From my trip to Florida, I recommend Mazzaro’s Italian Market for sampling the Italian roots of the Tampa-area food scene.
And for cheap (free) thrills the St. Pete Pier is great for kids including playground splash pad and actual beach area.
A periodically recurring conversation on social media is whether imports are bad for GDP. Everyone thinks they are clearly right, and then they lazily defer to brief dismissal of the opposing view. Some of this might be due to media format. Something just a tiny bit more thorough could help to resolve the painfully unproductive online interactions… And just maybe improve understanding.
It starts with the GDP expenditure identity:
The initial assertion is that imports reduce GDP. After all, M enters the equation negatively. So, all else constant, an increase in M reduces Y. It’s plain and simple.
Many economists reply that the equation is an accounting identity and not a theory about how the world works and that the above logic is simply confusing these two things. This reply 1) allows its employers to feel smart, 2) doesn’t address the assertion, & 3) doesn’t resolve anything. In fact, this reply erects a wall of academic distinction that prevents a resolution. What a missed opportunity to perform the literal job of “public intellectual”.
Let’s add a small but important detail to the above equation to distinguish between consumption of goods produced domestically and those produced elsewhere.
Continue readingThere are 62 songs called “Better Man” just on Ultimate Guitar (which doesn’t claim to be comprehensive), plus many more slight variations like “A Better Man” or “Better Man Blues”. Some of these are obscure, but many are from well-known artists including Taylor Swift, Oasis, Ellie Goulding, Justin Bieber, and Pearl Jam; one by Robbie Williams inspired a major motion picture also called Better Man.
Meanwhile there is only one song on Ultimate Guitar called “Better Woman”, plus one variation (“A Better Woman”), both from artists I hadn’t heard of (Sera Cahoone and Beccy Cole). Why such an extreme difference?
Is it that men are the ones who are terrible and need improvement? Or are men the ones who see hope for improvement, while women can’t change or don’t want to? Let’s consider what the lyrics have to say about this. Reading though them all I saw a few recurring categories of “Better Man”:
Wish I Were Better: I count 33 of the 62 songs in this category. A man singing about how he wishes he were better, usually because of a woman, the classic “You Make Me Want to Be a Better Man“. Sometimes this is hopeful that he will be, sometimes regretful that he hasn’t been or despairing that he won’t be. Occasionally the inspiration to be better comes from someone other than a woman he’s in love with, such as Jesus, his dad, or his kids.
You Make Me Better: 13/62. Same idea as the last category, except the man has already become better. Again usually because of a woman, but sometimes because of someone else like God or his kids or his friends. Another 3 are a variation of this, I Got Better, where the man changed without anyone’s help or for a woman who isn’t convinced he really changed.
Wish You Were a Better Man: 4/62, but includes the hit by Taylor Swift. A woman wishes a man she loved were better. Another 2 songs including the Pearl Jam hit are a variant of this, Can’t Find A Better Man, where a woman stays with a bad man because she doesn’t see a better choice. Steven Seagal (yes, that Steven Seagal) reverses things and sings that a woman should leave him because she can do better. Then there’s 1 example of the genre where Hellyeah wishes his father were a better man.
One-offs: There are a few 1-off “Better Man” songs that seem to be in a category of their own: Beth Hart’s celebration of finding a better man, Ellie Goulding‘s odd insistence that “I’m the better man” (even though she’s a woman), and Ryan Innes’ entry which is the closest anyone comes to saying they wish they were a worse man. By the way, there appear to be zero songs out there called “Worse Man”- perhaps some day I’ll write one, but its a free idea and I’d be happy to see one of you beat me to it.
