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.
Are you tired of hearing about revisions to jobs data? Well, there was another hot one released by BLS yesterday. Known as the “preliminary estimate of the Current Employment Statistics (CES) national benchmark revision to total nonfarm employment,” this change isn’t yet incorporated into the official jobs data. But it will, possibly slightly modified, be included with the January 2026 jobs release, altering jobs data back to April 2024. It is part of the normal annual process of reconciling the monthly, survey-based jobs data with the near-universe data from unemployment insurance records. Normally, this is a quiet affair, especially the preliminary estimate which is just giving a heads up to researchers about what will be coming in a few months.
I wrote about these preliminary figures last year, when the initial estimate was a negative revision 818,000 jobs. When revised and actually incorporated into the data, it was a somewhat smaller 598,000 jobs, which I then used in a post just last month to show that BLS hasn’t been getting worse at estimating jobs. If anything, they have been getting better. Yesterday’s report showed that the revision could be negative again, this time 911,000 jobs. That’s a little bigger than last year, but maybe it will end up being smaller in the final number. So, no big deal again?
Maybe not. The 911,000 jobs revision would actually be much larger than last year’s revisions because it’s coming on top of a slower growing labor force already. The initial report for March 2024 showed 2.9 million jobs added in the past year, so the 818,000 revision was a much smaller share than this most recent data, since the March 2025 initial report showed just 1.9 million jobs added in the prior year. And the March 2025 jobs numbers have already been revised down by over 100,000 jobs since the initial report, meaning that potentially half or more of the initially reported job gains would be lost due to the revision, as opposed to about 20 percent last year.
Is losing half of the job gains large? Yes. In fact, almost unprecedented:
(note: I am trying out a new chart template. Let me know what you think!)
“Both younger and older workers withdrew from the labor force in large numbers during the pandemic: In fact, their participation rates plummeted. Yet, within two years, the younger workers had bounced back to their pre-pandemic participation rates. But the older workers have not.”
They include a chart which seems to back up that assertion:
However, if you look closely, you will see that the older workers’ age group is open-ended. It includes 55-year-olds, as well as 95-year-olds. Given that the US population is aging, this seems like a poor choice.
While not available currently in the FRED database, there is data from BLS available for older workers that is not open-ended. For example, we can look at workers ages 55-64, who are older but still young enough that they are mostly below traditional retirement age. I use that data and compare with the 25-54 age group (note: because the 55-64 data isn’t available seasonally adjusted, I use the non-adjusted data for both age groups, then use a 12-month average, so my chart doesn’t exactly replicate the chart above):
By using a closed-end age group for older workers, we see that labor force participation has not only recovered from the pandemic, but it exceeds the pre-pandemic peak for both prime-age and older workers, and had done so by the Spring of 2023. In fact, both are now about 1 percentage point above February 2020. If we want to go to the first decimal place, older workers have actually increased their labor force participation slightly more: 1.1 vs 0.9 percentage points. But these are close enough, given that this is survey data, to say the recovery has been roughly equal.
The St. Louis Fed blog concludes by saying that early workforce retirements “will continue to depress the labor force participation rate of workers aged 55 and older for the foreseeable future.” But it’s not true that the LFPR of older workers is depressed! Provided that we exclude those 65 and older.
In 1967, about 56 percent of families in the US had incomes between $50,000 and $150,000, stated in 2023 inflation-adjusted dollars. In 2023, that number was down to 47 percent. So the American middle class shrunk, but why? (Note: you can do this analysis with different income thresholds for middle class, but the trends don’t change much.)
As you can see in the chart, the proportion of families that are in the high-income section, those with over $150,000 of annual income in 2023 dollars, grew from about 5 percent in 1967 to well over 30 percent in the most recent years. And the proportion that were lower income shrunk dramatically, almost being cut in half as a proportion, and perhaps surprisingly there are now more high-income families than low-income families (using these thresholds, which has been true since 2017). The number is even more striking when stated in absolute terms: in 1967 there were only about 2.4 million high-income households, while in 2023 there were 11 times as many — over 26 million.
Is this increase in family income caused by the rise of two-income households? To some extent, yes. Women have been gradually shifting their working hours from home production to market work, which will increase measured family income. However, this can’t fully explain the changes. For example, the female employment-population ratio peaked around 1999, then dropped, and now is back to about 1999 levels. Similarly, the proportion of women ages 25-54 working full-time was about 64 percent in 1999, almost exactly the same as 2023 (this chart uses the CPS ASEC, and the years are 1963-2023).
But since the late 1990s, the “moving up” trend has continued, with the proportion of high-income families rising by another 10 percentage points. Both the low-income and middle-income groups fell by about 5 percentage points. Certainly some of the trend in rising family income from the 1960s to the 1990s is due to increasing family participation in the paid workforce, but it can’t explain much since then. Instead, it is rising real incomes and wages for a large part of the workforce.
SPOILER ALERT FOR THE THIRD SEASON OF THE GILDED AGE
In Season 3 of the drama series “The Gilded Age,” one of the servants (Jack, a footman) earns a sum of $300,000 by selling a patent for a clock he invented (the total sum was $600,000, split with his partner, the son of the even wealthier neighbor to the house Jack works in). In the series, both the servants and Jack’s wealthy employers are shocked by this amount. Really shocked. They almost can’t believe it.
