Let’s play a game. Below is a list of 13 inflation measures. I want you to guess two of them. Specifically I want you to guess the two inflation metrics Kevin Warsh said were his preferred measures during his Senate nomination hearing last week.

If you’ve followed inflation over the past 5 years you may be familiar with some of these. They have come up at some point to prove or disprove your favorite theory of why inflation has behaved the way it has. They all have links to their actual sources.

Now try to guess which two Warsh pointed to as being the best ones for right now. It’s ok if you don’t know much about inflation or economics, I think you can make an educated guess which are the two.

If you guessed the lowest two, the two “trimmed” ones, the ones that said inflation was the lowest under Donald Trump, congratulations. You got it right. Specifically, Warsh said the following during his testimony (my bold):

Senator Catherine Cortez Masto: So some Federal Reserve officials have said that this excess is due to tariffs. Do you agree with that?

Kevin Warsh: Senator, I don’t. […] the Fed needs to do, is to try to use our new understanding and new data sources to see what’s really the inflation rate in the economy. We used to use Core PCE, Core measures, so we’d exclude food and energy, because it was sort of a rough swag as to what was going on. We don’t have to do a rough swag anymore. What I’m most interested in is, what’s the underlying inflation rate? Not what’s the one time change in prices because of a change in geopolitics or a change in beef, but what’s the underlying generalized change in prices in the economy?

My broad sense is that these inflation risks and the inflation damage the last several years is improving somewhat. It has improved somewhat in the last year. The measures I prefer are looking at things that are called trimmed averages, where we take out all of the tail risks, all of the one-off items, and we ask ourselves whether the generalized change in prices is having second order effects on the economy. Again, they’re not where they should be, but I think that the trend is quite favorable.

I’ve tried to approach the Warsh nomination with an open mind. After all, a wide bipartisan range of people affiliated with Harvard University said he was a good choice.

I understand the liberal worry that Warsh is a sock puppet for President Trump’s ambitions to control the monetary and financial system. I worry about that too. There is something bleak yet familiar about watching someone so rich they can’t disclose all their wealth or plans for divesting thinking they’ll win out against Trump, that they’ll be the one who doesn’t get bitten by the snake they are supporting.

I also don’t take any comfort that the Federal Reserve board would contain him or Trump’s influence. As Bharat Ramamurti discussed, Fed chairs generally get their way. They control the staff, set the agenda, put the first formal policy proposal on the table, and set the tone at the press conferences.

But, and there’s no polite way to say this, I’m more worried that he’s just not up for this moment. This is going to be a very difficult time for the central bank, and nothing Warsh has said has given me much confidence in his abilities. He says a lot of stuff that no doubt works well in the circles he rolls in, but I don’t think it stands up well to anyone thinking seriously about the topics.

The way he discusses the Fed’s balance sheet, for instance, doesn’t convey that he’s thought hard about the tradeoffs. It’s even more apparent when he argues AI productivity would lower interest rates, seemingly unaware of the more obvious counterargument, like Krugman notes, that it would raise them. It all seems shallow.

Trimmed Mean Was a Disaster Measure

Warsh’s quote above continues this. I genuinely mean this in a non-partisan way, the worst lesson you could have taken from the last six years is that trimmed mean measures have some special signal other measures lack. During the inflation wave they consistently performed worse in both directions, leaving many smart researchers positioned the wrong way at the wrong time throughout the whole inflation saga.

Looking at the graphic above, you can see how much trimmed mean inflation trailed overall inflation during the initial inflation wave. Various trimmed and related measures made people underestimate in 2021 how high and widespread inflation would become in 2022, especially expanding to services. Trimmed measures moving up in 2022 also caused smart people to think a recession was necessary to bring down inflation. Instead, we had the soft landing, where GDP went up ~2.8% a year during the two years where inflation fell four percentage points, making traditional measures like the sacrifice ratio have the wrong sign.

I’m going to mention some names here, but I want to be clear I screwed this up more than most. But take Federal Reserve Chair Jay Powell himself. At Jackson Hole in August 2021, when inflation picked up but was not yet widespread, he pointed to “trimmed mean measures […] generally show inflation at or close to our 2 percent longer-run objective.” Yet six months later, the February 2022 Monetary Policy Report used the very same Dallas trimmed mean to argue that inflation was skyrocketing.

