Data

Economic Freedom Is Enough

James BaileyLeave a comment

Adam Smith kicked off modern economics by asking what causes nations to become wealthy. We’ve spent centuries improving our answers to the question and we still have lots to learn, but I think Smith’s original answer holds up remarkably well:

Little else is requisite to carry a state to the highest degree of opulence from the lowest barbarism, but peace, easy taxes, and a tolerable administration of justice; all the rest being brought about by the natural course of things. All governments which thwart this natural course, which force things into another channel, or which endeavour to arrest the progress of society at a particular point, are unnatural, and to support themselves are obliged to be oppressive and tyrannical

I was reminded of this by the latest Economic Freedom of the World report just released:

The overall correlation of GDP per capital and economic freedom is strong, but I find the extremes even more striking. Economic freedom is necessary for wealth: every country with a GDP per capita above $50,000 has above-average economic freedom (getting even close without high economic freedom requires having lots of oil). Economic freedom is sufficient: every country with an economic freedom rating over 8 has a GDP per capita over $50,000.

I periodically see someone claim that a country “did everything right” in terms of policy but still wound up poor, usually implying that the country is doomed by their geography or genes, or arguing that “neoliberal policy” failed. Whenever I check in on these countries, I inevitably find them to be far from “doing everything right”, with economic freedom scores typically far below 8.

It’s true that these are “just correlations” and that economic freedom alone isn’t a perfect predictor. It’s worth looking into the outliers and wondering- if a country is above the trend line without oil wealth (like China), what else are they doing right? If a country is well below the overall trend line (like Guatemala), does it mean they are have something else working against them to counteract the benefits of economic freedom? Or does it mean they are about to turn the corner and see high economic growth?

Overall though I don’t think it’s a big oversimplification to say that Adam Smith was right, and economic freedom is enough to bring prosperity. If you’re looking for economic growth, start with Smith’s peace, easy taxes, and tolerable administration of justice, or the Economic Freedom of the World’s small government, strong property rights and legal system, sound money, and freedom to trade at home and abroad.

“The Grim Old Days” in Charts

Jeremy HorpedahlLeave a comment

I’ve just started reading Chelsea Follett’s new book The Grim Old Days, which shows just how bad things were in the past. It’s a great book, and I encourage everyone to buy it. If you want a sample of what’s in the book, check out her series of blog posts on “contemporary accounts of daily life in the past” or read her blog post summarizing some of the topics in the book.

As an economist when I think about the past, in my head I see charts. Lots of charts, full of data! Follett’s book is mostly narrative history, and it is not filled with charts and data. That’s OK, I still am very much enjoying the book. But what charts would I select to show how grim the old days were, if I wanted to take that approach to the topic?

Here are four good ones from Our World in Data.

Child mortality in the past was horrible, with only about half of children surviving to adulthood (goes along with Chapters 2 and 3 of her book):

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Music Spending Is Smaller Share Of A Bigger Pie

James BaileyLeave a comment

We noted that “People Are Paying For Music Again” in 2024, showing that streaming was partly making up for the drop in sales of physical recordings, while live music sales were setting record highs. Thus,

When you combine live and recorded sales, total spending on music has now passed the 1999 peak; this is the biggest the market for music has ever been.

But I didn’t have a chart showing the music market as a whole. I meant to make on for a followup post, but still hadn’t got around to it when I saw this from Joey Politano’s Apricitas Substack:

He uses BEA data instead of the music industry sources I was using, which means a wider range of years is available, and the colors tell the story nicely. This chart still shows a late ’90s peak because it is measuring music as a percentage of all consumer spending; but total real consumer spending is way up since the ’90s, so the real dollar peak of money flowing to the music industry is today. Here’s my version of the chart using real dollars:

This chart tells a more optimistic story. But it’s worth reading the entirety of Politano’s post, which suggests that AI is already significantly reducing overall employment in the arts. He also notes that money moving from recorded to live music has changed which artists are winning. I’ve noted something of a ‘rich get richer’ phenomenon, with ticket prices for top artists shooting higher while it becomes harder for regular musicians to stay full time.

