a sense of continuity – that something from the past continues today. They give us hope that our country will continue into the future. Grant presided over part of the reconstruction era. For part of this presidency, there was no one else who knew how he felt and no living person who had been in his position. What a tenuous time!*
The other presidents who, at some point, had no living predecessors were Theodore Roosevelt (26th), Herbert Hoover (31st), and Richard Nixon (37th). But since 1981, we’ve had three or more living presidents. So, most of us feel like that’s “normal”. Imagine if there was just Trump, and that’s it. That’d feel jarring.
A presidential term is four years and only one president was in office for more than two terms, Franklin Roosevelt (32nd). Let’s take a 24-year trailing average. With 8-year tenures, the least number of presidents is 3. With 4-year tenures, the greatest number of presidents is 6. Assassinations and other deaths of sitting presidents can push the number higher. The graph below is the number of unique people to act as head of state over the prior 24 years (I say ‘unique’ because Cleveland (22nd & 24th) and Trump (45th & 47th) both served two non-consecutive terms).
We can conclude that the number of unique presidents is not exceptionally high at this time. The historical average is about 5.2 unique presidents. We’ve been below that since 1998 owing to a higher proportion of two-term presidents since then. Before Biden (46th), Bush (41st) was the last time that we had a one-term president. So, in terms of executive regimes, the 21st century has been unusually stable. But this stability also places downward pressure on the number of surviving ex-presidents. So, we’ve had many living ex-presidents despite our few regime changes. Reason 1) doesn’t explain why we have so many living ex-presidents now.
