Author: Jeremy Horpedahl

Wealth of Generations: Update Through the First Half of 2026

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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.

Are You Better Off Than You Were 22 Months Ago? Midterm Elections and Real Wage Growth

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The phrase “are you better off than you were 4 years ago?” seemed to have been a winning campaign slogan for Ronald Reagan in 1980. But when I looked at the data, it didn’t really hold up, if we are looking at real income growth to determine “better off.” Real family income growth was actually positive under Carter, but he lost reelection. Then four years later, Reagan won reelection, despite having negative real income growth. This is a pattern we see often in recent presidential reelection years. Interestingly, it does not appear to be 4-year income growth that matters, but 1-year income growth might.

Is there any similar data we can look at for midterm elections? It is well known that Presidents almost always lose seats in a midterm election. But might they lose fewer seats if the economy is performing well? The following scatter plot shows all midterm elections since 1970. On the horizontal axis is the percent change in real wages (AHETPI series, deflated by the PCEPI) from the January a President takes office until November of the midterm election year. The vertical axis shows the net change in Congressional seats for the party of the President, including both the Senate and House (I weight Senate seats at 4.35x House seats, which is somewhat arbitrary).

Figure 1

What can we learn from this scatter plot? I would say there is a weak correlation between the variables: better real wage growth means losing few seats. The sample size is small, so I am reluctant to plot a trendline, but it would be positive. The only two midterm elections since 1970 where the President’s party actually gained Congressional seats had very high wage growth: Bush in 2002 (3.3%) and Clinton in 1996 (a very robust 5.3%). Very negative wage growth shows up just once, for the Nixon/Ford midterm in 1974, and here the Congressional losses were pretty massive.

Now you might object that other factors matter. And surely they do. The Republicans probably would have lost a lot of seats in 1974 anyway, due to Nixon’s resignation. But I’m sure the higher rates of inflation and resulting negative real wage growth didn’t help. And Bush probably got a significant post-9/11 hero bounce in 2002, though the real wage growth might have helped too.

However, Clinton and Obama had the worst midterm losses in this era, in 1992 and 2010, much worse than Nixon/Ford in 1974, and despite positive but weak real wage growth. Does this mean the correlation is useless?

Here’s one way to save it: compare Presidents against themselves. While Clinton and Obama had poor showings in their first midterm elections, their second midterms were much better — and had much better wage growth! Ditto for Nixon, though in reverse: his first midterm was pretty good, and so was real wage growth. Reagan had similarly bland real wage growth rates in both midterms, and had similarly mediocre Congressional losses. Bush did worse in his second midterm — again, you could blame a reverse-9/11 effect, with voters now weary of the global War on Terror, but wage growth was also worse.

What does this all mean for Trump? While we don’t have the full wage growth picture yet, through 18 months it has been pretty mediocre: 0.3%, and falling in recent months due to inflation exceeding wage gains for most of 2026. On social media and among pundits, a lot of people are trying to make comparisons to Biden. “But gas prices were even higher under Biden!”

If Figure 1 is at all useful, it is probably most useful to compare Trump to himself, in his first midterm. Wage growth through November 2018 was about 2.1%: not bad, not great. And he suffered middling Congressional losses. Because wage growth will almost certainly be much worse this time around, we can expect Trump to perform much worse than in his first, 2018 midterm. If Democrats pick up 20 seats in the House and 5 seats in the Senate (current prediction markets suggest about this many), this will put Trump pretty close to the Reagan 1986 point in Figure 1. That feels about right, given recent history, though it wouldn’t be surprising if the Republicans lost even more seats, since that would be a Congressional shift not much different from Trump’s first midterm.

Family Income Continues to Grow in 2025 Data

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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.

Women Have Always Worked, Often in the Formal Labor Force

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In a February 2025 blog post, I created a chart showing male and female work patterns both inside and outside the home, going back to 1900. The data was for the United States, and the general trends were more female work in the paid labor force, more male work in the household (rather than paid labor force), but overall total hours of work being roughly constant from 1900 to 2023 (on average, of course).

Despite women always working, 1900 did look a lot different: adult working-age women in the US spent about 50 hours per week working in their own household, and a little under 10 hours per week in the formal paid labor force. Men were almost a mirror image of this (though working a few hours less per week in total).

So the gradual change over the 20th century was a huge shift in gender roles, and by 2023 women on average spent about the same number of hours in household and paid work. But the US is something of an anomaly here. Female labor force participation was higher — in some cases much higher — in other developed countries around 1900. Using data collected by Claudia Olivetti, here are female labor force participation rates for working age women (generally ages 15-64) around 1890-1900 (I average across years if there are multiple estimates):

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Median Family Income for Married Couples With Children Is Probably Higher Than You Think

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In 2024, median income for married couples with children at home was $143,400 in the US. That’s an almost 80 percent real (inflation-adjusted) increase since 1974, the first year Census reports comparable data. Is there some selection bias in who chooses to get married and have kids? Yes. Has there been an increase in dual-income families? Yes, but probably much less than you think (the median family of this type already had two earners by the late 1970s).

