2026-07-17 11:52:55
My elderly rabbit Giggles has unfortunately been quite sick the past two days (he’s recovering now), so I haven’t had as much time to write. Instead I thought I would repost a book review I did two years ago. I’ll be back with more original content tomorrow!
In my post yesterday about the “We Must Act Now” statement, I noted that Daron Acemoglu successfully got the writers to alter some key language in exchange for his signature — most likely, adding the “steering” idea that I took issue with. Technological “steering” is the central policy recommendation of Acemoglu’s 2023 book Power and Progress, with Simon Johnson.
As it happens, I wrote a review of Power and Progress, back in 2024. In fact, it was by far the longest book review I’ve ever written.1 I actually read the book cover to cover in detail, marking passages and looking up sources as I went; this took a very long time, and it was very frustrating, since I had serious objections to nearly every part of the book. I took out that frustration by writing a very long and highly critical review.2
So anyway, here’s that review for you to enjoy. Hopefully it gets a few people to think a little harder about the AI-related ideas that Acemoglu has been promoting throughout the econ world, and why it would be a big mistake to make those ideas the default position of the economics profession on the AI issue.
“Do not be fooled by the monumental technological achievements of humankind.” — Acemoglu and Johnson
It’s hardly surprising that Power and Progress made it onto practically every list of the most important business books of 2023. First, there’s the unrivaled pedigree of the authors themselves. To call Daron Acemoglu a powerhouse in the world of economics would be a ludicrous understatement:
Acemoglu is also the main proponent of the institutional explanation for national development, through his famous book Why Nations Fail and its sequel, The Narrow Corridor (both with James Robinson). If you hear me talk about “inclusive institutions” and “extractive institutions”, I’m channeling Acemoglu.
Simon Johnson, meanwhile, is the author of some of my favorite popular books about economic policy, especially Jump-Starting America (with Jonathan Gruber) and 13 Bankers (with James Kwak). When I write more about the need to spend more on science and to restrain the excesses of the finance industry, I’m channeling Johnson.
The second reason this book was destined to garner attention is that it brings together two extremely timely strains of thought: 1) the widespread distrust of tech companies that has grown in American society over the last few years, and 2) the wave of anxiety over AI-driven automation. Power and Progress weaves those two anxieties into a more-or-less coherent whole — a sum of all technological fears, if you will. And it seems to have been spectacularly well-timed, since its release coincided closely with the coming of ChatGPT and other generative AI.
But given all of those powerful tailwinds, I have to say I’m kind of surprised at how little of a splash Power and Progress seems to have made. This is anecdotal of course, but in the 9 months since it came out, I’m not sure I’ve once heard someone reference the book or any idea in it. The authors clearly intended it to be a handbook for people who are scared about AI putting humans out of a job, the way Thomas Piketty’s Capital in the Twenty-First Century became a handbook for people worried about inequality, or Robert Gordon’s The Rise and Fall of American Growth became a handbook for people concerned about technological stagnation. But I don’t think it did.
Why not? One reason might be that the timing wasn’t as favorable as it might appear. Contrary to Acemoglu and Johnson’s assertion (on p.24 of the hardcover edition) that we live in an age of “blind techno-optimism”, the internet is absolutely chock-full of arguments and warnings about the downsides of AI. Concerns over the risk of rogue Artificial General Intelligence resulted in a boardroom coup attempt that almost drove Sam Altman out of OpenAI. Worries that AI wouldn’t uphold diversity led Google to implement some pretty hilarious countermeasures. Fears of mass surveillance, deepfakes, etc. are widespread. And of course the idea that AI is going to lead to mass unemployment is absolutely ubiquitous — so much so that practically every San Francisco tech event I go to features discussions about exactly this subject. Yes, even dance parties.
In other words, Power and Progress may have come out a little too late to make a big splash, and instead ended up just being one more voice shouting in the chorus.
On top of that, though, I have to say that this book…well, I just don’t think it’s very good. I winced while I wrote that sentence, because Simon Johnson is a personal friend, and Acemoglu is a celebrated genius, and because both of them have written such good books in the past. This is the first broadly negative book review I’ve written since 2014, and I’m a lot less combative of a blogger than I was a decade ago. I did not want to pan this book, especially because I think the topic is a good and important one, and I think the authors are brilliant people whose hearts are in the right place.
But I just don’t think the way this book was written ends up supporting the conclusions it draws. The historical examples it cites simply don’t support a narrative of out-of-touch technologists inventing the wrong sorts of technologies and hurting workers in the process. The book embraces a highly questionable definition of “power” in which persuasion in an open democratic society is painted as a threat. It often seems to assume its conclusions about the impacts of specific technologies, and it tells a jumbled and confusing story about the role of productivity growth. And its central claim — that society can push entrepreneurs to steer innovation in a direction that augments humans instead of replacing them — is not well-supported.
All in all, Power and Progress just fails to convince.
Power and Progress is of the “magisterial sweeping tome” class of econ book, like Capital in the Twenty-First Century, The Rise and Fall of American Growth, or Brad DeLong’s Slouching Toward Utopia. Much of the book is a history of technological innovation in general. As such, it tends to ramble; the authors often seem to get so caught up in the telling of this history that they neglect to tie each event to their central theses. In fact, those are often the most fun and fascinating parts of the book. But if I were to boil down Power and Progress to a set of core ideas, it would be:
Technological innovation’s impact on human welfare depends crucially on social choices about how those innovations are used.
Those choices are determined by the relations of power in a society, and in recent decades our choices have been steered in a negative direction by the power of tech company founders and venture capitalists.
The type of technologies that society invents can be chosen so as to distribute benefits more widely, by avoiding technologies that replace workers and inventing technologies that complement workers.
It’s the last of these that the book is most known for, because it’s the boldest, the most original, and the most controversial. But first let’s talk a bit about the other two.
The idea that technology’s impact on society is not determined solely by the nature of the tech itself, but depends on how we choose to use it, is obvious enough to be a truism. Everyone knows how the industrial technologies that have created so much wealth are also put to destructive uses in wars. Everyone knows that the same camera technology that lets you talk to your friend in a different city can allow governments to spy on their citizens. Everyone knows that there is a vast system of laws, international agreements, and social norms whose purpose, at least in theory, is to ensure that technology is used for good and not for ill.
But even though “technology can be used for bad purposes” should be a simple truism, Acemoglu and Johnson pick some very odd examples to illustrate the principle. For example, in the prologue, they have a list of what they claim are “new inventions that brought nothing like shared prosperity”. Here’s the fifth item on their list:
At the end of the nineteenth century, German chemist Fritz Haber developed artificial fertilizers that boosted agricultural yields. Subsequently, Haber and other scientists used the same ideas to design chemical weapons that killed and maimed hundreds of thousands on World War I battlefields.
The idea that the Haber-Bosch process has “brought nothing like shared prosperity” is an absolutely wild claim. Nitrogen fertilizers are so important to human existence that by the most common estimates, about half of the entire population of Earth — 3.5 billion people — is only sustained thanks to this technology. But because that same chemical reaction was used to create one particular type of chemical weapon that was responsible for a tiny fraction of the deaths in one particular war, Acemoglu and Johnson feel comfortable saying that a technology that literally gives life to half of humanity “brought nothing like shared prosperity”. It is the kind of claim that is so obviously wrong as to leave the reader slack-jawed — and yet it is deployed in support of an overall thesis for which countless better examples exist.
Unfortunately, this kind of questionable selection of historical examples is a hallmark of Power and Progress all the way through. For example, in Chapter 6, the authors write:
[Belief in the power of productivity] suggests that as technology advanced rapidly during the early phases of the Industrial Revolution, wages should have risen. Instead, real incomes of the majority stagnated.
Acemoglu and Johnson conclude that because textile manufacturing technologies were biased toward automating workers, they immiserated the working class of 1700s Britain. But those same textile manufacturing technologies have been at the center of the early stage of every other country’s industrialization as well. China went through a period where it made most of the world’s clothes, with its share peaking in the late 2000s. In 1995, apparel was China’s biggest export category.
But during this time, when Chinese garment workers were getting the descendants of the original British industrial technologies of power looms, their wages were skyrocketing — as were wages in the economy as a whole. The same is now true of Bangladesh — the country focuses relentlessly on the garment industry, and has access to all of the old automation technologies, and yet incomes in the country have tripled since 1990.
(As a side note, it’s kind of funny that after we’ve used “Luddite” as a slur for technophobes for all these years, Acemoglu and Johnson explicitly try to rehabilitate the original Luddites, writing that they “were right to worry about knitting frames decimating their livelihoods”. For this reason, I considered subtitling this review “the Bible of the Luddites”, but decided that the negative connotation of the word was too strong and it would be rude.)
A third questionable example in the book is the story of the Panama Canal. Acemoglu and Johnson describe the brutal exploitation of the workers who built the canal, and declare the project a “colossal failure”. That brutality was certainly real. But the authors cite it as a reason that the technology of the canal itself failed to bring broad-based prosperity. In fact, the opposite seems true; thanks to the canal, the people of Panama today enjoy a standard of living much, much higher than that of their Central American neighbors. This is not to say those economic benefits were worth the human cost. But the canal’s problems clearly seem associated with its construction, rather than unfair distribution of the benefits from the technology itself. Could the same canal have been built using more humane labor standards? The authors decline to speculate, simply declaring the whole project a failure and not even mentioning Panama’s prosperity.
A fourth dodgy example is the story they tell about Japan. In Chapter 8, Acemoglu and Johnson praise Japanese companies for “combin[ing] automation with the creation of new tasks”, noting that Japanese automakers didn’t reduce their workforces like American automakers did. But Japan’s manufacturing sector wages, like wages throughout the rest of the country, have been falling since the early 1990s, while American wages have stagnated but not fallen. So this story doesn’t fit the data.
A fifth example is in Chapter 7, when Acemoglu and Johnson write that “Henry Ford was a pioneer” in developing “a more cooperative relationship” with his workforce. I’m just wondering how this “more cooperative relationship” involved hiring thugs to gun down union organizers. Ford did pay higher wages to increase efficiency, but his actual dealings with representatives of labor was brutal and intolerant.
I could go on citing these questionable examples — my copy of Power and Progress is stained blue with all the notes I made in the margins — but this review would run into the dozens of pages, and you would quit long before you finished. But because there are so many questionable examples, Power and Progress is the kind of book that must be read closely and with a critical eye.
Update: In the comments, Brian Potter adds:
Another strike against this book is its terrible scholarship: it frequently gets basic facts incorrect because the authors haven't bothered to actually research the topic. Example: it claims at several points that Eli Whitney was responsible for the development of interchangeable parts, a claim that has been widely and thoroughly debunked.