You can see an exaggerated version of this among academics. In a December 2021 IMF paper, Laurence Ball and co-authors held up the Cleveland weighted median and Dallas trimmed mean as the measures that had stayed smooth through the pandemic to imply that underlying inflation was still contained. But by the time of Ball’s September 2022 Brookings paper, built on the Cleveland weighted median (an extreme version of trimmed mean), Ball had flipped to the opposite side, arguing that the trimmed measures showed the Fed would need to push unemployment “far higher” to get inflation back to target, and either inflation would stay high or the country was headed for “a substantial economic slowdown.”

As Preston Mui of Employ America later noted, the Brookings event discussing the 2022 Ball paper was a venue for many to conclude that a higher trimmed mean inflation necessarily meant a recession was required to bring down overall inflation rates.1 Jason Furman wrote a Wall Street Journal piece on the paper, calling it “the scariest economics paper of 2022” and arguing an elevated trimmed measure meant “we may need to tolerate unemployment of 6.5% for two years” to bring inflation down. Thankfully the soft landing didn’t require any of it.

My own theory of the inflation wave is that there were several overlapping shocks, especially to the composition of spending, to working remotely, and key inputs like semiconductors as well as commodities following the war in Ukraine. That pulled up many prices, and because we live in a Keynesian world full of rigidities, other prices did not fall. Then relative prices were out of sync, so that pulled up other lagging prices, moving the distribution of items to line up. You can follow this logic with microfounded models as in Lorenzoni and Werning (2023), ironically the BPEA paper of the following year.

This is even more important as the divergence picks up again, as more is being excluded in these tails. The Dallas Fed did a deep dive into what their methodology finds in this moment. After replicating for a future post, I’m reading that in recent months it looks like service inflation is picking up, something we’ve been covering, but is being hidden by the trimming method. So we’re back to where we started: what exactly is going on at this moment?

These kinds of stories won't be understood through just looking at the central distribution of inflation. These overlapping shocks generate the misleading trimmed-mean signal that fooled analysts during the actual inflation wave. Figuring this out requires wisdom and judgment.

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Rewatching the video of that 2022 Brookings Papers event, with all the economists calling for a major recession as soon as possible, there’s a funny moment where, as a result of multiple cameras, you can watch Claudia Sahm’s reaction in real time from her back row to Frederic Mishkin, of financial (in)stability in Iceland fame, saying that “bottom line is that the recession is probably going to be a serious recession” to bring inflation down.

Is it an epic eye-roll? I’ll leave you to judge. Claudia was one of the smartest people on this inflation wave, so subscribe to her Substack. Hi Claudia! 👋

I’ve written before about why I think people are concerned about affordability given high price growth in key items. I want to take a different angle. It’s a thought experiment, and it probably needs more analytical firepower than I can give it here, but I want to float it.

Making Comms People Sweat, Racial Wealth Gap Edition

First, let’s start with something you probably haven’t spent a lot of time fixated on: how would you describe the black-white racial wealth gap during the Biden administration?

Let’s take data from a Federal Reserve study, derived from the Survey of Consumer Finances, a high-quality survey of wealth administered every three years, last administered in 2022 (we don’t have 2025 yet). Here is black and white median wealth, inflation adjusted, for several years.1

One might say that “the racial wealth gap is the lowest it's been in 20 years.” This is the standard measurement of the ratio between the two, where 6.35 is the lowest since 2001. But reporters and others could ask, isn’t it the highest in decades? The absolute difference is the largest among all these measured here.

I would think a good response is: no, our measures of inequality are scale invariant. If you double incomes, the inequality measure should remain the same. This isn’t partisan. Check out the link for inequality measures on Wikipedia and there’s a whole “properties of inequality metrics” section that discusses scale independence. We talk about the 1% and the 99%, and if you double everyone’s income the 1% and the 99% still get the same shares. This is even more important since we compare inequality across decades, even a century, from the 1980s, 1950s, and 1920s. If we brought in an 18th century farmer to discuss absolute inequality measures, it wouldn’t answer any question that’s relevant.