With those caveats, this is still pretty impressive:

Nicholas Polson Has Written Over 200 Academic Papers in 2026 (so far)

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UPDATE: I stupidly didn’t realize my co-blogger Mike wrote about this too. My bad! I skimmed last week’s posts, but it didn’t click in my head. Be sure to read his thoughts.

ALSO: lots of the papers by Polson seem to have vanished from SSRN since I wrote this blog post… yesterday. Everything after August 14th has been taken down. That brings his count of papers in 2026 down to a mere 168 papers. Still essentially several lifetimes of output from a typical academic.

CODA: As Andrew Gelman documented in real time, all of the papers appear to have been removed from SSRN for now. No explanation as of yet, but you can still see many of the papers listed (for now) on Polson’s Google Scholar page.

For most academics, writing papers that may be eventually published in peer-reviewed journals is an important part of what we do. For some academics, it is the main thing they do (others have more emphasis on teaching courses at their university). Most academics always have a few projects they are working on, with perhaps a goal of finishing 2 or 3 a year, and thus having a regular pipeline of a few publications every few years. But some academics are much more prolific.

Take Daron Acemoglu for example. He has long been considered extremely prolific. So far in 2026, according to his Google Scholar page, he has had 15 papers that have either been published this year or come out as new working papers (that’s the bulk of them). In the past year (2025 and 2026), he has had publications in the Quarterly Journal of Economics, the American Economic Review, and the Journal of Economic Literature, among others (the AER paper was his Nobel lecture). For lower tier academics, that’s almost a lifetime of publications in 12 months or so. Acemoglu is extremely productive.

But I just discovered an economist that is, apparently, even more productive than Acemoglu, at least as measured by working papers. Nicholas Polson has, by my count using his SSRN page, already written 258 working papers in 2026 alone. He’s already written (or at least published to SSRN), six papers today, August 26, 2026. In the month of August 2026, he has written and posted to SSRN a total of 104 papers — and counting, since the month isn’t quite over.

These aren’t just short notes. Most of the papers are of normal academic length: 32 pages, 27 pages, 58 pages. The papers are both theoretical — involving complex math in some cases — or empirical, with regressions. Read any single paper, and it feels like just a normal academic paper, the kind of thing that an academic might work on for a few months. He even has a frequent co-author, which is common for economics papers (and helps to be more productive), a systems engineering professor named Vadim Sokolov, who is a co-author on a little over 100 of the papers this year.

What is going on here? Obviously the research productivity of Polson and his co-author Sokolov is aided by AI. Who isn’t using AI to increase their writing and research productivity these days? But I don’t think I have seen any academic, at least not in economics, that has really pushed it to the limit.

Presumably, many of these papers will get submitted to academic journals. I can imagine the editors of journals have a very hard job these days, as the number of papers submitted has likely increased significantly, while the time that referees have available has not increased much (of course, AI is likely making referees more productive too, though many journals ask you not to upload the paper into an AI program as a referee, since it is unpublished work when you are reviewing it).

I really don’t know where academic publishing goes from here. AI has made us all more productive in terms of output, but are we better at answering important questions in our science than pre-AI? Probably, though it is hard to know. Journals and the peer review process has traditionally been the filter to sort real contributions from gibberish. I don’t know how the peer review process continues in its current form given the massive increase in output (much of it good!) that we are seeing from academics. Dr. Polson is just a leading example of a growing challenge for academia.

Welcome Back to School: Potential College Students are Now Declining

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If you have spent any time around higher education lately, you have probably heard of the “demographic cliff” or “enrollment cliff” for years now. Well, it’s finally here. In terms of total number of births, the US peaked in 2007 at a little over 4.3 million births. That’s the highest year ever, even higher than the peak of the Baby Boom (not in terms of fertility rates, of course, I’m just talking about absolute number of births).

Babies born in 2007 turned 18 in 2025. But after 2007, births started to fall. In 2025, there were just about 3.6 million births, a decline of about 700,000 babies since 2007, or a 16 percent decline. The number of 18-year-olds won’t be exactly the same as the number of births in a given year: it’s actually usually a bit higher, as net immigration is much larger than the small number of children that die before they reach 18. For example, the 1982 birth cohort had 3.68 million babies, but 18 years later in the year 2000 there were 4.08 million potential college students.

Historically there have been about 10 percent more 18-year-olds than the birth cohort, but lately (2021-2025) it has only been about 5 percent higher than the birth numbers.

