Datasets can be pulled offline for all sorts of reasons. As I wrote in February, this shows the value of being a data hoarder– just downloading now any data you think you might want later:
Several major datasets produced by the federal government went offline this week…. This serves as a reminder of the value of redundancy- keeping datasets on multiple sites as well as in local storage. Because you never really know when one site will go down- whether due to ideological changes, mistakes, natural disasters, or key personnel moving on.
The US Federal government shutdown this month provides another reminder of this. So far most datasets are still up, but I’ve seen some availability issues:
The good news is that a number of institutions have stepped up in 2025 to host at-risk datasets (joining those like IPUMS, NBER, and Archive.org that have been hosting datasets for many years, but are scaling up to meet the moment):
Restore CDC hosts all CDC data as it was in January 2025.
Harvard Library’s project is less user-friendly but more powerful, archiving all 16 terabytes of Data.gov.
Yesterday on Twitter I shared a chart showing the age at first marriage for white men and women in the US, with data going back to 1880. I pointed out an interesting fact: at least for men, the age was essentially the same in 1890 and 1990 (27), though for women it was a bit higher in 1990 than in 1890 (by about 1 year).
This Tweet generated quite a bit of interest (over 800,000 impressions so far), and (of course!) a lot of skeptical responses. One skeptical response is that I cut off the data in 1990, when trends since then have shown continuously rising ages at first marriage, and by 2024 the comparable figures were much higher than in 1890 (by about 4 years for men and 6.5 years for women). In one sense, guilty as charged, though I only came across this data when looking through the Historical Statistics of the US, Millennial Edition, and that was the most current data available when it was printed. Here is a more updated chart from Census:
But there is another interesting fact about that data: the massive decline age of first marriage in the first half of the 20th century. Between 1890 and 1960, the median age at first marriage fell by about 3 years for men and 2 years for women. For men, most of the decline (about 2 years) had already happened by 1940. Thus, if we start from the low-point of the 1950s and 1960s (as many charts do, such as this one), it appears marriage is continuously getting less common in US history, while the fuller picture shows a U-shaped pattern.
This same pattern shows up in another measure of marriage data: the percentage of people that never get married. If we look at White, Non-Hispanic Americans in their late 40s, the picture looks something like this (keen observers will note that the Hispanic distinction is a modern one dating from the 1970s, but Census IPUMS has conveniently imputed this classification back in time based on other demographic characteristics):
Looking at people in their late 40s is useful because, at least for women, they are past their childbearing years. And using, say, the late 50s age group doesn’t alter the picture much: even though some people get married for the first time in their 50s, it’s always been a small number.
Here we can see an even more dramatic pattern. 100 years ago, it was not super rare for people to never marry: over 1/10 of the population didn’t! But by 1980 (thus, for people born in the early 1930s), it was much rarer: less than 4% of women were never married (among White, Non-Hispanics). In fact, the peak in 1920 of 10% unmarried women wasn’t surpassed again until 2013! And it’s not substantially higher today than 1920 for women, especially when considering the full swing downward. Men are quite a bit higher today, though the 1920 peak of 13% wasn’t surpassed again until 2006.
For a measure that peaks in 1920, we might wonder if new immigrants are skewing the data in some way, given that this is right at the end of about 4 decades of mass immigration. But just the opposite: if we focus on native-born women, the 1920 level was even higher at 11.1%, which wasn’t surpassed until 2022, and even in the latest figures it is less than 1 percentage point higher than 1920.
Precisely why we observed this U-shaped pattern in marriage (both first age and ever married) is debated among scholars, though my sense among the general public is that it isn’t much thought about. Most people (from my casual observation) seem to assume that marriage rates and ages were always lower in the past, and that modern times are the outliers. But in reality, the middle part of the 20th century seems to be the outlier. The “Baby Boom” of roughly 1935-1965 is possibly better understood as a “Marriage Boom,” with more babies naturally following from more and younger marriages.
Academics generally agree on the changing patterns of mortality over time. Centuries ago, people died of many things. Most of those deaths were among children and they were often related to water-borne illness. A lot of that was resolved with sanitation infrastructure and water treatment. Then, communicable diseases were next. Vaccines, mostly introduced in the first half of the 20th century, prevented a lot of deaths.
