Author: Jeremy Horpedahl

What is $300,000 from “The Gilded Age” Worth Today?

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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).

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Initial Jobs Reports from BLS are Very Good At Identifying Downturns in the Labor Market

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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.

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BLS Has Been Getting Better at Estimating Jobs, and They are Not More Favorable to Democrats

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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:

  1. The BLS is getting worse at estimating jobs numbers over time, leading to larger revisions
  2. 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:

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Is Everyone Going to Europe This Summer?

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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:

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GDP Predictions: Pretty Good!

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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.

Here’s the updated table:

And here is the original post explaining the data.

Second Quarter GDP Predictions

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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.

Inflation Is Stuck

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Here’s a somewhat niche measure of inflation: 6-month CPI excluding food, shelter, and energy. It might seem like a weird measure, as it excludes over half of the CPI. But there is a logic to at least considering it along with other measures.

Food and energy are both volatile, so they can give us a lot of noise. That’s why “core CPI” and other core measures are followed closely by the Fed and inflation watchers. But excluding shelter might also make sense, because increasing housing prices are largely due to supply constraints, and will move independently of monetary policy to some extent. Six-month inflation is also useful for a more timely measure than 12 months, the headline number.

As you can see in the chart above, this niche measure of inflation has been stuck for two and a half years. It has oscillated between about 0.5% and 1.5% since December 2022. And right now it’s almost exactly in the middle of that range. It has come down from 6 months ago, but higher than 1 year ago.

As you can see in the pre-2020 years, it generally oscillated between 0% and 1%. So 6-month inflation is stuck about 0.5% higher than we had become used to, which translates into roughly 1% higher annually.

In the grand scheme of things, 1% higher inflation isn’t the end of the world. But we do seem to be stuck at a slightly elevated rate of inflation relative to the decade before 2020.

23 MSAs Produce Half of US GDP

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The 23 blue-shaded MSAs in this map produce half of US GDP:

You might be tempted to think this map, like so many maps, is just a map of US population. It kind of is, but not completely. These 23 MSAs have 133 million people (as of the 2020 Census), or about 40% of the US population. That’s a lot, but it’s much less than half, which the GDP proportion they account for. In other words, these MSAs also tend to have above-average per capita income.

The three largest MSAs by population (NY, LA, Chicago) are also the three largest by GDP. But after the first three there are some interesting discrepancies. The San Francisco MSA is the 4th largest by GDP, but only the 12th largest by population — San Fran has a population similar to the Phoenix MSA, but almost double the GDP. San Francisco MSA has a very high GDP per capita (the third highest).

The San Jose MSA is also among these 23 largest MSAs for GDP, and also sticks out — it is the 13th largest by total GDP, but only the 36th largest by population. San Jose has a population similar to Cleveland and Nashville, but well over double the GDP of these two MSAs individually. In fact, there are 12 MSAs larger in population than San Jose, but that aren’t among these 23 MSAs that produce half of US GDP: places like St. Louis, Orlando, San Antonio, Pittsburgh, and Columbus. Silicon Valley really pulls up San Jose: it has the 2nd largest GDP per capita among MSAs, only beaten by much smaller Midland, Texas and its oil income.

Here is the full list of those 23 MSAs:

  1. New York-Newark-Jersey City, NY-NJ-PA
  2. Los Angeles-Long Beach-Anaheim, CA
  3. Chicago-Naperville-Elgin, IL-IN-WI
  4. San Francisco-Oakland-Berkeley, CA
  5. Dallas-Fort Worth-Arlington, TX
  6. Washington-Arlington-Alexandria, DC-VA-MD-WV
  7. Houston-The Woodlands-Sugar Land, TX
  8. Boston-Cambridge-Newton, MA-NH
  9. Seattle-Tacoma-Bellevue, WA
  10. Atlanta-Sandy Springs-Alpharetta, GA
  11. Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
  12. Miami-Fort Lauderdale-Pompano Beach, FL
  13. San Jose-Sunnyvale-Santa Clara, CA
  14. Phoenix-Mesa-Chandler, AZ
  15. Minneapolis-St. Paul-Bloomington, MN-WI
  16. Detroit-Warren-Dearborn, MI
  17. San Diego-Chula Vista-Carlsbad, CA
  18. Denver-Aurora-Lakewood, CO
  19. Baltimore-Columbia-Towson, MD
  20. Austin-Round Rock-Georgetown, TX
  21. Charlotte-Concord-Gastonia, NC-SC
  22. Riverside-San Bernardino-Ontario, CA
  23. Tampa-St. Petersburg-Clearwater, FL

Renting an Electric Vehicle in Norway

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I recently spent a week in Norway with my family. Highly recommended overall. While we were mostly able to get around the country by train, we needed to rent a car to get to a small, remote village where my great grandfather came from, and where I still have relatives. Prior to the drilling of several massive car tunnels in the 1980s and 1990s, Fjaerland was only accessible by boat.

And if you are renting a car in Norway today, it’s highly likely you will be renting an electric car (unless you specifically ask for a gas-powered car, as the older German couple in front of me at the rental counter did). The vast majority of new cars sold in Norway (over 90%) are electric, and since most rentals are new cars, that’s what they have.

Norway has made the biggest push in the world through public policy to encourage EV adoption, both for buying cars and for building up a charging infrastructure. In this post I will primarily focus on the consumer experience of renting an EV, though the public policy surrounding it is worth a discussion too.

So how was the experience?

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Understanding Data: A Chart on Gen Z and Alcohol

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Have you seen this chart?

The chart originates from Statista, as you can see from the label in the image. But it is very frequently shared on social media, Reddit, and elsewhere (often with the Statista label clipped), occasionally generating millions of views and lots of heated comments.

But it’s a bad graph. In so many ways. Let’s break them down.

The data comes from BLS’s Consumer Expenditures Survey. I use this data frequently, as regular readers probably know. The data in the viral chart is from 2021 (more on that in a moment), but if I create a similar chart using the most recent data in 2023 but also include spending by those older than Baby Boomers (primarily the Silent Generation), you will notice a curious thing:

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