My semi-serious solution is to bring back hipsters. Make it once again cooler to spend $20 each weekend on an obscure band’s show or vinyl than to spend $1000 on a VIP ticket to a Taylor Swift-level show once a year. Hipsters might be annoying, but the hipster music strategy is an efficient way to support more people putting in the time to make music- and I think that would be a good thing in a field where talent is fairly widely distributed. The difference between full-time musicians and semi-pros who do a few gigs a year (or the best amateurs) is often not musical talent but luck, connections, and the willingness and ability to push through early years with little income. We’re a richer society than we were in 1999 and we can afford to support more people giving music a real try.

Wealth of Generations: Update Through the First Half of 2026

Jeremy HorpedahlLeave a comment

It’s been a while since I updated my generational wealth chart, and we now have estimates through the 2nd quarter of 2026, so here’s the latest chart:

Figure 1

Wealth for younger Americans continues to grow substantially, but let me make two caveats:

  1. Yes, I know median data is better. I’m writing a book that uses median wealth data! But the latest median wealth data from the Fed’s SCF is currently only available through 2022, so it’s not super relevant to current conversations. We should have the 2025 data soon.
  2. Because of the way the data in my chart is produced in the Fed’s DFA, it groups everyone under age 45 together. That’s a mighty big group, and it because it encompasses both Millennials and a lot of Gen Z, it makes it hard to directly compare to earlier generations.

So, until we have 2025 median wealth data, and until the Fed’s DFA starts breaking out Millennials and Gen Z, here is my current best compromise chart:

Figure 2

In Figure 2, I have used the Fed DFA data for age groups, which are still pretty large groups, but you can consistently compare them over time. The average wealth level of both the 18-39 group and the 40-54 group have seen substantial gains. In fact, the gains for younger cohorts have been even better than middle-aged Americans, though both saw substantial gains.

And this chart shouldn’t be affected by the lack of household formation among some younger Americans: I am using the full population as the denominator, so if anything, this will understate growth rates. Even so, the growth rate from the depths of Financial Crisis in 2010 have been substantial: 228 percent growth from 2010 to 2026 for ages 18-39. The growth rate for ages 40-54 was less dramatic, though they also didn’t experience as large of a slump from 2007-2010.

While we can always hope and work towards growth rates being better, average wealth for Americans of working age is currently at record highs, having fully recovered from both the Financial Crisis and the inflation slump of 2022.

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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Introducing the Certificate of Need Panel Database

James BaileyLeave a comment

Certificate of Need (CON) laws require healthcare providers to obtain state approval before opening new facilities, expanding existing facilities, or introducing certain healthcare services or equipment. We’ve covered them frequently here, and I’ve written several papers on them.

But my research evaluating the effects of CON on healthcare facilities, spending, and outcomes (along with everyone else’s) always had the drawback that we relied on fairly crude measures of CON- often just a binary measure of whether a state had any CON requirements at all. The problem with this is that different states have wildly different approaches to CON- some states like Vermont require CON for as many as 27 separate types of health facilities, services, or equipment, including major ones like hospitals, while other states like Ohio require CON for only a single type (nursing homes). Previous attempts to catalog this variation tended to produce single-year snapshots (e.g. Institute for Justice, Cicero, Mercatus, NCSL). These are helpful for policymakers wanting to see how their state compares to others, but not so useful to researchers trying to measure the effects of CON, who would typically prefer many years of historical data.

The dataset us CON researchers have always wanted is now here!

It tracks 31 different types of healthcare facilities, services, and equipment that are sometimes regulated by CON, showing which ones required a CON in each state in every year back to 1990 (going even further back for some states). This means our national panel has more than 55,000 data points.

Number of Certificate of Need Requirements Per State, 2025

I’ve spent the last 2+ years working on this with a large team of coauthors (Sriparna Ghosh, Conor Norris, and Justin Leventhal) and research assistants, with support from Providence College and the Knee Regulatory Research Center at WVU. It involved reading decades of old state statutes on HeinOnline and Westlaw. We’ve released a paper, Certificate of Need: A New Comprehensive Panel, explaining the dataset in more detail and sharing ideas for how researchers could use it.