Similarly, food borne illness killed a lot of people before refrigeration was popular. The milkman would deliver milk to a hatch on the side of your house and swap out the empty glass bottles with new ones full of milk. For clarity, it was not a refrigerated cavity. It was just a hole in the wall with a door on both the inside and outside of the house. A lot of babies died from drinking spoiled milk.
Now, in higher income countries, we die of things that kill old people. These include cancer, falls that lead to infections, and the various diseases related to obesity. We’re able to die of these things because we won the battles against the big threats to children.
What prompts such a dreary topic?
I was perusing the 1870 Census schedules and I stumbled upon some ‘Schedule 2s’. Most of us are familiar with schedule 1, which asks details about the residents living in a household. But schedule 2 asked about the deaths in the household over the past year. Below is a scan from St. Paul, Minnesota.
When reading an old novel or watching a period drama movie or TV show, it is almost inevitable that some historical currency amounts will be mentioned. This is especially true when the work is dealing with money and wealth, for example the series “The Gilded Age” is about rich people in late 19th century America. So money comes up a lot. I wrote a post a few weeks ago trying to contextualize a figure of $300,000 from 1883 for that show.
A new Netflix series “The House of Guinness” is another period piece that spends a lot of time focusing on rich people (the family that produces the famous beer), as well as their interactions with poorer folks. So of course, there are plenty of historical currency values mentioned, this time denominated in British pounds (the series is primarily set in Ireland, where the pound was in use). On this series, though, they have taken the interesting approach of giving the viewers some idea of what historical currency values are worth today, by overlaying text on the screen (the same way they translate the Gaelic language into English).
For example, in Episode 4 of the first season, one of the Guinness brothers is attempting to negotiate his annual payment from the family fortune. He asks for 4,000 pounds per year. On the screen the text flashes “Six Hundred Thousand Today.”
The creators of the show are to be commended for giving viewers some context, rather than leaving them baffled or pausing the show to Google it. But is 600,000 pounds today a good estimate? Where did they get this number? As with the “Gilded Age” estimate, it’s complicated, but it is probably more than you think.
My new article, “Prohibition and Percolation: The Roaring Success of Coffee During US Alcohol Prohibition”, is now published in Southern Economic Journal. It’s the first statistical analysis of coffee imports and salience during prohibition. Other authors had speculated that coffee substituted alcohol after the 18th amendment, but I did the work of running the stats, creating indices, and checking for robustness.
My contributions include:
National and state indices for coffee and coffee shops from major and local newspapers.
A textual index of the same from book mentions.
I uncover that prohibition is when modern coffee shops became popular.
The surge in coffee imports was likely not related to trade policy or the end of World War I
Both demand for coffee and supply increased as part of an intentional industry effort to replace alcohol and saloons.
An easy to follow application of time series structural break tests.
An easy to follow application of a modern differences in differences method for state dry laws and coffee newspaper mentions.
Evidence from a variety of sources including patents, newspapers, trade data, Ngrams, naval conflicts, & Wholesale prices.
Generally, the empirical evidence and the main theory is straightforward. I learned several new empirical methods for this paper and the economic logic in the robustness section was a blast to puzzle-out. Finally, it was an easy article to be excited about since people are generally passionate about their coffee.
Bartsch, Zachary. 2025. “Prohibition and Percolation: The Roaring Success of Coffee During US Alcohol Prohibition.” Southern Economic Journal, ahead of print, September 22. https://doi.org/10.1002/soej.12794.
The Fraser Institute released their latest report on the Economic Freedom of the World today, measuring economic policy in all countries as of 2023. They made this excellent Rosling-style graphic that sums up their data along with why it matters:
In short: almost every country with high economic freedom gets rich, and every country that gets rich either has high economic freedom or tons of oil. This rising tide of prosperity lifts all boats:
This greater prosperity that comes with economic freedom goes well beyond “just having more stuff”:
The full report, along with the underlying data going back to 1970, is here. The authors are doing great work and releasing it for free, so no complaints, but two additional things I’d like to see from them are a graphic showing which countries had the biggest changes in economic freedom since last year, and links to the underlying program used to create the above graphs so that readers could hover over each dot to identify the country (I suppose an independent blogger could do the first thing as easily as they could…).
FRDM is an ETF that invests in emerging markets with high economic freedom (I hold some), I imagine they will be rebalancing following the new report.
