Data

Women Have Always Worked More Than Men: Hours of Work Since 1900

Jeremy Horpedahl3 Comments

This chart shows the average number of hours worked in the US, by gender, for those in their prime working ages (25-54), from 1900 to 2023. It includes both paid market work and household production (which includes activities like cooking, cleaning, shopping, and taking care of children):

Most of the data (from 1900-2005) comes from a 2009 paper by Valerie Ramy and Neville Francis, which looks at lots of trends in work and leisure in the twentieth century. I extend the data past 2005 using an update from Ramey through 2012, and then attempting to replicate their methods using the CPS (for market work) and the BLS ATUS (for home production).

A few things to notice. First, there is no data for 2020, as the ATUS didn’t publish any tables due to incomplete data from the pandemic. And even if we had data, it would have been a huge outlier year.

More importantly, there is an obvious long-term trend of declining market work and rising household production for men, and the opposite for women. In 1900 women worked over 6 times as many hours in the household as they did in the market, but by 2023 they worked almost the exact same number of hours in each sector.

Male hours in market work declined by about 16 hours per week (using 10-year averages, as there is a slight business-cycle effect on hours), but the total number of hours they worked declined much more modestly, by about 3 hours per week (note: these numbers include all men, whether they are working or not). Women saw similar changes, but in the opposite direction, with total hours worked only falling by about 4 hours per week, even though hours working at home fell by almost 22 hours.

Americans do have more leisure time than in 1900, but not dramatically so: perhaps 3-4 hours per week. This is an improvement, but less of an improvement than you might suspect by looking at hours of market work alone.

Ramey and Francis do try to carefully distinguish between household production and leisure. For example, yardwork and changing diapers are household production, while gardening and playing with your children are leisure. For some respondents to surveys, they may feel differently about whether gardening is “really” work or not, and some may enjoy changing baby’s diapers, but in general their distinctions seem reasonable to me.

Finally, we can say pretty confidently with this data that women have almost always worked more hours than men — the one exception in the 20th century being WW2 — and the gender gap was about 4 hours per week in both the early 1900s and the most recent decade (though it did fluctuate in between).

Trump’s Economic Policy Uncertainty

Zachary Bartsch3 Comments

I was on a panel of economists last night at an event titled “The Economic Consequences of President Trump”. We each gave a 5-minute summary from our area of expertise and then opened up the floor for questions.  This is a truncated summary of my talk. Since the panel included an investor, two industry economists, and another macro economist, I wanted to discuss something that was distinct from their topics. I’ve published a paper and refereed many articles concerning economic policy uncertainty (EPU) and asset volatility. I wanted to look at the data concerning President Trump – especially in contrast to Presidents Obama and Biden.

EPU matters because uncertainty can cause firms and individuals to delay investment and hiring decisions. Greater uncertainty can also cause divergent views concerning forecasted firm profitability. The result is that asset prices tend to become more volatile when EPU rises. One difficulty is that uncertainty occurs in our heads and concerns our beliefs, making it hard to measure. We try to get at it by measuring how often news media articles include the terms related to uncertainty, policy, and the economy. Since news content tends to report what is interesting, relevant, or salient to customers, there’s good reason to think that the EPU index is a decent proxy.

Using the Obama years as a baseline, the figure below simply charts out EPU. It was relatively low during Trump’s first term and then it was higher during Biden’s term – even after accounting for the Covid spike. The sharp increase toward the end is after Trump won the 2024 election. The EPU series conflicts with my perception of social media and media generally. My experience was that the media was far more attentive to the uncertainty that Trump caused. But, it may just be that the media outlets had plenty to report on rather than it being particularly indicative of EPU. After all, if the president exercises his power, then there is a certain swift decisiveness to it.

But if we look at a couple of particular policy areas, Trump’s administration faired worse. Specifically, Trump caused a ruckus concerning trade policy and immigration. Remember when Biden continued the aggressive trade policy that Trump had adopted? That’s consistent with lower EPU. Similarly, Biden made the immigration process much easier and faster while Trump’s deportation haranguing results in a somewhat stochastic means by which people are deported.  Again, that spike at the end is after Trump won the 2024 election.