Another issue with the book’s examples is the lack of footnotes or endnotes. Instead of citing specific works in support of each specific claim — as most books do — Power and Progress has a bibliographic essay at the end. Many sources are mentioned in this essay, but it’s often difficult, and sometimes impossible, to match the sources to specific claims. As a result, you often end up having to choose between exhaustively searching multiple sources to figure out where the authors got a particular point, or simply giving up and trusting that the authors are accurately representing the data.
For example, in Chapter 1 the authors ask “What if…AI also impoverishes billions in the developing world?”, and asserts that “evidence is mounting” that this concern is “valid”. But where is the evidence that AI threatens to impoverish billions? That’s an astonishingly strong claim about a technology about which little is known, and I can’t find any source in the bibliographic essay. An empirical study I do know is Acemoglu, Autor, Hazell and Restrepo’s 2022 paper “AI and Jobs: Evidence from Online Vacancies”, whose abstract concludes:
We find no discernible relationship between AI exposure and employment or wage growth at the occupation or industry level, however, implying that AI is currently substituting for humans in a subset of tasks but it is not yet having detectable aggregate labor market consequences.
So that paper certainly doesn’t include mounting evidence that AI threatens to impoverish billions. But I can’t find which paper the authors relied on to make this claim.
In fact, because I’ve read many of the Acemoglu papers that undergird this book, I also know that there are instances where the data doesn’t quite say what the authors claim. For example, in Chapter 8, Acemoglu and Johnson argue that “digital technologies became the graveyard of shared prosperity” over the last few decades. In this chapter, they attribute some meaningful piece of the recent rise in inequality to the introduction of digital technologies to the workplace. But because I’ve read Acemoglu and Restrepo’s 2020 paper “Robots and Jobs: Evidence from U.S. Labor Markets”, as well as the working paper version from 2017, I know to be skeptical of this claim.
Acemoglu and Restrepo found that a narrow category of automation — industrial robots — was associated with decreased employment and wages. But as the Economic Policy Institute’s Larry Mishel and Josh Bivens noted, when Acemoglu and Restrepo measured the effect of workers’ “exposure to IT capital” in general — i.e., how much the employers invested in IT tech overall — they found either no effect or a positive effect on employment and wages. Here’s the relevant table from the 2020 version of the paper:
The estimates of a positive impact of IT capital on employment and wages are in the original working paper version, in table A9.
Now, this doesn’t mean that computers and the internet weren’t a driver of mass unemployment or stagnant wages. Maybe they were! Acemoglu and Restrepo (2020) could simply be wrong; in fact, since their working paper first came out in 2018, many other studies have ended up contradicting their findings about the negative impacts of robots. And all of these papers look within specific industries or companies — as does Acemoglu and Restrepo’s 2022 follow-up paper about automation and inequality between demographic groups. The overall effect of automation on economic growth, absolute wages, and the composition of industries in the economy simply isn’t known.
In other words, it could be very well be that automation has been impoverishing people, or it could be that it has been enriching people overall. I’d simply like to know where the authors get the data to back up their claim about information technology, especially when one of the authors’ most famous papers appears to contradict that claim.
To sum up, footnotes and endnotes are a technology that has unambiguously benefitted the world, and even though they can be a bit of a pain the butt, authors should include them.
Anyway I digress; back to the book’s central theses.
Despite the questionable examples, it’s clearly true that technology can be used to benefit average people or to hurt them. But how does society choose how to use technologies? Acemoglu and Johnson’s answer is “power”, from which they get the title of their book. But what is power? Here, in Chapter 3, Acemoglu and Johnson deploy a definition that veers into the tautological:
Power is about the ability of an individual or group to achieve explicit or implicit objectives. If two people want the same loaf of bread, power determines who will get it.
Using this definition, how could we ever conclude that power wasn’t the reason for an observed outcome? Two people want a loaf of bread, and one of them gets it; we know this was due to “power”, because “power” is defined by who gets a loaf of bread. This kind of definition is semantically valid, but empirically useless; if you define “power” such that it simply means “whatever caused an outcome to happen”, you haven’t isolated causality, you have simply given it a new name.
Acemoglu and Johnson have a reason for employing a definition this infinitely broad; it allows them to include persuasion and compulsion in a single category of “power”.
The authors’ historical examples of when power determined the distribution of the benefits of technology include cases when laws and the threat of violence allowed some people to extract the benefits of technology for themselves — the cotton gin increasing slaveowners’ profits in the American South, or lords extracting agricultural surplus from peasants in medieval Britain. These are instances of compulsion, which certainly fit with our common, everyday, colloquial use of the word “power”.
But Acemoglu and Johnson also spend a lot of time arguing that persuasion is also a form of power. They cite instances in which techno-optimists and businesspeople in 18th century England and 21st century America persuaded the public to enact pro-business policies, through articles, speeches, conversations, and so on. Their explanation for why inequality has increased since the 1970s is, in effect, that silver-tongued technologists managed to persuade American society to weaken pro-worker institutions, and to allow the technologists to invent technologies that replaced human labor instead of complementing it.
The authors don’t venture to say exactly why these techno-optimists’ pro-business vision prevailed — they write that “an idea is more likely to spread if it is simple, is backed by a nice story, and has a ring of truth to it,” but they admit that “quite a bit of this process [of persuasion] is random,” and declare that “you are enormously lucky if you get the right idea, with just the right ring to it, at just the right time.”
I have to admit, this kind of surprised me. I expected to see some sort of pseudo-Gramscian theory of cultural hegemony (or at least some references to Gramsci or similar writers). Instead, the authors just sort of shrug and put it all down to luck. For some reason, the techbros just wrote really good posts, and by doing so they ruled the world — at least until their luck ran out and the world turned against them, I suppose.
In fact, I have to confess that the entire chapter on power and persuasion left me bewildered. I do not understand why we should put accidental success in a nonviolent marketplace of ideas in the same conceptual category as chattel slavery and feudalism. It seems to yield neither understanding nor solutions. Perhaps the historical example of the cotton gin might give us some insight about how to explain the spread of laissez-faire economics, but simply labeling both things as varieties of “power” does not yield that insight. And the idea that persuasion is power doesn’t seem to suggest any kind of systemic fix for the problem that sometimes society is persuaded to do things that increase inequality.
That doesn’t mean Acemoglu and Johnson have no solutions to recommend, though. They want to strengthen institutions like unions and labor laws, but their main idea is to redirect technological innovation toward technologies that complement workers instead of replacing them.
“With such persuasiveness, you tend to convince yourself that you are correct.” — Acemoglu and Johnson
Over the last six years, Acemoglu and Restrepo wrote a series of theoretical papers in which they lay out a number of different ways that new technology can affect workers’ jobs and wages. Basically, this ends up being a fancier version of a very old idea — capital can either substitute for labor or complement it. If capital substitutes for labor, then capitalists win, because they can replace people with machines, and pay people accordingly less. But if capital complements labor, then workers win, because capitalists have no choice but to hire them to work the new machines, and pay them good wages. Acemoglu and Restrepo make this theoretical breakdown a bit more nuanced, but in the end it really boils down to whether machines replace people or augment their abilities (either by making them more productive or by giving them new things to do).
Power and Progress attempts to analyze the history of technology through the lens of this theory. In eras where technology seemed to progress rapidly but workers’ wages didn’t grow much, like 18th century Britain, Acemoglu and Johnson argue that the main cause was technologists inventing machines that replaced human labor; in other eras where wages grew rapidly, like the late 19th century, they argue that technologists were inventing machines that created new tasks for humans to do.
At times this can begin to feel like a just-so story. For example, Acemoglu and Johnson cite electricity as a technology that was good for workers, because it created so many new industries for people to work in. But didn’t electrification also replace human labor in quite a large variety of ways? Electric lights save us the labor of making candles, electric dishwashers and washing machines and dryers automate our housework, and so on. How do we know this task automation outweighed by the new tasks electricity creates?
They also argue that wages in industrial Britain began to increase in the late 19th century because steamships and telegraphs — as opposed to looms — “expand[ed] the set of tasks and opportunities for workers”. As far as I can tell, this is a claim without evidence. Why was the telegraph’s automation of message couriers less significant than its creation of new jobs for telegraph operators? In Chapter 8, Acemoglu and Johnson blame communication technology for increasing inequality by enabling the offshoring of jobs to China and other countries. Why would modern communication technologies have exactly the opposite effect of the telegraph?
And in Chapter 6, Acemoglu and Johnson praise the early United States for its direction of innovation, writing that American businesses compensated for a lack of skilled labor by, in the words of engineer Joseph Whitworth, “call[ing] in the aid of machinery in almost every department of industry.” In Chapter 7 the authors write that the American use of interchangeable parts “was first and foremost an effort to simplify the production process so that workers lacking in artisanal skills could produce high-quality products.” But how is that any different from the British use of power tools to help unskilled make textiles in the 1700s? The difference is never explained.
On the other side of the coin, Acemoglu and Johnson cite most modern information technology as something that automates more tasks than it creates. But what about all the new tasks that IT creates — mobile developers, web designers, digital media marketers, content moderators, and so on and so forth? Why are these less economically important than the tasks that the internet automates away (encyclopedia salespeople, etc.)?
One answer is that if we assume that Acemoglu’s theory of automation is the main thing that’s going on, we can just infer the effects of particular technologies from macroeconomic outcomes — if we see wages stagnate, it must be because task automation outweighed task creation. But for anyone who suspects that Acemoglu’s theory might not actually be the main thing going on in the economy, just saying that the proof is in the pudding is a bit unsatisfying. It feels like a just-so story.
In fact, the historical examples in Power and Progress leave themselves open to alternate narratives. The main alternative narrative is about productivity.
Acemoglu and Johnson repeatedly argue that if productivity gains are produced by automation, workers don’t see the benefits. They cite the idea that productivity naturally uplifts workers — which they call the “productivity bandwagon” — as one of the main nefarious narratives that technologists use to persuade society to allow them to invent technologies that replace workers.
But what if the “productivity bandwagon” narrative is true?
There are two main historical periods the authors cite as examples of excessive automation leading to stagnant wages — the early Industrial Revolution in 18th century Britain, where textile machines like looms replaced human artisans, and America since the 1970s. (Note: they make an error when they say, in Chapter 8, that “declines in real wages…have been a major part of U.S. labor market trends.” In fact, when you include benefits, real hourly compensation has grown a bit more slowly since 1973, but has still consistently risen.)