Housing is certainly more expensive than in the past. I have written about this several times, including a post from last year showing that between about 2017 and 2022 housing started to get really expensive almost everywhere in the US, not just on the West Coast and Northeast (as had previously been the case). I don’t think the housing affordability crisis is in serious doubt anymore, and it can’t be explained over the past few years by increasing size and amenities, since those haven’t changed much since 2017 (though it is relevant when comparing housing prices to the 1970s).
But why did this happen? Knowing why is crucial, not merely to blame the causes, but because the policy solution is almost certainly related to the causes. I and many others have argued that supply-side restrictions, such as zoning laws, are the primary culprit. The policy solution is to reduce those restrictions. But a recent op-ed titled “Why your parents could afford a house on one salary – but you can’t on two,” the authors place the blame for housing prices (as well as the stagnation of living standards generally) on a different factor: Nixon’s 1971 “severing the dollar’s link to gold.” The authors have a book on this topic too, which I have not yet read, but they provide most of the relevant data in this short op-ed.
Does their explanation make sense? I am skeptical. Here’s why.
But as Jeremy often points out here, young adults have actually been doing pretty well at building wealth. So why are they so gloomy?
Since I’ve now aged out of the young adult category, I’m obligated to start by wondering if kids these days are just whinier, and need to quit doomscrolling and toughen up. But if I try to see things their way, here’s what I can come up with for why their pessimism could be rational:
It’s About The Future: Sure things have been fine, but that is about to change. The more farsighted youth know they will be the ones expected to pay back the big deficits the Federal government is running. They have student loans to pay today now that payments have fully resumed. I predicted after the 2022 student loan forgiveness that we would be back to all-time highs in student debt by 2028, but in fact we are there already. The youth unemployment rate is now 10.5%, up from 6.6% in April 2023, and could rise a lot more if AI really starts displacing jobs:
Source: Brynjolfsson, Chandar and Chen 2025.Source: Michigan Consumer Survey
2. It’s About Housing: House prices are at all time highs (far above the prices during the 2000s “bubble”). Mortgage rates remain high, and to the extent that Fed rate cuts push them down, they will likely push prices higher, leaving homes hard to afford. High credit standards post-Dodd-Frank mean younger buyers in particular find it hard to get a mortgage; homeownership rates are falling while the average age of homeowners shoots upward. Most older people already own a house, while most young people want to buy but see that as increasingly out of reach.
Good luck getting a mortgage without super-prime creditEveryone thinks it’s a bad time to buy a house, but this matters most if you’re young and don’t already own oneThe median American is 39 years old but the median homebuyer is 56
This is post coauthored with Jack Cavanaugh, Ave Maria University Graduate of 2025.
Say that you want to become a successful lawyer. What does that mean? One possible meaning is that you are well-compensated. Money is not everything, but it does give people more options for how to spend their time and resources. Law degrees are a type of graduate degree. So, what bachelor’s degree major should one choose in preparation for law school? We lack rich administrative data on college majors and LSAT scores.
Luckily, the 2023 American Community Survey (ACS) comes to the rescue. It has all of the typical demographic covariates, income, occupation, and college major. So, if we make the small leap that well-prepared law school students become high-performing lawyers who are ultimately paid more, then what college major puts you on the right path? What should your major be?
We don’t look at an exhaustive list. We place several occupations into bins and examine only a few alternative majors. Any unlisted major falls under ‘other’. Below are the raw average incomes by occupational category and college major. Note two majors in particular. First, Pre-law literally has the word ‘law’ in the name and is marketed as preparation for law school. However, it is the undergraduate major associated with the lowest paid lawyers. For that matter, Pre-law majors have the lowest pay no matter what their occupation is. Second, Economics majors are the most highly paid in all of the occupations.
This is from the latest Census release of CPS ASEC data, updated through 2024 (see Table F-23 at this link). In 1967, only 5 percent of US families earned over $150,000 (inflation adjusted).
Addendum: Several comments have asked how much of these trends can be explained by the rise of dual-income households. The answer is some, but not all of it, which I have written about before. Dual-income households were already the most common family structure by the 1980s. There hasn’t been an increase in total hours worked by married households since Boomers were in their 30s. You can explain some of the increase up until the Boomers by rising dual-income households, but this doesn’t explain the continued progress since the 1980s. And as Scott Winship and I have documented, even if you look just at male earnings, there has been progress since the 1980s.
Economist Writing Every Day 