What about our 2 “Better Women”? Sera Cahoone’s song (the only one with the exact title “Better Woman”) is a standard “Wish I Were Better” entry, just as a woman (though the person she wants to be better for might still be a woman as usual):
So I step on up and be a better woman in your eyes
From now on I’m gonna love everything about you
Beccy Cole’s “A Better Woman” concludes that she doesn’t actually want or need to become a better woman:
I ain’t changin’ nothin’
Just to have your lovin’
Yeah, I’m alright with who I am
I don’t need to be a better woman – I just need a better man
The boring explanation for the gender discrepancy is that “Better Man” just scans better rhythmically. But I don’t think can explain a 60-2 (or 60-1 if we’re being strict) difference, and there seems the be a big underlying difference in the prevalence of these themes for men and women, not just titles. This matches up with the classic sayings from Camille Paglia:
A woman simply is, but a man must become
Or this one often attributed (probably incorrectly) to Einstein:
Women marry hoping that the man will change. Men marry hoping the woman will stay the same. Both are usually disappointed.
Whatever the cause, you can find the playlist I made of all 60 “Better Man” songs I could find on Youtube Music here:
I liked most of them (surprisingly given the range of genres and the fact that I hadn’t heard of most of the artists), but my favorite in this vein is to forget being a Better Man or Better Woman, and instead be “A Better Son/Daughter” like Rilo Kiley says:
The following chart from Arbor Research shows that the average age of cars on the road in the US is 14.5 years. If we go back to 1995, it was almost half that, and the increase has been steady since over the past 30 years. Similar data from the Bureau of Transportation Statistics confirms these numbers.
Why would this be? I see two primary explanations that are possible. One is that cars are becoming more reliable (better quality), so consumers are happy to drive them longer. The other is that cars today are less affordable, so people are only hanging onto old cars because they are forced to. One of these is a happy explanation, one is consistent with a narrative of stagnation. Which is true?
I am not a car expert, so I can’t speak to the first, though I will note that there are Facebook groups dedicated to people that have cars with hundreds of thousands of miles on their odometers.
On the affordability question, we do have some good data, but it points in the opposite direction: cars are much more affordable today than in 1995, or even before that.
Continue readingHey look, I just found a way to get infinite free electric power:
This sort of extension-cord-plugged-into-itself meme has shown up recently on the web to characterize a spate of circular financing deals in the AI space, largely involving OpenAI (parent of ChatGPT). Here is a graphic from Bloomberg which summarizes some of these activities:
Nvidia, which makes LOTS of money selling near-monopoly, in-demand GPU chips, has made investing commitments in customers or customers of their customers. Notably, Nvidia will invest up to $100 billion in Open AI, in order to help OpenAI increase their compute power. OpenAI in turn inked a $300 billion deal with Oracle, for building more data centers filled with Nvidia chips. Such deals will certainly boost the sales of their chips (and make Nvidia even more money), but they also raise a number of concerns.
First, they make it seem like there is more demand for AI than there actually is. Short seller Jim Chanos recently asked, “[Don’t] you think it’s a bit odd that when the narrative is ‘demand for compute is infinite’, the sellers keep subsidizing the buyers?” To some extent, all this churn is just Nvidia recycling its own money, as opposed to new value being created.
Second, analysts point to the destabilizing effect of these sorts of “vendor financing” arrangements. Towards the end of the great dot.com boom in the late 1990’s, hardware vendors like Cisco were making gobs of money selling server capacity to internet service providers (ISPs). In order to help the ISPs build out even faster (and purchase even more Cisco hardware), Cisco loaned money to the ISPs. But when that boom busted, and the huge overbuild in internet capacity became (to everyone’s horror) apparent, the ISPs could not pay back those loans. QQQ lost 70% of its value. Twenty-five years later, Cisco stock price has never recovered its 2000 high.
Beside taking in cash investments, OpenAI is borrowing heavily to buy its compute capacity. Since OpenAI makes no money now (and in fact loses billions a year), and (like other AI ventures) will likely not make any money for several more years, and it is locked in competition with other deep-pocketed AI ventures, there is the possibility that it could pull down the whole house of cards, as happened in 2000. Bernstein analyst Stacy Rasgon recently wrote, “[OpenAI CEO Sam Altman] has the power to crash the global economy for a decade or take us all to the promised land, and right now we don’t know which is in the cards.”