But Acemoglu and Johnson also note that both of these eras had sluggish productivity growth. Perhaps wages were stagnant in those eras because productivity was also stagnant?
Regarding the early Industrial Revolution, some researchers argue that the labor share didn’t actually fall. For example, here’s Crafts (2020):
[R]eal wages grew more slowly than real GDP per worker during the industrial revolution. However, the discrepancy was much less than has been claimed such that in 1820 the former had risen by about 12 per cent since 1770 and the latter by about 16 per cent. Second, labour productivity grew quite slowly prior to 1830 averaging a little below 0.4 per cent per year in the 60 years after 1770. Nevertheless, in the context of demographic pressure this was a very good outcome by pre-industrial standards. Third, as relative prices changed and exportable manufactures became cheaper, over the long run real product wages grew somewhat faster than real consumption earnings. Fourth, the share of profits in GDP rose over time from 17.2 per cent in 1770 to 31.3 per cent in 1860 but this was associated with a decline in the share of land rents and the share of labour was little changed. Fifth, looked at through the lens of growth accounting the evidence is of total factor productivity (TFP) growth accelerating only gradually to 0.6-0.8 per cent per year during 1830 to 1860 with the steam age only materializing after 1830.
In sum, this looks more like a story of paradoxically slow productivity growth than of pro-rich growth. The story of the industrial revolution is definitely not one of a new general-purpose technology boosting productivity growth at the expense of a big shift in the distribution of income which is the current fear about AI.
As for the U.S. since the 1970s, inequality has definitely increased, but — contrary to what you may have heard — pay has largely kept pace with productivity. Automation may have made wages more unequal (this is the argument of Acemoglu and Restrepo’s 2022 paper), but the modest decline in labor’s share of the national pie was probably mostly about land values increasing. Which means that aggregate wage stagnation was largely due to slowing productivity growth in recent years as well.
In fact, Acemoglu and Johnson also blame automation for stagnating productivity! In Chapter 8, they write that “productivity gains from automation may always be somewhat limited.” They coin the term “so-so automation” to describe technologies that take humans out of the loop but fail to increase productivity much by doing so. They argue that technologies that make better use of human capabilities lead to faster productivity growth as well as higher wages and lower inequality.
OK so…why isn’t that the book’s central this? You could write a very interesting book about how technologies that complement humans are better for both productivity and broad-based prosperity than technologies that try to substitute for humans wholesale. I would definitely read that book! But Acemoglu and Johnson did not choose to write that book; instead, they warn against a focus on productivity, claiming that it’s a seductive but dangerous narrative used by the greedy, fast-talking techbros. In my mind, this weakens their overall narrative.
Throughout Power and Progress, the authors tell a story about a “menu of technologies” that entrepreneurs can choose from. On one hand, companies can choose to invest in automation, replacing workers, increasing inequality, causing slow wage growth, and maybe reducing productivity in the process. On the other hand, they can choose to invest in technologies that create new tasks for humans to do, thus increasing wages and decreasing inequality. Their story is that out of greed and/or elitism, entrepreneurs often choose the former, so it’s in the interests of society to push them toward choosing the latter.
But when the authors approvingly cite examples of new industries springing into being, they never give an explanation of why entrepreneurs and technologists chose to create these new industries, instead of trying to cut costs in existing industries. My default assumption would be that the people who invented and commercialized steamships, telegraphy, interchangeable parts, autos, electricity, and telephones were driven by the same sort of motivations that animated the people who invented and commercialized power looms, computers, and the internet. If not, why not? Was there ever a case when governments or unions pushed entrepreneurs to select Option A from the “menu of technologies” instead of Option B?
When Acemoglu and Johnson do discuss union power, it’s in the context of worker training. In Chapter 7, they write:
In fact, for unions [in the 1960s] the central issue was worker training. They insisted on training provisions to ensure that workers could be brought up to the necessary skill level to operate the new machinery and benefit from it.
This is very different from affecting the direction of innovation! This is a case of workers collectively pushing companies to invest in human capital, so that worker skills can catch up to the direction in which innovation was already going.
As far as I can tell, this book does not contain even one single example of when a union or government supposedly pushed an entrepreneur or company to choose a different path of technology in order to benefit workers. As far as I can tell, it does not even contain one single example of when an engineer, entrepreneur, company or investor chose to create a technology in order to benefit workers more.
In other words, there is no evidence here that the “menu of technologies” actually exists. It’s not clear that technologists and industrialists even know in advance whether the inventions they create and commercialize will create more new tasks than they automate. And this raises pointed and troubling questions for the authors’ preferred solution to the problems of inequality and wage stagnation.
In Chapter 11, Acemoglu and Johnson roll out their proposed solutions. Having concluded that inequality and wage stagnation are due to “tech billionaires and their agenda” choosing the wrong technologies from the “menu”, they call for democratic people-power to force the techbros back onto the labor-augmenting path.
What’s totally unclear is how to do this. Acemoglu and Johnson admit that “redirecting” the path of technological innovation is going to be an incredibly tall order:
Determining how different digital technologies are used and their impact on wages, inequality, and surveillance is much harder [than assessing their climate impacts]…Moreover, given the difficulty of distinguishing automation from other uses of digital technologies, automation taxes are currently not practical.
And yet the authors still claim that this can be done! Yet they’re maddeningly vague on the details:
There is a telltale sign of automation technologies: reducing the labor share of value added, meaning that once these technologies are introduced, how much of value added goes to capital increases and how much gets captured by labor decreases…On this basis, technologies that increase the labor share can be encouraged via subsidies for their use and their development.
But how do we know in advance, before a technology is invented, whether it will increase or decrease the labor share? This is just replacing one target of guesswork — new task creation vs. automation — with another target of guesswork.
Fundamentally, it still boils down to some sort of mandarins in a room somewhere — economists? government engineers? bloggers? — trying to assess the economic effects of a technology that doesn’t even exist yet.
As I wrote in a post last June, this is probably an impossible task. Some of the world’s top experts thought that AI would replace radiologists within a few years, but demand for radiologists boomed even as the new AI tools were coming online. The technologists got it wrong.
And the economists are just as likely to get it wrong. For example, industrial robots are the one technology that Acemoglu consistently rails against as an example of harmful automation. That’s based on his 2020 paper with Restrepo, where they found that companies that buy more robots employ fewer humans. But a whole bunch of other economists followed up on this research and found the exact opposite — robot adoption is correlated with more jobs at a company or in an industry. Here was a list I made back in 2022:
I’ll list a few of these studies:
1. Mann and Püttmann (2018) — Where Acemoglu and Restrepo…looked at correlation, this paper attempts to identify causation. They look at automation-related patents in an industry — a proxy for innovation in the automation space — and then look to see whether that industry gains or loses jobs. They find that “advances in national automation technology have a positive influence on employment in local labor markets”, though this isn’t true for every area.
2. Dixon, Hong and Wu (2021) — These authors looked at robot adoption in Canada, at the level of the individual company (or “firm”, as economists say). They found that companies that adopted more robots hired more people, while also improving the quality of their products and services.
3. Koch, Manuylov and Smolka (2019) — This paper looks at firm-level data for manufacturing companies in Spain. They find that robot adoption is associated with a substantial increase in employment as well as output.
4. Adachi, Kawaguchi and Saito (2020) — This paper finds the same thing as the previous one, but for Japanese companies over the course of a 40-year period.
5. Eggleston, Lee and Iizuka (2021) — These authors look at robot adoption by nursing homes in Japan, and find that it strongly increases employment, although it does result in existing nurses working fewer hours (and thus getting paid less).
6. Hirvonen, Stenhammar, and Tuhkuri (2022) — This paper looks at a technology subsidy program in Finland that increased adoption of a broad range of advanced technologies at Finnish firms. They find that this led to employment increases.
In fact, by this point the trend is clear. Essentially everyone is finding that, contra Acemoglu and Restrepo…robots are correlated with — and probably cause — higher employment in the companies and areas where they’re adopted.
What’s happening is that companies that use more robots hire more humans (and retain their existing humans) in jobs that complement the robots. That’s exactly what we saw with previous waves of automation — people find new roles, robots increase their productivity, and they get paid more. Looking at the countries that use the most robots in their manufacturing industry, it seems likely that this virtuous cycle is happening even at the level of whole nations.
Zooming out from just the manufacturing sector, Hötte, Somers, and Theodorakopoulos have a very interesting 2022 review paper in which they look at the literature on the entire range of automation technologies. Here’s an article in which they explain their results. Hötte et al. find that automation does replace jobs, but that this effect is outweighed by the “reinstatement” effect — in other words, people find new jobs to do. And their incomes generally rise as a result.
So no, there’s no possibility that a council of mandarins — engineers, economists, or whoever — can sit there evaluating every potential new technology that companies or inventors want to create, and deciding whether it will raise or lower the labor share. I mean, you could make a council of mandarins, and it could look at plans for new technologies, and it could issue decisions, but in practice it would be throwing darts at a dartboard. And it would be an incredibly costly tax on our companies, since it would introduce massive delays into their decision-making process. Their Chinese rivals, on the other hand, would suffer no such delays.
In other words, I see no hope for Acemoglu and Johnson’s preferred solution. The utter vagueness with which the idea is presented in Power and Progress doesn’t suggest that the authors have thought carefully about how this solution might work in practice.
In particular, these solutions seem inferior to something far simpler: policies to increase labor share ex post. Labor market institutions like co-determination and sectoral bargaining, and direct interventions like wage subsidies funded by taxes on capital income, can push up the labor share without requiring panels of experts to predict the unpredictable. And if entrepreneurs really do have any degree of foresight about whether their innovations will tend to push the labor share up or down, these policies will act like a Pigouvian tax on the kind of cost-cutting that Acemoglu and Johnson decry. With a wage subsidy, for example, the higher the market rate you can afford to pay your workers, the more of a rebate you can get from the government. So if there are technologies that augment your workers and let you hire new workers, a wage subsidy gives you an incentive to create them.
Anyway, I think simple policies like these should be economists’ first go-to solutions, rather than the creation of whole new social institutions.
At the start of this review, I talked about how Power and Progress may have missed its moment by getting lost in a flood of fears about AI. But there’s another way in which the book might be poorly timed. Wage inequality — the very thing that Acemoglu and Restrepo (2022) try to explain — has flatlined since the early 2010s.
Meanwhile, real wages have been rising strongly for years now, interrupted only by the post-pandemic inflation. And wages for production and nonsupervisory workers have risen more robustly than those for managerial workers:
Meanwhile, employment for prime-age Americans is near all-time highs, and unemployment is at record lows; everyone who wants a job in America has one.