Change In Certificate of Need Requirements Per State, 1990 to 2025
Change In Number of States Requiring Each Type of CON From 1990 to 2025

The dataset is public and free for everyone to use- we just ask that people cite us (though feel free to ask any of the dataset’s creators if you do want us as coauthors on your paper using the data). I’d love to hear your ideas for how you might use it, or how we can improve it- this is Version 1.0 but we plan to maintain and improve it going forward so that it can become the standard for the field.

Iso-Standard Errors & Unemployment By Major

Zachary Bartsch4 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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Family Income Continues to Grow in 2025 Data

Jeremy HorpedahlLeave a comment

Yesterday the Census Bureau released their annual treasure trove of data from the Current Population Survey, Annual Social and Economic Supplement. Loads of new data are now available for 2025 on income, poverty, and health insurance coverage. This new data allows me to update one of my favorite charts, showing the distribution of family income in the U.S. since 1967. Census releases this data by grouping families into nine different income groups, which I have collapsed into three groups, each being as close to one-third of the total as I can get using the publicly available data.

Figure 1

See also a similar chart from Mark Perry which uses household income (rather than family income) over the same time period.

Figure 1 shows that, adjusted for inflation, the proportion of families with income over $150,000 has grown almost seven-fold since 1967. There has been a roughly constant one-third of the population between $75,000 and $150,000, and the ranks of those under $75,000 has been cut in half since 1967. Again, these dollar figures all adjusted for inflation, using the preferred deflator of the Census Bureau.

What’s even more astonishing is when we look at the number of families at various thresholds, rather than just the share, as seen in Figure 2.

Figure 2

In 1967 there were fewer than 3 million families with incomes over $150,000 (in 2025 inflation-adjusted dollars). By 2025, that had grown to over 31 million families. If we look at the highest income threshold in this Census data — over $200,000 — the number of families has grown from just 1 million in 1967 to over 20 million in 2025. And there are fewer lower-income families too: the number of families under $75,000 shrunk, not just as a proportion of the total as seen in Figure 1, but even in absolute terms by almost 3 million families from 1967 to 2025.

Of course, some of these families do have more earners than in 1967, though we shouldn’t overstate that too much. Using other data from Census, we can see that the share of families with multiple earners hasn’t increased much since 1967, and especially hasn’t since the mid-1990s.

Table 1

As seen in Table 1, as far back as 1967 the majority of families in the U.S. had multiple earners. Now it’s true these were not all married couples with both spouses working full-time, and hours of work in the household have risen over time — but not much since the 1990s. This data is somewhat skewed by the aging of the population, as evidenced by the rising number of families with no earners. But even if we drop those families with no earners, multiple-income families haven’t grown much: from 58% of the total in 1967 to 63% in 2025.

Economics Major Income Premium

Zachary Bartsch2 Comments

I’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.

Challenge 1: Top Business Schools

“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.

Challenge 2: Econ Majors Choose Higher Paying Occupations

“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.

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New Health Freedom Index

James BaileyLeave a comment

The Center for Modern Health and the Knee Regulatory Research Center just released an index of how free residents in each state are to provide and pursue health care as they see fit. Their summary map looks like this:

The index was created by averaging measures of freedom in 54 separate categories, summarized into the 5 broad areas of Professional, Institutional, Patient, Payment, and Delivery Freedom. A report with maps for each of the 54 underlying measures is here, and a spreadsheet with all the data is here.

This project represents a major effort on an important issue, but I have to say my favorite part is just how unusual the final map of the overall ranking looks. I’ve created many maps of the states based on data and seen many more, but almost all of them (no matter the underlying variable they represent) end up falling into a handful of looks. They are either secretly maps of population density, or North vs South, or East vs West, or the South + Appalachia (high poverty, low health and education, et c). But the Health Freedom Index groups states in a way I’ve never seen before, putting Maine and New Hampshire with Mountain West states as the most free, while South Carolina and Louisiana join California and much of the Northeast among the least free.

Some of the Index’s creators will be presenting it online on September 18th.

Note: I’m affiliated with the Knee Regulatory Research Center at WVU, but I wasn’t directly involved with this project. I’m working on a different data project with Knee I hope to discuss here soon.