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Hospitals Remain Full Even as Covid Subsides

James Bailey1 Comment

The average hospital is now 3/4 full- more full than during much of the worst of the Covid pandemic, and well above the 2/3 occupancy rate that prevailed during the 2010s. This is according to a study out yesterday in JAMA Open:

This seems to be due to a reduction in bed supply, rather than an increase in demand:

The number of staffed hospital beds declined from a prepandemic steady state of 802 000 (2009-2019 mean) to a post-PHE steady state of 674 000, whereas the mean daily census steady state remained at approximately 510 000

To me this is one more reason to reform Certificate of Need laws that put barriers in the way of hospitals opening or adding beds. Luckily I see a lot of momentum for CON reform this legislative season, including the highest-occupancy state, Rhode Island:

Forecasting the Fed: Description Vs Prescription

Zachary BartschLeave a comment

After raising rates in 2022 to belatedly combat inflation, the FOMC was feeling successful in 2024. They were holding the line and remaining steadfast while many people were getting all in a tizzy about pushing us into a recession. People had been predicting a recession since 2022, and the Fed kept the federal funds rate steady at 5.33% for an entire year. Repeatedly, in the first half of 2024, betting markets were upset that the Fed wasn’t budging. I had friends saying that the time to cut was in 2023 once they saw that Silicon Valley Bank failed. I remained sanguine that rates should not be cut.

I thought that rates should have been higher still given that the labor market was strong. But, I also didn’t think that was going to happen. My forecasts were that the Fed would continue to keep rates unchanged. At 5.33%, inflation would slowly fall and there was plenty of wiggle room for unemployment.

Then, we had a few months of lower inflation. It even went slightly negative in June 2024. Some people were starting to talk about overshooting and the impending recession. I documented my position in August of 2024. Two weeks later, Jerome Powell gave a victory lap of a speech. He said that “The time has come for policy to adjust”.  Instead of discerning whether the FOMC would cut rates, the betting markets switched to specifying whether the cut would be 0.25% or 0.5%. The Fed chose the latter, followed by two more cuts by the end of the year.

https://fred.stlouisfed.org/graph/?g=1DIsX

I was wrong about the Fed’s policy response function. But why? Was the FOMC worried about the downward employment revisions? That was big news. Did they think that they had inflation whipped? I’m not sure. There was a lot of buzz about having stuck the soft landing. In late 2024, I leaned toward the theory that the Fed was concerned about employment. Like, they thought that we had been doing better until then.

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National Survey of Children’s Health Backup

James BaileyLeave a comment

The NSCH is the latest casualty of the new administration taking down major datasets from government websites. Between Archive.org and what I had downloaded for old projects, I was able to get all the 2016-2023 topical NSCH files and post them on an Open Science Foundation page.

I took this as a chance to improve the data- the government previously only made the topical Public Use Files available in SAS and Stata formats one year at a time, so I added a merged version for all available years in both Stata and Excel formats.

I hope and expect that the National Survey Children’s Health will be back up at official websites soon. But I expect that other datasets will be taken down permanently, so now is the time to download what you think you might need and add it to your data hoard– especially if you want anything from the Department of Education.

2024 Labor Market: Not the Greatest Ever, But Pretty, Pretty Good

Jeremy Horpedahl1 Comment

At the end of 2023 I asked: was 2023 the greatest labor market in US history? I presented some data to suggest that, yes, maybe, probably, it was the greatest labor market in US history.

That post was partly inspired by critics of the unemployment rate as a broad measure of labor market utilization. Yes, the UR isn’t perfect, and it misses some things. But other measures of labor force performance tend to move with the UR, and so it’s still a useful measure. 2023 saw not only some of the lowest unemployment rates in US history (rivaling the late 1960s), but also some of the highest employment rates (only beat by the late 1990s). Wage growth was also robust. And other measures of unemployment, such as the much broader U-6 rate and the Insured Unemployment Rate, were also at record low levels (though the data doesn’t go back as far).

Today I learned about a very interesting, though I think probably confusing, measure called the “true unemployment rate.” Produced by the Ludwig Institute, it uses the same underlying data source (the CPS) that the BLS uses to calculate the unemployment rate and other measures mentioned above. This “true” rate is definitely intended to shock you: it suggests that 25 percent of the workforce is “unemployed.”

But they aren’t actually measuring unemployment. What they are doing, in a sense, is combining a very broad measure of labor underutilization (like the U-6 rate mentioned above) with a measure that is similar to the poverty rate (but not exactly). They count people as unemployed if they are part-time workers, but would like to work full-time (U-6 does this). But they also count you as unemployed if you earn under $25,000 per year. Or if you don’t work at all, you are counted as unemployed — even if you aren’t trying to find a job (such as being a student, a homemaker, disabled, etc.). The entire working age population (ages 16+, though they don’t tell us the upper limit, we can probably assume 64) is the denominator in this calculation.