All this has happened in exactly the time frame during which AI has exploded. Predictive AI burst onto the national scene in 2012 with the ImageNet paper, the basic technology for generative AI was created in 2017 with the transformer paper, and generative AI became really widespread in 2022-23 with LLMs and AI art programs. To reiterate: essentially all of the commercialization and implementation of artificial intelligence has happened during a time in which wages have been rising, inequality has been flat or falling, and employment has been high.
Maybe AI just isn’t big enough to kill all the jobs yet; maybe we just have to wait a few years and we’ll all be unemployed or working for pennies. Or maybe AI is actually the kind of technology that improves task productivity and creates new tasks instead of automating old tasks away. Or maybe Acemoglu has something wrong in his models, and further theoretical and empirical explorations will overturn his conclusions about the key role of automation in fostering inequality. I’m not sure.
Whatever is going on, though, I think it should give the AI worriers pause. This was not on the menu. If you went back to 2012, and asked people to predict the impact of a new machine that could recognize objects and imitate speech and create beautiful art, they probably would have assumed that the rising inequality that they had experienced for the last 30 years would now be turbocharged. And — at least so far — they’d have been completely wrong.
To me, that thought experiment illustrates the folly of trying to predict the economic effects of new technology. It also suggests another reason why Power and Progress didn’t make the same kind of splash it might have made in 2018. Obviously, our economic problems haven’t all been solved. But perhaps, underneath all of the anger and pessimism, Americans realize that something has shifted in their economy, for the better. And perhaps that’s making them a bit less interested in the kind of pessimistic economic narratives that flew off the shelves in the 2010s.
It was about 7000 words, which is 2000 words longer than the review of The Courage to Act that I wrote for International Finance.
I also paywalled it, which was probably a stupid decision. Book reviews, especially highly critical ones, shouldn’t be paywalled; they should be disseminated as widely as possible, in order to reach as many of the book’s readers as possible.
2026-07-15 16:19:54
“We must do something. This is something. Therefore, we must do this.” — the Politician’s Fallacy
The other day, a friend asked me to add my signature to a statement called “We Must Act Now: A Statement on AI’s Transformation of the Economy”. A bunch of economists, including many famous and influential ones, have been signing it. Here’s the text of the statement:
AI may become radically more powerful over the next 10 years.
This could drive an unprecedented transformation of our economy, larger than the Industrial Revolution, but unfolding over a vastly shorter time frame. It could bring risks, including large-scale job displacement, as well as opportunities such as major gains in living standards.
Economists, policymakers and technology leaders must act now to understand the economics of transformative AI and to build the incentives, guardrails, and institutions needed to steer AI in a direction that complements humans and benefits society.
That’s it. That’s all it is. It doesn’t say what our action ought to be, only that “we must act”. There’s no appendix, no longer manifesto attached below. It just says AI is getting good, AI could be economically important, AI could take people’s jobs and/or make us a lot richer, and that we have to do something to make sure the AI age turns out alright.
But what is that something? What actual policies would I be recommending by signing this statement? None that I can see. It’s completely vague and unspecific.
This might seem like it makes the statement innocuous and bland (so why not sign it?). At some point, however, the authors may decide to release a second statement, with policy specifics. I’ll inevitably be associated with those ideas, even if I don’t sign the second statement. Relatively few people will pay attention to the difference between who signed only the first statement and who signed both. So by signing this first statement, I would essentially be giving my imprimatur to unknown policy proposals. I don’t want to do that. So I didn’t sign.
In fact, the existing statement, vague as it is, does contain at least one clue as to the kind of ideas that the authors will eventually come up with. At the end, it calls for us to “steer AI in a direction that complements humans”. I recognize this as the main idea in the book Power and Progress, by Daron Acemoglu and Simon Johnson.
In fact, not only did Acemoglu sign the statement, but it appears that the authors changed the text in order to get him to sign! He writes:
Why did I sign this statement?…First, I had a hand in revising it, after the organizers reached out to me. I did not feel like I could sign the initial version…[M]ost importantly, I wholeheartedly agree with the ending: “to build the incentives, guardrails, and institutions needed to steer AI in a direction that complements humans and benefits society.”
This is what I have been arguing for over a decade now. Good AI needs to complement humans, and this requires a redirection, because the current focus on AGI is, in all but name, an agenda for displacing humans from meaningful work. That’s why steering AI must be a first priority.
So although Acemoglu doesn’t say which part of the final text he got to insert in exchange for his signature, it’s a pretty good bet that it’s the part about “steering” AI.
As it happens, I think “steering” AI is a bad policy idea. The first reason is that it’s basically impossible; no one actually knows how a technology will complement or substitute for human labor at the time they invent it. Inventors don’t know how their inventions will ultimately be used by businesses — and the more general-purpose a technology is, the less they know. Could James Watt, in 1765, have predicted most of the applications of steam power? Absolutely not. So he had no way of knowing whether the steam engine would ultimately create more jobs than it destroyed.
In fact, although AI might eventually be a big job-destroyer, right now it doesn’t seem to be. The employment rates for people age 20-24 and 25-54 are both just about the same as they were before ChatGPT ever existed:
And the employment rate for young college grads — the group everyone thinks is most likely to be hurt by AI — is also basically unchanged:
So if there’s any wave of AI job destruction, it’s not visible in the macro data yet. As for the micro data, there are a few studies that show companies reducing their hiring of certain kinds of workers when they adopt AI, but most don’t really find much. In fact, one recent study found that companies that adopt AI hire more workers than other companies in the same industry:
This is despite the fact that lots of people in the AI industry think their inventions are going to destroy jobs. So far they’ve just been wrong, and many of them are feeling pretty astonished right now.
Even when it comes to specific occupations, technologists are often startlingly wrong on the “complement or substitute” question. Geoffrey Hinton, one of the inventors of modern AI, famously predicted the end of human radiologists within a few years, only to see a boom in hiring and salaries for radiologists when it turned out that AI actually complemented their skills.
So how the heck are businesspeople and inventors supposed to “steer” AI toward being complementary to human workers? They obviously couldn’t predict the labor market effects of the last round of AI — at least, in the short term. So why should anyone believe that technologists have the ability to purposefully invent different forms of AI with different labor market effects?
The second problem with the idea of “steering” AI is the question of who does the “steering”. Acemoglu’s book, Power and Progress, never answers this question. Here’s what I wrote in my (very long) review of that disappointing book:
Acemoglu and Johnson admit that “redirecting” the path of technological innovation is going to be an incredibly tall order…[H]ow do we know in advance, before a technology is invented, whether it will increase or decrease the labor share?…Fundamentally, it still boils down to some sort of mandarins in a room somewhere — economists? government engineers? bloggers? — trying to assess the economic effects of a technology that doesn’t even exist yet…[T]his is probably an impossible task.
Acemoglu himself has certainly not had a better record than the technologists when it comes to predicting the effects of AI on jobs. He wrote an empirical paper claiming that companies that buy robots tend to hire fewer workers, but this paper was contradicted by a very large number of follow-up studies. And he wrote a theoretical paper claiming that AI wouldn’t do much to raise productivity, but that prediction was based on arbitrarily assuming away parts of his own model.
So any panel of wise mandarins that Acemoglu and his fellow-travelers assemble in order to “steer” AI technology is likely to have absolutely no idea what they’re doing. Here’s what I wrote about that idea back in 2023:
[I]f we were to set up a panel of experts and task them with deciding which lines of research and innovation to encourage and which to discourage in order to maximize jobs and wages, they would be operating purely on gut instinct and quasi-science-fictional supposition…[I]n practice, any panel or commission set up to speed up and slow down various types of AI will be simply adding noise to the innovation process, offering rewards and punishments essentially at random. That’s not good for the development of technology as a whole, since it introduces uncertainty into the innovation equation. But it’ll also be ineffectual in terms of actually protecting human workers.
Three years later, having witnessed so many of the dire predictions of job destruction dashed on the rocks of reality, I see absolutely no reason to change my assessment. Acemoglu’s big idea — basically, to put him and his friends in charge of AI development — is not a good idea.
In fact, the surprisingly benign effect of AI on jobs so far calls into question the very notion that “We Must Act Now”. Yes, I agree that AI presents a very severe security threat, and we must act on that. But on the economic front, it’s possible that inaction is the right move.
Statistically speaking, we probably don’t live in the best of all possible worlds when it comes to AI’s economic effects. But we might be close enough that any large-scale attempt to interfere in AI development might leave average human workers worse off. There’s certainly plenty of historical precedent for that — collectivization of agriculture, Mao’s “backyard production”, and a bunch of other heavy-handed interventions in the development of an entire sector crashed and burned spectacularly.
It might be, in other words, that the AI we’re building now is already highly complementary to human workers, and that the best approach is not to “Act Now”, but to simply sit there and do nothing. Even the seemingly empty slogan of “We Must Act Now” might actually be wrong. Perhaps we mustn’t.
In any case, if the authors of the “We Must Act Now” statement add specificity to their policy proposals, I’ll consider signing it. But right now, the statement seems to hide some genuinely inadvisable Acemoglu-ism behind a screen of extreme vagueness.
2026-07-13 20:38:16
“Buy a big house and live in the suburbs” — Tracy Chapman
“Outside suburbia’s sprawling everywhere/ I don’t want to go, baby” — Kim Wilde
I grew up in the suburbs, and when I got the chance, I moved to a big city and never looked back.1 I love living in dense, built-up urban areas, with great train systems and tons of restaurants and shops. Japanese cities are the best in the world, and I’ve written plenty of posts about what makes them so great. But you don’t have to be Japan in order to create amazing metropolises — New York City, Paris, Istanbul, Seoul, London, etc. are all excellent places to live.
I’m far from the only person who feels this way. Rents in New York City are absolutely insane — $5300 a month to live in Manhattan, $4350 to live in Brooklyn. That’s partly because there are a lot of good jobs in NYC — it’s a cluster for industries like finance and media. But more and more, Americans move to cities because they like living there.
As early as 2000, economists were starting to find that “amenities” were driving America’s urban revival even more than job opportunities were. Couture and Handbury (2020) find that wanting to be close to restaurants and nightlife explains about 40% of the trend of young, high-earning, college-educated people2 moving to cities in recent decades. Furthermore, the stereotype of dense cities as crime-ridden and unsafe is just wrong — NYC has one of the lowest violent crime rates among big cities in America.