For the moment, nothing seems set to stop the tidal wave of spending on AI capabilities. Big tech is flush with cash, and is plowing it into data centers and program development. Everyone is starry-eyed with the enormous potential of AI to change, well, EVERYTHING (shades of 1999).
The financial incentives are gigantic. Big tech got big by establishing quasi-monopolies on services that consumers and businesses consider must-haves. (It is the quasi-monopoly aspect that enables the high profit margins). And it is essential to establish dominance early on. Anyone can develop a word processor or spreadsheet that does what Word or Excel do, or a search engine that does what Google does, but Microsoft and Google got there first, and preferences are sticky. So, the big guys are spending wildly, as they salivate at the prospect of having the One AI to Rule Them All.
Even apart from achieving some new monopoly, the trillions of dollars spent on data center buildout are hoped to pay out one way or the other: “The data-center boom would become the foundation of the next tech cycle, letting Amazon, Microsoft, Google, and others rent out intelligence the way they rent cloud storage now. AI agents and custom models could form the basis of steady, high-margin subscription products.”
However, if in 2-3 years it turns out that actual monetization of AI continues to be elusive, as seems quite possible, there could be a Wile E. Coyote moment in the markets:
Economist Craig Paulsson has made a simple game free to all.
When you go to MapGDP.com you will find a real picture from Google Maps and a simple question. Guess the GDP/capita in the country where this picture was taken.
Watch his YouTube introduction
See Craig’s announcement about the game on his Substack
Many economics teachers will at some point visit the topic of “what is GDP” or “economic growth.” This web game is great for both topics. I put the website on my classroom projector and called on students to take the guess. We then could do the reveal together. I rate this high value for low effort from a teacher’s perspective. No login or account creation required.
If you are an EWED reader and not an econ teacher, you might have fun playing the game yourself. Almost as satisfying as Wordle…
Today I’m just sharing a truly awe-inspiring resource. The University of Missouri has what is essentially a central clearinghouse for prices and wages. If you want the price of anything, then they should be your first stop.
See the screenshot at the bottom. The website links to the original sources for household consumption prices, occupation wages, etc. They make it easy to cut the data by date, industry, location, etc. Because they cite their sources, you can see some data series that are not even available on FRED – without having to perform the painful sleuthing on a government website.
I especially like this site for its historical data. One of the challenges of historical US data is that individual cities may not have prices that are representative of the national levels or trends. Lower levels of market integration make representative samples even more important than in modern data. But really, that was more of a concern for 20th century researchers. Now, we love our panel data. So, the historically less integrated markets of the US provide ‘toy economies’ that include greater regionalism and local shocks.
Although David Jacks has loads of tabulated data, he doesn’t have it all. The Missouri library site links to PDFs of original statistical publications which, while digitized, have never been tabulated into useable data fit for modern researchers.
Go have a look around. You won’t regret it.
https://libraryguides.missouri.edu/pricesandwages/1870-1879
Datasets can be pulled offline for all sorts of reasons. As I wrote in February, this shows the value of being a data hoarder– just downloading now any data you think you might want later:
Several major datasets produced by the federal government went offline this week…. 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.
The US Federal government shutdown this month provides another reminder of this. So far most datasets are still up, but I’ve seen some availability issues:
The good news is that a number of institutions have stepped up in 2025 to host at-risk datasets (joining those like IPUMS, NBER, and Archive.org that have been hosting datasets for many years, but are scaling up to meet the moment):
Yesterday on Twitter I shared a chart showing the age at first marriage for white men and women in the US, with data going back to 1880. I pointed out an interesting fact: at least for men, the age was essentially the same in 1890 and 1990 (27), though for women it was a bit higher in 1990 than in 1890 (by about 1 year).