There are, of course, all kinds of social, economic, and political implications of falling births. I just want to mention one that is specific to the industry that I work in: potentially falling college enrollment. And because this enrollment will not be uniform across states and universities, this will cause serious budget issues for many colleges in the coming years.

Some folks in higher ed have lately been asking when the demographic cliff will hit. It’s here:

Everything At the Grocery Store is On Sale Relative to 1980

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Back in May 2024, I wrote about grocery prices in 2024 compared to 1980. Relative to average wage increases, almost everything was more affordable — the one exception was bacon.

Grocery prices have continued to climb since 2024, but so have wages. What does the comparison look like now? Well, I have good news for bacon lovers:

Figure 1

The chart shows the change in relative affordability, as measured by how many minutes of work at the average wage it would take to purchase the item (using consistent product sizes and weights). As I wrote in that 2024 post, these are not items that I cherry picked. These are all 24 grocery items where BLS has price data in both 1980 and 2026 (out of about 150 items total). Perhaps there is some survivorship or selection bias as to which items are available in both years, but looking at the list this seems like a pretty reasonable shopping cart for a typical consumer (well, maybe there aren’t buying all the meats every week, but probably every month). The prices are updated through July 2026, with the CPI data just released this morning.

While I have used average wages here, that isn’t a trick. We don’t have a median wage for 2026 yet, but using a measure of median earnings you can see that average wages and median weekly earnings increased at exactly the same rate since 1980.

But consumers probably aren’t thinking about prices relative to 1980. Their time horizon is likely shorter. What if we made the same comparison to 2026 using the 2019 prices, which is right before the pandemic and within most shopper’s recent memory:

Figure 2

Relative to 2019, things do look quite as rosy. Some items are “on sale” in terms of affordability, but a lot of items aren’t, especially a lot of proteins. And no doubt many consumers will focus on the items that are less affordable, rather than those that are more affordable (and even for these items, the nominal price is higher, so consumers might still be frustrated).

It is important to note that this basket of 24 items isn’t a perfect representation of all the items consumers purchase. Compared to the CPI “food at home” index, average wages have actually increased more since the beginning of 2019. But consumers are right to feel that beef and few other items are much less affordable than 2019, even if over the long run they are more affordable.

Figure 3

Seven-Year Grocery Inflation is Running High

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A recent poll tells us that 66 percent of Americans think groceries are unaffordable. Is this a reasonable position? Let’s add some context.

Figure 1 is one way to look at the problem. It shows the cumulative 7-year inflation rate for groceries, going back almost 100 years. 7 years is an arbitrary time period, but I think it makes sense: it can reasonably be described as “recent memory”; right now it encapsulates the period going back about 6 months before the pandemic; and it has a few time periods of around 100% grocery inflation and a few with close to 0%.

Figure 1

First things first: grocery price deflation over a 7-year time horizon is highly unusual. The only time it happened was the 1930s, a time when you had general price deflation and groceries followed that pattern. It was also a pretty bad time for the economy and society. I’m not saying you can’t have general food price deflation with a major depression, but it doesn’t show up in the historical record going back over 100 years.

Now to the present: the most recent 7 years look pretty bad. In absolute terms, 33 percent grocery inflation is above the long-run average of 25 percent, and definitely above the average of the last 40 years of 21 percent (for most adults, the past 40 years is as far back as their memory goes in terms of being acutely aware of grocery prices). Yes, there have been a few time periods with higher grocery inflation, notably the two World Wars and the 1970s.

But the really important context is the 7 years prior to the pandemic, when grocery inflation was so low (4-5 percent every 7 years) that it probably felt like 0% to most people. That was the recent experience people had become accustomed to before the pandemic. The only other time since the Great Depression it was that low was the late 1950s through the 1960s — though that 15-year window is bookended by two periods of around 100% grocery inflation!

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GDP Growth in the Second Quarter: Updated Forecasts

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GDP growth data for the second quarter of 2026 comes out tomorrow. As I have been doing for the past several quarters, here is an update on two model forecasts (Atlanta and NY Feds), betting market implied estimates (Kalshi), and an average from a survey of economists (WSJ). Yellow shading indicates which forecast was closest to correct in each quarter (green is if two forecasts were about the same).

In the past two quarters, the WSJ survey has been the best predictor. The Atlanta Fed GDPNow model used to be my favorite, but it has performed pretty poorly in the past 3 quarters. As I have discussed before, an average of the Atlanta Fed and Kalshi was better than any single predictor. I continue to include the NY Fed estimate, even though it seems to be a very terrible predictor, because some people like to talk about it.

The Atlanta Fed, Kalshi, and the WSJ survey are all showing very similar estimates for Q2. If I was a betting man, I would bet on 1.8% for the BEA advance estimate.