How can we put that $300,000 from 1883 in New York City in context so we can understand it today?
A recent WSJ article attempts to do that. They did a good job, but I think more context could help. For example, they say “Jack could buy a small regional bank outside of New York or bankroll a new newspaper.” Probably so, but I don’t think that quite conveys the shock and awe from the other characters in the show (a regional bank? Ho-hum).
First, the WSJ states that the “figure nowadays would be between $9 and $10 million.” That’s just doing a simple inflation adjustment, probably using a calculator such as Measuring Worth (it’s a good tool, and they mention it later in the story). But as the WSJ goes on to note, that probably isn’t the best way to think about that figure.
Here’s my best attempt to contextualize the $300,000 figure: as a footman, Jack probably made $7 to $10 per week. Or let’s call it $1 per day. That means Jack’s fellow servants would have had to work 300,000 days to earn that same amount of income — in other words, assuming 6 days of work per week, they would have had to work for almost 1,000 years to earn that much income. Jack appears, to his co-workers, to have earned that income almost in one fell swoop (though in reality, he spent months of his free time toiling away at the clock).
Yesterday I showed that BLS jobs reports from the CES aren’t getting worse over time, if we judge them by how much they are later revised. In fact, they are much better than decades past, with the last 20 years or so standing out as much better than the past.
Today I want to address a related but separate topic: are the initial jobs reports good at telling us when a downturn in the labor market is beginning? This is actually the strongest argument for releasing this survey data in a timely manner, even though the data often goes through significant revisions later. The report typically comes out the first Friday of a new month, so it is very current data. Given that the likely new BLS Commissioner has signaled he prefers the more accurate quarterly release, even though it is 7-9 months after the fact, it is useful to ask if these initial reports have any value in telling us when labor market declines (and recessions) are beginning.
That’s right: you are getting two posts from me this week, on essentially the same topic. Because it’s very important right now.
The short answer: the report is very good for the purpose of identifying downturns, especially the start of the downturns. Let’s walk through the past few recessions.
You’ve probably heard a lot about BLS data recently (or at least more than usual) with Trump firing the BLS Commissioner after a bad monthly revision to the nonfarm payroll jobs figures. But this didn’t come out of the blue, as there was plenty of criticism of the jobs numbers during the Biden term as well, mostly coming from the political right.
The two main criticisms leveled at the BLS, in my reading of it are:
The BLS is getting worse at estimating jobs numbers over time, leading to larger revisions
The revisions are done in a way that is favorable to Democrats
I think both of those claims can be analyzed with the following chart, which also shows those claims to be incorrect:
I had planned to write about the Trump-BLS fight today. But considering that two of my co-bloggers have already written about this (Mike on Monday and Scott on Tuesday) and that I have written about supposedly “fake” jobs numbers before several times (see January 2024 and August 2024), I will hold off on that topic until all of the dust settles. But this is a very important topic, and I believe Trump is clearly in the wrong (as is Kevin Hassett, see my tweets from this week), so please do continue to follow this topic and sane voices on it (see a Tweet from Ernie Tedeschi and from me for a long-run perspective on data accuracy).
But now, on to something a little more light-hearted: is everyone traveling to Europe these days?
Judging by my Facebook feed, it seems that Yes, lots of people are traveling to Europe. But this could be a result of selection bias in at least two ways: the people I am friends with on Facebook, and what people choose to post about on Facebook.
So what does the hard data say? We actually have pretty good long-run data on this question. In short: yes, lots more Americans are traveling to Europe (and overseas generally). Though don’t worry: not everyone went to Europe this summer, despite what social media might have you believe.
For starters, here’s a chart showing three decades of US overseas travel:
Last week I wrote about the GDP predictions from Kalshi and the GDPNow Model. They were both showing 2.4% for Q2 of 2025 last week. They both changed slightly by yesterday, up to 2.8% and 2.9%. The final result (technically, the “advanced” result, but the final one for purposes of this comparison) was 2.97%. The Atlanta Fed GDPNow model continues to be a top performer, and you can’t do much better than averaging these two estimates. And you can pretty consistently do better than the median result from the WSJ/Dow Jones survey of economists.
Back in April I wrote about 4 different estimates of GDP growth and how well they have performed since 2023. With the 2nd quarter of 2025 GDP data coming out next week, what do the best performing predictors currently say?
In that last post, I showed that the Atlanta Fed GDPNow model and the Kalshi betting market were generally the best performers. And furthermore, averaging these two improves the predictive power a little more. As of today, the GDPNow model is predicting 2.4% growth and Kalshi is… also predicting 2.4%!
There will be a few more updates to GDPNow over the next week, and of course Kalshi is constantly updating as more people bet. But as of right now, 2.4% growth seems like a reasonable prediction. That may surprise some people, especially given all of the pessimism surrounding tariffs and policy uncertainty generally. But despite all of this, the US economy appears to be just continuing to chug along.
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