I’ve been a relentless advocate of building more dense, walkable cities in America. Not only would this raise GDP (because of improved clustering effects), but it would let Americans live where they want. The demand for life in cities like NYC exceeds America’s willingness to supply these environments; this raises rents in places like NYC, which pushes a lot of people into the suburbs who don’t want to be there. Forcing those city types into the ‘burbs raises rents for people who like suburbia. Basically, everyone would be happy if America had a few more Manhattans and a lot more Brooklyns.
Yet among my fellow urbanists and YIMBYs, I often encounter disdain or outright hostility toward the suburbs that define most of America’s present urban landscape. This isn’t just an urbanist thing, of course — my own parents had a lot of negative things to say about suburbia, and ranting against its sterility and boredom is a staple of pop culture. But the criticisms are just way overdone; the suburbs are not the isolating, lonely hell that they’re often made out to be.
And I think that the constant ranting against the suburbs complicates the quest for denser cities. It creates the suspicion that urbanists and YIMBYs want to make the whole nation into Manhattan. Nothing like that could ever happen, of course; even Japan is mostly suburbanized. But painting the quest for denser metropolises as an attack on suburbia makes everything needlessly confrontational, polarized, and zero-sum.
So I think it’s helpful to go through some reasons why the suburbs aren’t actually as bad as they say.
One of the most persistent myths about suburbia is that it forces you to commute a long way to work:
It’s possible, of course, for something like this to be true. If cities refuse to build housing (which they do), and if jobs are concentrated in the city center (thanks to clustering effects or agglomeration or whatever), then people will be forced to live far out on the periphery and endure punishing commutes to get to work.
The thing is, it’s not true. Despite greater sprawl, Americans have some of the shortest commutes in the developed world. This is from the OECD:
Why do Americans take less time to commute to work? One reason is that it’s not just our homes that are scattered and dispersed; our workplaces are too. America’s urban agglomerations are polycentric; they’re not just one central business district surrounded by concentric rings of houses. Suburbanites tend to live near where they work.
Another reason Americans have shorter commutes is that cars are almost always faster than public transit at getting you from point A to point B. This isn’t just true in places like America and Canada that are built with cars in mind. It’s true in places like Stockholm and Amsterdam that were built to be transit-friendly — even when you take parking time into account. In fact, it’s not even close. This is from “Disparities in travel times between car and transit: Spatiotemporal patterns in cities”, by Liao et al. (2020):
We use real-world data to make realistic estimates of travel time by car and by PT [Public Transit] and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively…A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities…for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. [emphasis mine]
And here’s a chart, showing that public transit takes more time whether you’re measuring in terms of the length of the trip or the percent of the population reached:
That doesn’t mean cars are better than public transit. Cars cost more, and they require a lot more land to move the same number of people. But because they take you directly from point to point, instead of making a circuit and stopping periodically, they get you there faster.
Of course, commuting by car and commuting by train or bus aren’t the same experience. Driving gives you more privacy, but it forces you to pay attention to the road instead of reading or playing games. Traffic can be frustrating, but so can jostling and bumping strangers for a space on the train. You’re a lot less likely to be sexually harassed in your car, but you’re more likely to die in an accident than to be murdered on the bus. And so on.3
But the point is, cars are not a cost forced upon suburbanites in exchange for their large houses, as some urbanists believe. They are a thing people want in and of themselves, and are willing to pay a lot of money for, all over the world. The convenience, sense of freedom, and privacy cars offer is a benefit of suburbia to many people, in addition to the large house and cheap land. Car ownership is a form of wealth.
Again and again, I hear urbanists declare that the suburbs are lonely and isolating. It certainly sounds logical. In a city, you’re walking past other people constantly — on the street, on the train, in cafes and restaurants. In suburbia, you’re shut at home inside your giant house or alone in the metal shell of your car. How could suburbia not be more lonely than the big city?
But in survey after survey, we don’t find this to be the case. Here’s Abshire et al. (2022):
Data were obtained from 616 adults (278 from small rural, 100 from large rural, 98 from suburban, and 140 from urban areas) from June 2018 through October 2019…Mean unadjusted loneliness scores were lower in suburban compared to urban areas…The prevalence of loneliness was 50.7%, 59.0%, 40.8%, and 54.3% in small rural, large rural, suburban, and urban areas, respectively. Suburban living was associated with lower odds for being lonely compared to urban living…but this association was not statistically significant in the adjusted model[.] [emphasis mine]
And here’s Hammond et al. (2021):
Data from 756 participants who completed 16,602 assessments between April 2018 and March 2020 were used in order to investigate associations between momentary feeling of loneliness, the social environment (i.e. overcrowding, social inclusivity, population density) and the built environment (i.e. contact with nature)…Increased overcrowding and population density were associated with higher levels of loneliness; in contrast, social inclusivity and contact with nature were associated with lower levels of loneliness. These associations remained significant after adjusting for age, gender, ethnicity, education and occupation. [emphasis mine]
And here’s Morris and Pfeiffer (2016):
Based on data from the 2003 to 2013 American Time Use Surveys, this research…assess[es] whether suburban living is associated with less socializing than city living in mid-to-large American metropolitan areas. After controlling for personal characteristics, we find no meaningful difference in suburbanites’ and city dwellers’ time spent socializing across a wide range of social activities. [emphasis mine]
Bower et al. (2022) review 57 different studies and find no systematic association between loneliness and any measurable feature of the built environment.
Why doesn’t the simple intuition work here? Probably because people aren’t just like particles bouncing around in a chemistry experiment — they don’t simply form human connections and bonds just because they happen to walk past each other. A few relationships form from random urban conversations, but most form through work, or friends-of-friends, or shared hobbies, etc.
And while meeting a ton of new people is fun, what really gets rid of loneliness is repeated interaction with people you know and care about. Imagine going to ten parties filled with strangers versus having two close friends over for dinner. Which of those is more likely to leave you feeling lonely? What about 100 Hinge dates versus having a relationship? Remember that Japan has the world’s best cities, but struggles with widespread loneliness.
In fact, suburbs have some features that make it easier to interact with the people you really care about. Those big houses aren’t just for walking around all alone and going “Wow my house is so huge”. They’re for entertaining guests. It’s harder to have a dinner party or a TV night or a game night at a tiny little Manhattan apartment than at a big suburban McMansion. Cars help too; those short travel times make it easier to just pop over and hang.
This doesn’t mean cities are socially inferior to suburbs. Constantly meeting new people is exciting. There’s more fun stuff to go out and do with your friends in a city — restaurants, parties, and so on. It’s just a different lifestyle.
The urban revival of the 1990s through the 2010s was a modest thing. It was largely driven by young people, high earners, and educated people. Here are some charts from Jed Kolko a decade ago:
During most of that urban boom, it was actually the suburbs that were growing much faster. And in recent years, the trend toward suburbanization has only accelerated. Here’s a much more recent chart:
Kolko shows that although the densest city centers are rebounding from the pandemic, more than 100% of this is driven by immigration — domestic migration is still strongly away from city centers.
In fact, the new suburbanization trend is being led by Millennials. This is from the Joint Center for Housing Studies:
[W]e found that throughout the past decade millennials were moving to suburbs that were farther out from the city center. There are many ways to define suburbs, and in this paper we rely on a framework that considers rates of homeownership, single-family housing, and car commuting, in addition to proximity to a metro’s urban core. While there is extensive research and discussion about millennial preferences for walkable urban areas, we found that the places with the largest increases of early millennials were both suburban and on the periphery of metropolitan areas. [emphasis mine]
Why are Millennials moving out? Part of it — the part that urbanists and YIMBYs will emphasize — is that they’re being pushed out by higher rents, which are a result of cities failing to build more housing. But that’s not the whole story. Millennials are also being pulled to the suburbs, because suburbs are generally better for raising kids.
Albouy and Faberman (2025) find that the kind of high-skilled workers who drove the urban boomlet of the 1990s through the 2010s tend to move out — and value urban amenities less — once they get a little older:
We show that high-skill workers disproportionately sort into high-amenity areas, but do so relatively early in life. Workers of all skill levels tend to move towards lower-amenity areas during their thirties and forties. Consequently, individuals’ time use and expenditures on activities related to local amenities are U-shaped over the life cycle…We present evidence that the move towards lower-amenity (and lower-cost) metropolitan areas is driven by changes in the number of household children over the life cycle: individuals, particularly the college educated, tend to move towards lower-amenity areas after having their first child. [emphasis mine]
Kolko shows something similar — people with a kid over 6 tended to move out of cities during the urban boomlet, even as childless people and people with young children were moving in:
Anyone with kids can easily rattle off the reasons why suburbs make it easier to raise kids. A big house means more room for kids to have their own bedrooms, space to play, and a back yard to run around in safety. A car makes it a lot easier to ferry kids around to school, or soccer practice, or wherever. Cars also make it much easier to do large grocery shopping trips — try taking a week’s worth of food for a family of four home from the store on foot or on a bike and you’ll quickly understand.
It’s almost a cliche that people move to the city when they’re young — to jump-start their career, to party, to meet friends, to explore the world, to date around, and to find their spouse — and then move out to the ‘burbs once they settle down and have kids. The cliche is rooted in reality.
Instead of treating the suburbs as some sort of hellish place that Americans’ car culture has exiled them to, urbanists should recognize that suburbs have real advantages that dense urban cores can’t easily replicate. Yes, too many Americans are pushed out to the ‘burbs by unaffordable housing. But a lot move there of their own free will, because they want a nice place to raise kids, enjoy a short quiet drive to work, and hang out with their friends in a big comfy house. It’s not the lifestyle for everyone, but it’s the lifestyle that a lot of people love and aspire to.
Which doesn’t mean we should ignore the real disadvantages of suburbia, either. Low-density sprawl is very expensive to maintain. It tends to make people less healthy, because they walk less. It reduces variety among restaurants and brick-and-mortar retail outlets, because it’s harder to cater to niche tastes when you don’t have a critical mass of people nearby. Those tradeoffs shouldn’t be ignored.
Nor should we accept that the current suburban form is the optimal one. A lot of American urbanism has focused not on Manhattanizing urban cores, but on giving suburbs a few more of the benefits of cities — creating “gentle density” with rowhouses and duplexes and small apartment buildings, allowing retail in residential areas, adding bike lanes and commuter rail, and so on. This should all continue.
But it’s simply a mistake to frame the question of urban form as “Which is better, cities or suburbs?”. The answer is that they’re good for different things, and they appeal to different sets of people. Instead of fighting flame wars over whether the whole country should look like Manhattan or the Inland Empire, we should aim for a country that has room for everyone. We should build great city centers for the same reason we should build great suburbs — because Americans deserve to have great places to live, no matter what kind of place they want to live in.