So again, this is attempting to combine a broad measure of employment with a poverty measure (though here poverty is defined by your own wage, rather than your household income). So of course you will get a bigger number than the official unemployment rate (or even the U-6 rate).

But here’s the thing: even with this much broader definition, the US labor market was still at record lows in 2023! Given this new information I learned, and that we are now through 2024, I decided to update the table from my previous post:

From this updated table, we see that by almost every measure, 2023 was an excellent year for the US labor market. The only measure where it slightly lags is the prime-age employment rate, which was a bit higher in the late 1990s/2000. Real wage growth was also quite strong in 2023, despite still having some lingering high inflation from the 2021-22 surge.

How about 2024? By almost all of these measures, 2024 was slightly worse than 2023. And still, 2024 was a good year. A pretty, pretty good year for the labor market. And while the UR ticked up in the middle of the year, it has since come back down a bit and is now right at 4%. As for the “true” unemployment rate, it followed a similar pattern, ticking up a bit in mid-2024, but by December it was back slightly below the level from December 2023.

Alternative “true” measures of the economy rarely give us any additional information than the standard measures — other than a shocking, but confusing, headline number.

RGDP Underestimates Welfare

Zachary BartschLeave a comment

Like many Principles of Macroeconomics courses, mine begins with an introduction to GDP. We motivate RGDP as a measure of economic activity and NGDP as an indicator of income or total expenditures. But how does more RGDP imply that we are better off, even materially? One entirely appropriate answer is that the quantities of output are greater. Given some population, greater output means more final goods and services per person. So, our real income increases.  But what else can we say?

First, after adjusting for price changes, we can say that GDP underestimates the value that people place on goods and services that are transacted in markets. Given that 1) demand slopes down and 2) transactions are consensual, it stands to reason that everyone pays no more than their maximum value for things. This implies that people’s willingness to pay for goods surpasses their actual expenditures. Therefore, RGDP is a lower bound to the economic benefits that people enjoy. Without knowing the marginal value that people place on all quantities less than those that they actually buy, we have no idea how much more value is actually provided in our economy.

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Triumph of the Data Hoarders

James Bailey3 Comments

Several major datasets produced by the federal government went offline this week. Some, like the Behavioral Risk Factor Surveillance Survey and the American Community Survey, are now back online; probably most others will soon join them. But some datasets that the current administration considers too DEI-inflected could stay down indefinitely.

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.

External hard drives are an affordable option for anyone who wants to build up their own local data hoard going forward. The Open Science Foundation site allows you to upload datasets up to 50 GB to share publicly; that’s how I’ve been sharing cleaned-up versions of the BRFSS, state-levle NSDUH, National Health Expenditure Accounts, Statistics of US Business, and more. If you have a dataset that isn’t online anywhere, or one that you’ve cleaned or improved to the point it is better than the versions currently online, I encourage you to post it on OSF.

If you are currently looking for a federal dataset that got taken down, some good places to check are IPUMS, NBER, Archive.org, or my data page. PolicyMap has posted some of the federal datasets that seem particularly likely to stay down; if you know of other pages hosting federal datasets that have been taken down, please share them in the comments.

Was the US at Our Richest in the 1890s?

Jeremy Horpedahl2 Comments

Donald Trump has repeatedly said that the US was at our “richest” or “wealthiest” in the high-tariff period from 1870-1913, and sometimes he says more specifically in the 1890s. Is this true?

First, in terms of personal income or wealth, this is nowhere near true. I’ve looked at the purchasing power of wages in the 1890s in a prior post, and Ernie Tedeschi recently put together data on average wealth back to the 1880s. As you can probably guess, by these measures Trump is quite clearly wrong.

So what might he mean?

One possibility is tax revenue, since he often says this in the context of tariffs versus an income tax. Broadly this also can’t be true, as federal revenue was just about 3% of GDP in the 1890s, but is around 16% in recent years.

But perhaps it is true in a narrower sense, if we look at taxes collected relative to the country’s spending needs. Trump has referenced the “Great Tariff Debate of 1888” which he summarized as “the debate was: We didn’t know what to do with all of the money we were making. We were so rich.” Indeed, this characterization is not completely wrong. As economic historian and trade expert Doug Irwin has summarized the debate: “The two main political parties agreed that a significant reduction of the budget surplus was an urgent priority. The Republicans and the Democrats also agreed that a large expansion in government expenditures was undesirable.” The difference was just over how to reduce surpluses: do we lower or raise tariffs?

