Author: Jeremy Horpedahl

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.

How to Escape the Productivity Slump

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I have a new essay up at Human Progress today. Here’s a slice of it:

The productivity slowdown is not an immutable law of nature. It is, at least in part, the consequence of policy choices. Human ingenuity remains as powerful as ever. We have more scientists, more capital, and better tools than any previous generation. The challenge is not generating ideas; it is allowing those ideas to spread.

…

An additional one or two percentage points of annual productivity growth may sound insignificant. Yet when compounded over decades, the effects are transformative. Higher productivity means higher incomes, better health outcomes, more abundant energy, and greater opportunities for future generations. The ideas already exist. The question is whether we will allow them to flourish.

Read the full piece.

Yes, Americans Probably Are About 46 (or Maybe 65) Times Richer Than in 1776

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My post and chart from last week showed the phenomenal growth of average income in the US since the Founding. Using GDP per capita historical estimates and adjusting for inflation, this figure is about 46 times greater today than right around the time we declared independence.

It will probably not surprise you that some folks were skeptical. Could this really be true? Two major objections were raised to using GDP per capita. First, wouldn’t it be better to use a median income value rather than a mean (simple average)? Second, wouldn’t a measure of wages be better than GDP per capita?

I really would like to show you an annual series of median income data back to 1776, but unfortunately it just doesn’t exist. Good median income data are hard to find much before the 1950s, much less the 1770s. However, while median values are often better for showing levels, the growth rates of median wages and mean wages aren’t that different for periods when we have comparable data. Consider the following chart, which compares median wages (as calculated by EPI using CPS data) and mean wages (from BLS’s series for non-supervisory workers) since 1973. I have stated these in nominal terms, so don’t take this as real growth rates, but rather it is a raw comparison of two series (we could apply the same inflation adjustment to both, but that won’t change the picture, only the numbers).

Median wages increased by 667% and mean wages increased by 657%, almost identical. Again, these aren’t inflation adjusted, but that’s not the point of this exercise. The point is that whether you use mean or median wages, at least since 1973, the growth rates are the same. Was this true if we went back another 200 years? We can’t say for sure. But many people have this same skepticism about mean wages in recent decades. I think it is better to use median values when you have them, but we shouldn’t throw up our hands and claim we know nothing if all we have is mean wages.

Next, consider the following chart. It begins in 1790, but instead of using GDP per capita, as I did last week, it uses a measure of average wages from economic historian Lawrence Officer. This measure is for “production workers in manufacturing,” and it is a total compensation measure, meaning that it will include the value of fringe benefits as well — though these aren’t noticeable in the data until the 1930s. This is still an average value, but because it is for manufacturing laborers, it won’t be distorted by the wages of managers and owners in that industry, and it won’t be affected by the growth of new industries that might require more years of education (indeed, manufacturing wages are lowering than overall average wages today, so this is taking the hard case). I have also included a second line, which only includes manufacturing wages (not benefits) that I have blended with Officer’s compensation series starting in the 1930s, in case you think including benefits is somehow “cheating.” (Note the log scale again, as in last week’s chart.)

The trends here are very much in the ballpark from the GDP per capita chart I created last week. Using total compensation, wages are 65 times higher than in 1790. Using only wages, they are 49 times higher. Notice that these are both better than the 46 times multiplier using GDP per capita. How is that possible, since I am using the same price deflator in both cases? First, average hours of work have fallen significantly since the 18th century, so incomes haven’t risen quite as much as wages. Second, there was a bit of a decline in GDP per capita during the Revolutionary War, and if we use 1790 as the baseline for GDP per capita, the multiplier is 63. But again, these numbers are all in the ballpark: whether the true figure for a typical American is 46x, 49x, 63x, or 65x, this is a tremendous amount of economic growth.

If you want to look at that chart pessimistically, you will see that there is some reduction in growth rates in the past few decades. That’s true whether we use wages or compensation. This is a well known issue, and has been discussed endlessly in academic papers and on social media. I don’t want to glaze over it here, but I mostly will: the long-run trend of growth in the US is amazing. That’s true whether you use GDP per capita, or wages or compensation for production workers.

So once again, Happy 250th Birthday to the USA and all of you living in the wake of that amazing 250 years of economic growth!