This Tweet generated quite a bit of interest (over 800,000 impressions so far), and (of course!) a lot of skeptical responses. One skeptical response is that I cut off the data in 1990, when trends since then have shown continuously rising ages at first marriage, and by 2024 the comparable figures were much higher than in 1890 (by about 4 years for men and 6.5 years for women). In one sense, guilty as charged, though I only came across this data when looking through the Historical Statistics of the US, Millennial Edition, and that was the most current data available when it was printed. Here is a more updated chart from Census:
But there is another interesting fact about that data: the massive decline age of first marriage in the first half of the 20th century. Between 1890 and 1960, the median age at first marriage fell by about 3 years for men and 2 years for women. For men, most of the decline (about 2 years) had already happened by 1940. Thus, if we start from the low-point of the 1950s and 1960s (as many charts do, such as this one), it appears marriage is continuously getting less common in US history, while the fuller picture shows a U-shaped pattern.
This same pattern shows up in another measure of marriage data: the percentage of people that never get married. If we look at White, Non-Hispanic Americans in their late 40s, the picture looks something like this (keen observers will note that the Hispanic distinction is a modern one dating from the 1970s, but Census IPUMS has conveniently imputed this classification back in time based on other demographic characteristics):
Looking at people in their late 40s is useful because, at least for women, they are past their childbearing years. And using, say, the late 50s age group doesn’t alter the picture much: even though some people get married for the first time in their 50s, it’s always been a small number.
Here we can see an even more dramatic pattern. 100 years ago, it was not super rare for people to never marry: over 1/10 of the population didn’t! But by 1980 (thus, for people born in the early 1930s), it was much rarer: less than 4% of women were never married (among White, Non-Hispanics). In fact, the peak in 1920 of 10% unmarried women wasn’t surpassed again until 2013! And it’s not substantially higher today than 1920 for women, especially when considering the full swing downward. Men are quite a bit higher today, though the 1920 peak of 13% wasn’t surpassed again until 2006.
For a measure that peaks in 1920, we might wonder if new immigrants are skewing the data in some way, given that this is right at the end of about 4 decades of mass immigration. But just the opposite: if we focus on native-born women, the 1920 level was even higher at 11.1%, which wasn’t surpassed until 2022, and even in the latest figures it is less than 1 percentage point higher than 1920.
Precisely why we observed this U-shaped pattern in marriage (both first age and ever married) is debated among scholars, though my sense among the general public is that it isn’t much thought about. Most people (from my casual observation) seem to assume that marriage rates and ages were always lower in the past, and that modern times are the outliers. But in reality, the middle part of the 20th century seems to be the outlier. The “Baby Boom” of roughly 1935-1965 is possibly better understood as a “Marriage Boom,” with more babies naturally following from more and younger marriages.
This is the first of a series of occasional posts on observations of how some individual initiatives made strategic impacts on World War II. Most major decisions were made by teams of qualified engineers or military staff or whatever. But there were cases where one person’s visionary action made a material difference. There were, of course, many thousands of individual acts of initiative and heroism that went into the outcome of any given battle. However, I will focus on actions that shifted the entire capabilities of their side.
In this regard, I recently read how the intervention of President Roosevelt helped to give the U.S. nine additional aircraft carriers in the Pacific at a time when they were critically needed. As of U.S. entry into WWII in December, 1941, America had a total of 7 carriers, while Japan had 11.
It had been clear for a while that the U.S. needed more carriers, but (pre-Pearl Harbor), the Navy was more focused on building battleships; for centuries, big ships carrying big cannons were the vessels that ruled the seas. Navy brass had run studies of carrier sizing, and decided they would rather have fewer, larger carriers, due to operational efficiencies. A problem was these large carriers took years to construct.