Well ok, not quite. My PhD years and my first year at Stony Brook involved more stints of suburban living.
“Yuppies”, in the parlance of our times.
Whether your car or the bus smells better probably depends on a lot of things.
2026-07-12 17:22:33
I grew up in the 1980s in a small house with only one bathroom shared between four people. The floor was linoleum. There was a carport instead of a garage, and we had one beat-up used Toyota Tercel hatchback. I don’t remember when we got our first color TV, but when I was young we had a black-and-white one that my grandmother gave us. Our furniture was all second-hand and we kept the couches covered up with worn old blankets.
When I was young, I mowed lawns for money. As a high school kid, I signed up to pick cotton by hand (!!) for an agricultural research project at Texas A&M University, for minimum wage1. I have also worked as a cashier. Twice in my life, I have been a member of a labor union, and I have marched in a strike.
I have never once considered myself part of the working class.
Why not? Because I have never thought of class as being defined by a present snapshot of someone’s lifestyle or material circumstances. Instead, I always thought of class as being about someone’s potential. And I grew up always knowing that my economic potential went far beyond the rather humble circumstances of my early childhood.
For one thing, my family was upwardly mobile. My grandparents could probably be called “working class” in their youth — my grandmother worked in a sweatshop as a teenager, my grandfather wore cardboard in his soles because his family couldn’t afford shoes. But after World War 2, thanks to the GI Bill and rapid economic growth, my ancestors advanced into the middle class, with jobs like optometrist, athletic coach, and registered nurse. My father had a PhD and a tenure-track academic job that promised to pay a lot more after a decade of work. We weren’t rocketing up the income distribution, but we were clearly climbing.
Our humble lifestyle in the early and mid 1980s reflected this future orientation. Our family income was probably around the 35th-38th percentile,2 but this was because we were a one-earner family. My mother chose to spend the first seven years of my life as a housewife — which she did in order to make sure my sister and I got a thorough, accelerated education. We lived an abstemious life in part because we saved as much money as we could.
What were we saving for? My college education, and my sister’s. We were smart kids; we knew we would go to good schools, and we did.3 We knew our college educations would allow us to get good jobs that paid more than our parents ever made. And we were right.
As for the union membership, I was part of the grad student instructors’ union at the University of Michigan, and the professors’ union at Stony Brook. When I marched in a strike in 2008 to secure a raise and health benefits, I was already getting paid to complete a PhD that would eventually increase my earning power even more. Even as I scarfed free food from charcuterie boards at departmental events to save money, I was building up my future earning power at a rapid clip.
Class in some countries is about the past; you can be a shabby aristocrat if your grandfather was the Earl of Whatevershire. In American policy discussions, class is often implicitly about the present — where you lie in the income and wealth distributions this year. But on some level, everyone knows that class in America is really about the future.
Milton Friedman had a theory that sort of gets at this idea, in fact. It’s called the Permanent Income Hypothesis. “Permanent income” is the income you can expect to make over the course of your life. If you’re a shabby grad student living off of cup ramen, your current income is low, but your permanent income is high, because you know you’re probably going to make a lot of money in the future.4
But class in America isn’t just about the money you will make in the future; it’s about the money you could make if you wanted. I know schoolteachers who live modern middle-class lifestyles despite having graduated from the best schools in the country. They could have gone to work for companies and made decently big bucks, but they preferred a more laid-back lifestyle. Whether their children should count as middle class or upper class is an interesting question, but they themselves are clearly the American equivalent of Europe’s shabby aristocrats, because they forsook the upper-class lifestyle voluntarily.
Meanwhile, there are millions upon millions of Americans for whom working in high-paying salaried jobs was just never an option, and never will be. They will spend their entire careers driving long hours in a truck, or stocking shelves at a store, or installing smoke alarms in people’s houses, simply because this is the best they can do.
This explains at least part of why most low-income Americans traditionally consider themselves middle class. They expect to be middle-class at some point; they don’t think they’ll be trapped driving a truck or stocking a shelf forever.5 And it could also explain why the number of Americans calling themselves “lower class” rose in the 2000s and 2010s, as growth in incomes temporarily stagnated and the potential for rapid downward mobility became clearer after the financial crisis:
I’m being very approximate here, of course, and I’m speaking for the country more than I probably should. In fact, America has less class consciousness than many other societies, and when we do talk about the idea, there’s rarely agreement on what it should mean. I wrote about the contested nature of class in American society two years ago:
But I’m speaking from my own personal experience because I think it illustrates something important about class in America. Although we disagree about what the concept should mean, most of us feel deeply uncomfortable with a notion of class that ignores future possibilities. When we see a guy proudly wear a United Auto Workers jacket even though his only UAW experience was as a grad student instructor at Harvard University, something about it feels deeply inauthentic:
There’s nothing wrong with this, of course. It’s not fake; the UAW really does include grad student unions these days. And MacKay could easily be doing this not out of rank political opportunism, but from a sincere desire to express solidarity with union workers from all walks of life.
And yet Evan MacKay is really not in the same boat as people who stand on an assembly line, even if those people make more money than he does. He could, if he wanted, go work in private equity and live in a mansion on Cape Cod. Your typical UAW member could not do that. There’s a sense in which his jacket’s implicit claim of “we’re all in this together” papers over that harsh reality.
Progressives have had an extremely tough time appealing to Americans with low incomes and low education levels. Decades ago, those people tended to vote for Democrats; in 2024, they broke solidly for Donald Trump. Even as they’ve become more and more progressive, the Dems have become the party of well-educated high earners:
Even people who identify themselves as “working class” have been abandoning the Democrats:
Joe Biden tried very hard to win over labor unions, but the Teamsters — once a Democratic stalwart — refused to endorse him.
The socialist faction might style itself a friend of the working class, but it faces the same problem. Zohran Mamdani won his mayoral race thanks to the support of higher-income, educated voters, while his defeated “establishment” opponent did better with lower-income people, Blacks, and Hispanics. Other socialist victories have seen a similar pattern.
This is not actually a new problem. A century ago, in The Road to Wigan Pier, George Orwell complained bitterly that the British socialists of his day were middle-class intellectual elites who failed to win over the working class because they were completely out of touch:
The first thing that must strike any outside observer is that Socialism, in its developed form is a theory confined entirely to the middle classes. The typical Socialist is not, as tremulous old ladies imagine, a ferocious-looking working man with greasy overalls and a raucous voice. He is either a youthful snob-Bolshevik who in five years' time will quite probably have made a wealthy marriage and been converted to Roman Catholicism; or, still more typically, a prim little man with a white-collar job, usually a secret teetotaller and often with vegetarian leanings, with a history of Nonconformity behind him, and, above all, with a social position which he has no intention of forfeiting. [emphasis mine]
Half a century later, Barbara and John Ehrenreich wrote something similar about the American left in their essay on “the professional-managerial class”. The “PMC”, as it has become known, was beginning to dominate the political left even in 1977; today, it arguably forms the Democratic Party’s most important voter base. The DSA faction that’s now becoming more influential within the party is disproportionately drawn from this class — 80% of DSA members had college degrees in 2021, and more than a third had postgraduate degrees (more than twice the national average).
Although they might not have realized it, what the Ehrenreichs were describing was a result of the rising economic importance of human capital. The “youthful snob-Bolshevik[s]” Orwell described in the 1930s were few in number, but the growth of knowledge industries has made this group of people far more numerous — and far more influential in our culture and our politics.
Because of its Marxist inheritance, the idea of the working class is very important to American socialists. But that’s been increasingly hard to square with the fact that voters with lower income and lower education have been steadily drifting away from the Democrats, and form relatively little of the DSA’s membership. One response has been for some socialists to paint themselves as the actual working class. This is a less charitable interpretation of Evan MacKay’s UAW jacket, and it’s also something that has come up in casual conversation. This is from my post back in 2024:
In January 2017, I was at a house party in Berkeley. People were discussing why Hillary Clinton had lost to Donald Trump, and one woman — a law student at the University of California — declared that it was because Clinton had ignored the “working class”. I asked her to describe someone in the working class. She imagined a “sex worker” who had a bunch of student loans and a humanities degree that she wasn’t able to use… I had expected her to describe a unionized auto worker or steelworker or a stereotypical Midwestern guy in a hard hat,…I was utterly unprepared for her to instead describe someone from her own educated progressive social circles…To this law student, the “working class” was simply those of her friends who were most down on their luck.
But on some level, I think this just doesn’t ring true, even within socialist circles. Class in America is just too deeply connected with earning potential and human capital; most people can’t really bring themselves to believe that someone with a diploma from a good school is “working class”, even if they happen to be pulling an unlivable wage as an adjunct professor and sleeping in their car at the moment.
So socialists are always on the lookout for champions who seem more authentically working class. They thought they found such a champion in Graham Platner, who recently dropped out of his Maine Senate race due to rape allegations. This is from a New York Times story about how Platner was recruited:
Last July, in a small town in coastal Maine, three progressive, self-styled recruiters of economic populists showed up at the blue-shingled house of Graham Platner, a little-known oyster farmer and Marine veteran who lived largely off government benefits…They knew his name from local labor organizers and activists, and they had watched a video on the internet of him talking about oysters. Struck by his left-leaning ideology, his working-class affect and his gravelly voice, they became convinced that he could win a Senate seat in Maine — and quickly persuaded Mr. Platner of the same…The recruiters — Dan Moraff, Leanne Fan and Morris Katz — told Mr. Platner he was “the one,” a “hero of the movement,” “a historical figure” who could be “leading a revolution,” according to half a dozen people with knowledge of their conversations.
Is Platner actually working class? You can argue it either way. He went to a good college but dropped out due to psychological issues. He’s the son of a lawyer and an architect, but failed to make much money with his oyster farming business, and mostly lived on welfare benefits. He doesn’t fit cleanly into the kind of class categories I described above.
But the people who picked him do! Dan Moraff and Morris Katz are both educated scions of rich families — Moraff’s ancestor founded the company that became Toys “R” Us — while Leanne Fan is a sociology PhD student. If the NYT’s reporting is correct, they appear to have picked Platner based entirely on stereotypes and vibes — he seemed rough and tough and down on his luck, so they assumed he was a real working class guy who could connect with other working class guys. This seems to have convinced them that Platner was a messianic figure who could bring wayward working-class voters back to the Democratic fold.
As many people have said, if Trump is “a poor person’s idea of a rich person,” Platner is basically the opposite — a rich person’s idea of a working-class person.