It does seem that in Trump’s mind being “rich” in this period was about budget surpluses. Let’s look at the data (I have truncated the y-axis so you can actually read it without the WW1 deficits distorting the picture, but they were huge: over 200% of revenues!):

It is certainly true that under parts of the high-tariff period, we did collect a lot of revenue from tariffs! In some years, federal surpluses were over 1% of GDP and 30% of revenues collected. But notice that this is not true during Trump’s favored decade, the 1890s. Following the McKinley Tariff of 1890, tariff revenue fell sharply (though probably not likely due to the tariff rates, but due to moving items like sugar to the duty-free list, as Irwin points out). The 1890s were not a decade of being “rich” with tariff revenue and surpluses.

Finally, also notice that during the 1920s the US once again had large budget surpluses. The income tax was still fairly new in the 1920s, but it raised around 40-50% of federal revenue during that decade. By the Trump standard, we (the US federal government) were once again “rich” in the 1920s — this is true even after the tax cuts of the 1920s, which eventually reduced the top rate to 25% from the high of 73% during WW1.

If we define a country as being “rich” when it runs large budget surpluses, the US was indeed rich by this standard in the 1870s and 1880s (though not the 1890s). But it was rich again by this standard in the 1920s. This is just a function of government revenue growing faster than government spending. And the growth of revenue during the 1870s and 1880s was largely driven by a rise in internal revenue — specifically, excise taxes on alcohol and tobacco (these taxes largely didn’t exist before the Civil War).

1890 was the last year of big surpluses in the nineteenth century, and in that year the federal government spent $318 million. Tariff revenue (customs) was just $230 million. There was only a surplus in that year because the federal government also collected $108 million of alcohol excise taxes and $34 million of tobacco excise taxes. In fact, throughout the period 1870-1899, tariff revenues are never enough to cover all of federal spending, though they do hit 80% in a few years (source: Historical Statistics of the US, Tables Ea584-587, Ea588-593, and Ea594-608):

One more thing: in some of these speeches, Trump blames the Great Depression on the switch from tariffs to income taxes. In addition to there really being no theory for why this would be the case, it just doesn’t line up with the facts. The 1890s were plagued by financial crises and recessions. The 1920s (the first decade of experience with the income tax) was a period of growth (a few short downturns) and as we saw above, large budget surpluses. The Great Depression had other causes.

How FRASER Enhances Economic Research and Analysis

Zachary BartschLeave a comment

Most of us know about FRED, the Federal Reserve Economic Data hosted by the Federal Reserve of St. Louis. It provides data and graphs at your fingertips. You can quickly grab a graph for a report or for a online argument. Of course, you can learn from it too. I’ve talked in the past about the Excel and Stata plugins.

But you may not know about the FRED FRASER. From their about page, “FRASER is a digital library of U.S. economic, financial, and banking history—particularly the history of the Federal Reserve System”. It’s a treasure trove of documents. Just as with any library, you’re not meant to read it all. But you can read some of it.

I can’t tell you how many times I’ve read a news story and lamented the lack of citations –  linked or unlinked.  Some journalists seem to do a google search or reddit dive and then summarize their journey. That’s sometimes helpful, but it often provides only surface level content and includes errors – much like AI. The better journalists at least talk to an expert. That is better, but authorities often repeat 2nd hand false claims too. Or, because no one has read the source material, they couch their language in unfalsifiable imprecision that merely implies a false claim.

A topical example would be the oft repeated blanket Trump-tariffs. That part is not up for dispute. Trump has been very clear about his desire for more and broader tariffs. Rather, economic news often refers back to the Smoot-Hawley tariffs of 1930 as an example of tariffs running amuck. While it is true that the 1930 tariffs applied to many items, they weren’t exactly a historical version of what Trump is currently proposing (though those details tend to change).

How do I know? Well, I looked. If you visit FRASER and search for “Smoot-Hawley”, then the tariff of 1930 is the first search result. It’s a congressional document, so it’s not an exciting read. But, you can see with your own eyes the diversity of duties that were placed on various imported goods. Since we often use the example of imported steel and since the foreign acquisition of US Steel was denied, let’s look at metals on page 20 of the 1930 act. But before we do, notice that we can link to particular pages of legislation and reports – nice! Reading the Smoot-Hawley Tariff Act’s original language, we can see the diverse duties on various metals. Here are a few:

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