Thus, as of 1940 the projections were that the U.S. Navy would receive no new carriers before 1944. As a naval war with Japan looked more and more likely, the President got concerned. FDR had been Assistant Secretary of the Navy during World War I, and maintained an interest in naval affairs, so he had informed judgement here. In October, 1940, Roosevelt sent a letter to the Chief of Naval Operations, expressing interest in converting merchant ships into carriers for secondary duties such as convoy escort, antisubmarine warfare, aircraft transport, and air support of landing beaches. The Navy’s response was lukewarm. In 1941, FDR proposed that some of the many cruisers under construction could be converted to small carriers. The Navy considered this, and on 13 October 1941, the General Board of the United States Navy replied that such a conversion showed too many compromises to be effective: such carriers would be less stable platforms than the big carriers, and carry less than half the number of planes per ship.
I think most presidents would have given up at this point, but not FDR. He immediately ordered another study (I assume with the implicit message, “…and this time give the boss the answer he wants”). Lo and behold, on 25 October 1941, the Navy’s Bureau of Ships reported that aircraft carriers could in fact be converted from cruiser hulls. They would be of lesser capability, but fast enough for fleet action, and available much sooner than large carriers.
The December 7, 1941 attack on Pearl Harbor changed everything. That ninety-minute raid showed that aircraft carriers were by far the most critical warships. A carrier could reach out a hundred miles and easily sink any battleship with torpedo bombers, as Japan showed on that “day of infamy” and further demonstrated by sinking British battleships near Singapore, and chasing the British navy largely out of both the Pacific and the eastern Indian Oceans. (If the brass had been paying attention, the British Navy had already used carrier-based torpedo bombers to cripple battleships at the Taranto raid and with sinking the Bismarck, well before Pearl Harbor).
The U.S. did end up converting some (slow) merchant ships to carriers, and built a huge number of small, slow, fragile “escort” carriers for transporting planes and for shore bombardment. But there was still an immediate need for better-protected small “fleet” carriers which were fast enough to keep up with the big carriers and which could survive being hit by a bomb. Japanese leaders knew they could not prevail in a long drawn-out war, so their strategy was to inflict so much damage on American military and territorial assets in the first year of conflict that the U.S. would sue for peace under Japanese terms. Japan, like Germany, was very successful at first. The Japanese overran nearly all of Southeast Asia, including the Philippines (an American possession), the Dutch East Indies (a source of rubber, petroleum, and minerals) and the British stronghold at Singapore. They came perilously close to invading Australia. So the first year or so was critical: the Allies needed to survive the onslaught from a better-prepared opponent until American mobilization took full effect.
The Navy settled on repurposing a suite of nine Cleveland class light cruisers which were under construction. These new “light carriers” could carry about 30 planes apiece, compared to a complement of around 60 planes on the full-sized ships. The smaller carriers carried fewer spares, rolled more in heavy seas, and had smaller flight decks which led to more accidents. Nevertheless, they provided a boost to U.S. naval air power at a critical time.
The U.S. entered the war with seven fleet carriers, of which six were assigned to the Pacific. In the course of 1942, four of those six fleet carriers were sunk, and the other two were severely damaged from bombs and torpedoes. Thus, there was a time in October, 1942 that the U.S. had not a single operational carrier in the Pacific, while Japan was fielding around six. That was dire.
No new U.S. carriers were commissioned until the last day of 1942 (U.S.S. Essex). That was a long dry spell. Finally, in the first six months of 1943 eight fleet carriers commissioned. Of these, three were full-sized ships, while five were the cruiser-based light carriers. That finally gave the U.S. some breathing room, which allowed it to defend its assets and pursue offensive operations. These “Independence-class” light carriers fought in many battles, sometimes providing around a quarter of the fleet airpower.
Thereafter, the astonishing mid-century American industrial capacity took over. From mid-1943 through mid-1945 another 17 fleet carriers (including four more Independence-class light carriers in the second half of 1943) poured out of U.S. shipyards, along with some 60 “escort” carriers. By late 1944, this gigantic fleet had utterly overwhelmed Japan’s navy.
But it was largely Roosevelt’s vision and repeated poking of the stodgy Navy staff that produced the first batch of light carriers which helped tipped the balance of forces during the critical first eighteen months of the war.
Economist Writing Every Day 