This episode doesn’t make me particularly optimistic about Democrats’ ability to reconnect with their rapidly vanishing blue-collar base. I don’t think it’s impossible, of course. But it would probably require moderating on social issues — DEI, immigration, etc. — instead of just running candidates that pattern-match to the kind of people that bullied rich lefty kids back in junior high.
The Platner saga also makes me a bit pessimistic about American society as a whole. Although we don’t talk about class much, it’s separating us more and more, and the cultural gap is now so big that lots of Americans seem unable to even imagine what Americans from other social classes are like.
The integrating institutions that once pushed us together across class lines — church, the military, schools in mixed-income neighborhoods — have waned in importance. College has grown to fill some of that void, but less than half of the country is really prepared to handle the rigors of a college education, and so it ends up dividing our society more than it unites it. Mass media has fragmented into the millions of little social verticals that make up the internet. We’ve sorted ourselves geographically — knowledge workers live together in progressive urban enclaves on the coasts, while blue-collar types inhabit the small towns and down-market suburbs.
I read Tocqueville, and I miss the roiling, fluid, egalitarian young democracy that I never knew. I think back to my childhood, in a little house on a dusty side street in a small Texas town, and I feel like I can just barely recall the fading embers of that stubbornly classless democracy. Something happened between then and now. We let the Old World sneak up on us.
In case you’re wondering what picking cotton by hand is like, it sucks. Other than my teenage friends and myself, the only people willing to do it were illegal immigrants from Mexico. Working alongside illegal immigrants, knowing that it was just a summer experience for me but would be their job for the rest of their life, gave me a deep respect for illegal immigrant laborers. Yes, they violated my country’s sovereign border, but they did it so that they could do backbreaking low-paid menial labor for their whole lives, just to feed their families back home. And they worked harder than my friends and I did, even knowing that their future would probably never get any better than that. Those are the people whose lives of backbreaking labor put cheap food on your table.
For households, it was more like the 45th percentile. My dad’s salary was almost twice the median personal income of the time. Part of the reason my childhood sounds a bit shabby is that the whole country was a lot poorer back then; one-bathroom houses without garages weren’t so unusual.
I went to Stanford; my sister got into Harvard but turned it down to go to the University of Michigan, because of my family’s quixotic belief that public schools are good. That turned out to be a very expensive decision on her part; Stanford, with its incredibly generous need-based financial aid policies, charged me zero tuition, while my sister had to pay out-of-state tuition despite a merit scholarship. Fortunately, my family had saved money, and so was able to pay for my sister’s college without making her take out student loans.
Friedman’s hypothesis turned out to be wrong — he thought only permanent income mattered for consumption, but it turns out that temporary ups and downs matter too. But expectations of future income are a factor that determines people’s current behavior, so Friedman’s intellectual effort wasn’t wasted.
For the upper-class and rich people who identify as “middle class”, it’s probably more of a combination of A) humility/social desirability bias, and B) the fact that they’re comparing themselves to other rich people in their social circles.
2026-07-10 23:23:02
In my post on America’s 250-year anniversary, I argued that respect for the individual was the “secret sauce” in the U.S.’ long-lasting preeminence among nations. But it was certainly not the only factor. There were plenty of institutional innovations that helped the U.S. stay on top — economically, militarily, and in terms of the attractiveness of its society.
One of these was American science. Today we take things like the modern research university, government grants for science, public-to-private research spinoffs, etc. for granted, but a lot of that infrastructure wasn’t there before World War 2. It was either invented or scaled up massively by the U.S. government in the postwar period, led by far-sighted scientist-bureaucrats like Vannevar Bush (pictured above). If you want to read about this history, a good place to start would be Jonathan Gruber and Simon Johnson’s excellent book Jump-Starting America.
Those scientific institutions didn’t exist in a vacuum, however. They were backed by the U.S. government’s abiding faith in the power of science — and, even more fundamentally, by deep popular trust in the scientific enterprise. Science gave us radar and the atom bomb in wartime, and in peacetime it gave us plastics, vaccines, cheap food, and a thousand other things that made our lives easier. Science was also the driver behind American industry — chemicals, aerospace, telecommunications, computers, electronics, and so on.
We owed science our jobs, our livelihoods, our comfortable living standards, and our greatness and power as a nation. It’s little wonder that both political parties, even as they fought viciously over other issues, were steadfast in their support of science. For a long time, the only people who distrusted science were a hippie fringe on the left who disliked modernity (or thought they did, anyway), who also disliked American industry and American power, and who subscribed to an early version of degrowth environmentalism.
America’s scientific enterprise is still strong, especially compared to the systems in Europe, Japan, Korea, and other developed countries. It has lost a lot of ground to China in a relative sense, but a lot of that is because of China’s incredible growth; as China has poured untold amounts of money and talent into its research labs, spending and output have overtaken the U.S. by some measures.
There are a few ways in which China’s rise creates problems for American science — for example, top scientists can choose to work in China instead of in the U.S. — and of course there’s the concern that China’s technological strength will help its military to reign supreme. But overall, China’s rise in science should be good for American science, since American scientists can use Chinese discoveries for free and build on them.
The much bigger problem is that the scientific enterprise America built during and after World War 2 is now being threatened with absolute decline. The biggest problem, of course, is that much of the country — the Republican half — has basically lost faith in the scientific enterprise. To what degree this loss of faith is justified is an open question, and deserves to be discussed openly. But the larger point — that the system that powered American dominance is under threat — is true either way.
There’s a myth, popular in right-wing circles, that scientists have lost the trust of regular Americans — either due to the increasingly left-wing composition of academic departments, or to misbehavior during Covid, or to DEI-related research taking over science, etc. This also fits with a wider narrative that Americans are losing trust in all of our institutions.
But it just isn’t true. Poll after poll shows that on the whole, Americans still trust scientists and want to spend more on science. For example, here’s a Pew poll from late 2025 showing that although about a fifth of Republicans did lose confidence in science in 2020, two thirds still have at least “a fair amount” of confidence:
That same poll found that scientists are among America’s most trusted groups — even better trusted than the military!
In fact, Americans trust scientists more than people in most other countries do.
Other polls show that although Republican trust in science has dropped somewhat, Republican support for spending more on science remains very strong:
A Pew poll in 2023 found the exact same thing:
So when MAGA types tell you about scientists losing their credibility, or a drop in trust in science, they’re only talking about themselves. Whatever left-wing politicization of science happened during the Biden administration — and there was definitely some of that — it was not enough to make most Republicans lose faith in the scientific enterprise, or favor research cuts in order to purge unwanted ideology from the system.
That doesn’t mean I think progressives should continue down the path of politicizing scientific research. They should not, and Biden made real missteps in this area. Objectivity clearly matters for public trust of science in the long term. But as of right now, there’s no crisis of trust in science, except among the smallish minority of people who are running around screaming that there’s a crisis of trust in science.
But despite science’s overwhelming popularity and public trust, Trump and his administration are launching an unprecedented and devastating attack on American science — cutting funding, and forcing science projects to undergo ideological review by government commissars.
2026-07-08 11:56:06
“Her belly may be full, but her spirit will be empty.” — Captain Picard
Usually, these “GDP is actually good” posts start with a big disclaimer — an acknowledgement of all the things GDP doesn’t measure, all the reasons that measuring GDP is an inexact science, and all the ways that we need to improve society other than just making the GDP line go up. If you want a standard wonky explanation of why GDP is a useful number, here’s one that I wrote four years ago:
Today I’m going to do something a little different. I’m going to tell you what I think the debate over GDP is really about.
Free trade usually raises GDP. Immigration, done right, raises GDP.1 Rightists in America want less free trade and less immigration. But every time they propose restricting trade and immigration, someone — either libertarian business/econ types on their own side, or moderate liberals on the other side — says “That will make America poorer!”. So they want some way to neutralize this objection, so they can do things that will, in fact, make America poorer.
So America’s right borrowed an argument from the European left. The European left favors degrowth, and another term for degrowth is “making GDP go down on purpose”. So naturally, they’re always trying to find reasons to denigrate GDP as a metric of human flourishing (see here, here, and here for examples). The American right is simply tweaking these arguments to make them more appealing to their own base.
JD Vance, who has emerged as the consensus leader of the New Right, makes a bunch of these anti-GDP arguments in his new book. For example, he uses the example of Japan to point out that unobserved quality differences in non-traded products can make it difficult to compare GDP across countries:
If you’re focused on GDP, a $6 pint of Japanese strawberries is no different from a $6 pint of American strawberries. If you’re focused on dollars and cents, each contributes equally to the economic indicators. But if everyone in Japan eats better strawberries than everyone in America, the economic indicators have failed to measure something meaningful.
This is actually a good argument, and I’ve made it myself many times in the past. I’m in Japan right now, and there actually are a lot of little things that make Japanese products and services a bit nicer than their American counterparts — clean tables at Starbucks, slightly better-tasting food, and so on. Economists who try to adjust for quality differences end up catching some of these things, but probably miss most of them. That ends up creating a problem for GDP comparisons between countries. And it’s only one of many such problems. Comparing lifestyles in countries where life is very different is just a difficult thing to do.
But instead of simply noting that economics is hard, JD Vance uses this good argument as a reason to bash the entire field of economics:
When I got back home, a friend asked me if I learned anything on my trip to Japan. “Yes,” I replied snidely. “Maybe economics is just fake.”
When you read some of Vance’s other arguments against GDP, his agenda becomes clearer:
[A]s the decline of Christianity has left us without a shared moral language, economics has stepped into the vacuum. We pretend there are scientific answers to questions of values. Take one of the major issues of the 2024 campaign and a significant focus of our time in the White House: Should our trade policy be oriented around protecting domestic industries and jobs or around ensuring a short-term supply of cheap consumer goods?
This idea — that economists urge values of base consumerism on society, and ignore other moral considerations — is common in European leftist discourse. But instead of urging us to care more about inequality, power, and so on, as European leftists do, Vance wants us to care more about spiritual elevation, morality, community — i.e., things that the American right cares about. He goes on to write:
[W]e now live in a society almost blinded to considerations outside of the economic. This way of thinking is inherently opposed to the Christian way, which demands more focus on people…Take, for instance, the time we spend with our children…Domestic labor—that done by moms and dads—if unpaid, is uncounted in measures like GDP. When I leave work to spend time with my children, when I cook them dinner or argue with them about eating their carrots, I am engaged in economically unproductive work. No money changes hands, so it doesn’t show up in our national figures. By contrast, if I left for dinner at 6 p.m. and returned to work until midnight while paying a total stranger to look after my kid, my contribution to GDP would be much higher.
and:
If you step away from the glory of economic statistics, so much of American life has gone wrong. An influx of prescription opioids became a flood of synthetic opioids, which has led to tens of thousands of deaths each year and a declining life expectancy among a substantial portion of our society. We have made great progress on reducing infant mortality, but we send our children into a world—even in the physical security of their own homes—that bombards them with images and influences that have left them isolated, depressed, and increasingly at risk of self-harm. We are more disconnected, lonely, and isolated, even in the midst of historic levels of material comfort.
All this economic abundance coexists with intense spiritual misery. We orient people toward a life of consumption. We tell them to find meaning in the home they buy, the money they earn, the prestige of their job. We bombard them with all manner of creature comforts, and add their consumption—price club mega-size junk—to our national GDP. We use that GDP as a yardstick for our broader society, which is why it’s possible for false prophet economists to argue the American Dream is healthy even as suicide and addiction rates soar and the laughter of children fades from our streets.
Some of these arguments are — in my opinion — reasonable. A culture of overwork can boost GDP, at least in the short term, at the expense of quality time with family. This is actually a common argument of the center-left, which is why liberals have long fought — often successfully — for more paid family leave and other policies that reduce GDP slightly in exchange for more quality time with family. Whether this has increased birth rates isn’t clear — the evidence is very mixed — but it’s a very popular policy.
Other arguments are clearly mistaken. Over-prescription of opioids has clearly reduced GDP, by a massive amount. Yes, selling a bunch of opioid painkillers to Americans raises GDP by a few billion dollars, but this is vastly outweighed by the trillions of dollars of GDP that we lose from having a bunch more people addicted to painkillers, heroin, and fentanyl. Here’s the Philadelphia Fed in 2023:
There is growing evidence that the opioid epidemic has harmed many aspects of the real economy, including the labor market, consumer finance, and municipal finance. According to analyses from the Council of Economic Advisers’ 2019 report, the annual (nominal) economic cost of the opioid epidemic, including the cost of lives lost, is estimated at about $700 billion (roughly 3.4 percent of GDP) in 2018 alone, and over $2.5 trillion from 2015 to 2018. [emphasis mine]
So if you care about GDP, you should view curbing opioid and opiate abuse as a huge priority! Vance is simply not thinking about this very clearly.
But Vance’s real problem is that he conflates correlation with causation. In words that could have come straight from the mouth of a European degrowther, he rails against “creature comforts”, “consumption”, and “price club mega-size junk”. But nowhere does he explain why depriving Americans of these creature comforts would give them closer-knit families, a stronger sense of morality, stronger communities, reduced loneliness, and so on.
Why would taking away Americans’ large houses, SUVs, big-screen TVs, or central air conditioning make them spiritually richer? Modern Europe — which JD Vance spends much of his time railing against — lacks most of these things. And yet America has higher fertility rates than Europe, we go to church much more, and we have a much more robust social conservative movement. Europe has also been far more restrictive of speech that criticizes Islam, as Vance repeatedly notes. Yes, America has been trending away from social conservatism and Christianity in recent decades, but so has Europe, and the gap remains. Other developed countries in East Asia — most of which are moderately poorer than the U.S. in GDP terms — are extremely secular.
What about America’s past? We were much poorer in the 1950s, yet we went to church a lot more, had larger families, and so on. If you took away the material gains we’ve made since then, would we go back to tradwives and bowling leagues and lawn parties and Sunday church and 4 kids per family?
Perhaps, but it’s doubtful. Remember that the 1950s and 1960s were the culmination of a long upswing of community, religiosity, and so on in American society — something the sociologist Robert Putnam has documented extensively. Church attendance rose:
Fertility was on the upswing too:
And if you believe Putnam’s numbers, social solidarity increased all throughout the early 20th century:
It’s important to remember that this all came during the most robust and rapid period of GDP growth that America has ever seen. Over the period in which our social solidarity was soaring, our GDP per capita nearly tripled:
It was during this time that Americans got many of the “creature comforts” that Vance despises — the single-family homes, the cars, the televisions, the lawns, and so on. The image of that material prosperity, depicted in glossy ads and paintings from the time, is a powerful part of 1950s nostalgia.
In fact, many economists argue that one big cause of the Baby Boom was the fact that economic growth — bigger houses, better medical care, new labor-saving devices like washing machines, refrigerators, and vacuums, and so on — made it easier and cheaper to raise kids. This is from a relevant Works in Progress article by Anvar Sarygulov & Phoebe Arslanagic-Little:
Parenthood rapidly became much easier and safer between the 1930s and 1950s. The spread of labour-saving devices in the home such as washing machines and fridges made raising children easier; improvements in medicine making childbirth safer; and easier access to housing made it cheaper to house larger families…
[H]ousehold electrification paved the way for other technologies, including home refrigeration…By the 1940s, electric washing machines were becoming normal in middle class homes…Between 1936 and 1956, America’s maternal death rate fell by 94 percent, from 51 deaths per 10,000 live births to under 3…[M]edical advances, which were being made across the West, radically reduced the most serious potential cost faced by prospective mothers: life itself…
Alongside strides forward in household and medical technology…[I]t became easier to secure a home in which to raise children. The number of houses built soared across the West after World War Two…This house-building bonanza led to sharp rises in homeownership rates.
The golden thread linking the phenomena that comprise the triple mechanism we describe above – advances in household technology, progress in medical technology, and easier access to housing – is that they together sharply reduced the cost of having children. [emphasis mine]
If you like the kind of society we had in 1960, you can’t ignore the story of how we got to 1960. The answer was “economic growth”. This, combined with the examples of Europe and Asia, is why there’s no reason to believe that forcing Americans to be poorer — taking away the “creature comforts” Vance despises — would lead us to suddenly rediscover the value of community, family, and religion.
Now it’s worth noting that if you were to decrease America’s GDP to poor-country levels — below $15,000 per person, as opposed to over $90,000 today — you might be able to raise fertility. That’s how low you have to go before most countries have fertility above replacement level:
Countries with a basically pre-modern standard of living — where many women can’t read or write, and infant mortality is so high that families have to have many kids as a form of insurance — tend to have above-replacement fertility (though some don’t). But even this law is weakening, as fertility rates in Sub-Saharan Africa plunge, so even that extreme level of GDP reduction would probably fail to restore high fertility over the long run. Also, I kind of doubt that JD Vance wants to force Americans to live lives similar to those lived in Sub-Saharan Africa.
What about JD Vance’s preferred policies — trade protectionism and immigration reduction? Would those restore American community, family, and religion, at the expense of a bit of GDP? As I said earlier, that’s actually what I think this whole debate is really about.
On trade, you’ve seen Trump explicitly make the argument that Americans are going to need to suffer a bit of material deprivation in order to achieve the administration’s goals:
But how will doing this restore community, family, etc.? Presumably you could make an argument that protectionism will bring back good manufacturing jobs, which will then give men the confidence and social standing they need to get married and have kids. There’s just one big problem with this, though: Trump’s trade policy doesn’t actually increase the number of good manufacturing jobs. We’ve lost manufacturing jobs since Trump took office last year!
In fact, Trump’s tariffs are hurting the U.S. manufacturing sector, by raising the cost of intermediate goods. Economists understand this pretty well; JD Vance, who thinks “economics is just fake”, does not seem to understand it.2
How about immigration? Rightists will endlessly cite Robert Putnam’s finding that diversity reduces social trust in American communities. But as Bryan Caplan and many others have pointed out, the effect size is tiny — in Putnam’s research, going from zero diversity to maximum diversity reduces social trust by the equivalent of 1 point on a 100-point scale. This suggests that all the mass deportations in the world won’t move the needle on American community and togetherness.
In other words, JD Vance’s crusade against GDP is a cargo cult. Sure, GDP doesn’t measure “the beauty of our poetry or the strength of our marriages,” to use Robert F. Kennedy’s famous words. But that doesn’t mean that making Americans poorer will make their poetry more beautiful or their marriages stronger. Nor does it mean that policies that also happen to make us a bit poorer, like immigration reduction or tariffs, are any more likely to strengthen our society.
In a famous episode of Star Trek: The Next Generation — my favorite TV show of all time — Captain Picard castigates an alien for turning his society into a fascist empire. When the alien responds that the fascist government’s forced modernization program raised his daughter out of poverty, Picard responds with the beautiful quote at the top of this post: “Her belly may be full, but her spirit will be empty.” But was Picard arguing that it was the full bellies themselves that emptied the people’s spirits? Was he merely arguing that the fascist empire ought to become a poorer fascist empire, in order to restore the virtue of the people? Only a fool would think so.
Look, I also want America to have a stronger society. I want us to have more kids. I want us to have more stable families. I want us to have closer-knit communities, better moral values, etc. But that doesn’t mean the New Right knows how to get us there. So far, the New Right has built nothing — no new community organizations or institutions, no religious revival, nothing that would knit our society together. It has merely thrashed and thrashed against modernity, with no plan for a replacement. JD Vance’s crusade against GDP is simply more of the same.
“Done right” means getting immigrants who, on average, earn more than the native-born. This raises GDP by a composition effect — you have richer people on average. It also probably raises GDP by other means — increasing market size which increases returns to scale, boosting innovation and entrepreneurship, and so on. If you get mostly low-skilled immigrants, things get dicier — the composition effect reduces GDP because you’re importing poorer people, but the increased market size may still cancel that out. But in general, if you want higher per capita GDP, you should be selective in terms of who you let into the country. Total GDP, of course, is a different matter — if you want a bigger country, in order to be more powerful in military terms, then letting in tons of low-skilled immigrants may be worth it even if they reduce per capita GDP. And of course, there are distributional reasons to allow in low-skilled immigration — eldercare and so on. But basically, if you let in high-skilled immigrants, your society gets richer by pretty much every metric.
In fact, if you care about Americans having jobs, maximizing GDP growth is an important part of the equation. Okun’s Law — one of the most well-established regularities in economics — states that the unemployment rate is very strongly (negatively) correlated with quarterly GDP growth:
And only when GDP is growing robustly is there upward pressure on wages. The greatest period of growth for working-class wages in America since the 1960s was 2014-2024, despite inflation and the pandemic, because steady GDP growth kept us at full employment for most of that time.
Nor is this simply a case of correlation vs. causation. The mechanism is aggregate demand — when the economy is growing fast, people need more workers to produce things. So if you increase aggregate demand to make GDP grow faster, you raise total employment (though you don’t always want to do this, because of inflation). Yes, you can give people government jobs when the economy isn’t growing, but A) these jobs will generally not be great, and B) this will lower productivity and put downward pressure on wages. So for good jobs, you need GDP growth.
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