(You can also imagine the look of shock and horror on the faces of a team of communication professionals when they realize you would really say this to a reporter, in fact you think this is not just a great and sufficient answer, but you can’t even wrap your head around an alternative argument.)

(And in case you are wondering on the ‘lowest in 20 years’ verdict, alas, Politifact: “We rate this claim Half True.”)

Economists are trained to think in ratios and how results change if they are multiplied by constants. Our inequality measures work this way. It’s also a stronger, key assumption built into macroeconomic theory, that if you double every price and every income then real variables shouldn’t budge. That is homogeneity of degree zero.

But everyday people have concerns, in the short term, over absolute differences. I want to be clear that, by talking about absolute levels, I’m not invoking money illusion. I want to at least approximate stories that could survive rational expectations. And I think absolutes can matter because people believe their room to absorb an unexpected cost scales with absolute income. I think there are three such vibecession stories that survive this.

Three Reasons In Play

It’ll be easier with some examples, and those are best based on actual numbers. Here, from the Economic Policy Institute, are real wages by percentile, from 2019 to 2024.

Wages are up following inflation. These are big wage increases for those at the bottom. But it still nets to a real absolute increase of under $2 an hour, indeed most are under that threshold though the 80th percentile has higher absolute increases than the 20th. If the new price environment is more volatile and complicated then absolute differences might matter.

Let’s discuss these three.

Essentials

Imagine a person near the bottom of the income distribution, who is spending as much or more than they make as a result of borrowing. This makes them very sensitive to specific costs, like food, electricity, and auto insurance, all of which are hard to cut.2 Even if essentials rose in proportion, the risks of having little should make people concerned about absolute prices. But these items rose much faster than overall prices, as shown in Figure 1 above. For those with low incomes, the squeeze here is particularly difficult, and if you view utility as spending above a certain baseline, that baseline increasing faster causes distress.

Housing

I tend to be a housing-theory-of-everything person, so much so that I’m excited to say I’ll have a big housing report out in the next month or so. And the housing situation is bad.

The median sale price of a newly built home bought with an FHA mortgage, a common financing path for first-time buyers, went from about $240,000 in 2019 to around $356,000 in late 2025, up roughly 48%. The 30-year mortgage rate went from around 4% in 2019 to over 6% now, which means the same house costs more every month even before the sticker price moves. Homeowners benefit from the wealth, but they also realize any new home they are going to buy has gone up in price too, and interest rates are higher. Fully-anticipated inflation can make home-buying more inaccessible (Modigliani and Lessard, 1975). But given the even higher costs here, it is easy to see how this causes angst.

Risks

I think the absolute level also matters for tail risk situations, where sudden, unexpected costs could impose a much higher penalty even adjusted for inflation. Let’s take health care. Kaiser Family Foundation’s Employer Health Benefits Survey reports that family coverage premiums in 2024 are up 24% since 2019, comparable to overall inflation. But the average isn’t what you worry about, it’s the tail. In any given year you might face a plan that dropped your doctor this cycle, a partial denial on a procedure, or a specialty referral that sends you outside the network. And now the premium tax credits have expired, while there’s also a trillion dollars of cuts to Medicaid on the horizon.

Essentials, housing, and tail risks all survive two key tests. First, they follow Binetti, Nuzzi, and Stantcheva (2024) in that “increased complexity and difficulty in household decision-making” are a central problem people cite for inflation in surveys. All of these fit that characterization.

Second, these were made worse under Trump, so they can justify the further decrease in sentiment and economic conditions in 2025. Tariffs made food prices worse, housing was hit by tariffs, interest rates, and likely deportations, and insurance is being hit by price increases from the end of the premium tax credits and future Medicaid cuts which will devastate rural and regional health care providers. This is a pretty bad situation for people. But these are solvable problems, in fact we can solve them.

The argument of the piece is that women’s employment rates are currently being “propped up” by government policies. Which policies? She names several that trace to the early 1990s. She explicitly names and dates the Civil Rights Act of 1991. She references student loans the government backs, which come out of a 1993 law, as well as immigration, which particularly takes off in the 1990s. She also names other events from the past decade, like DoorDash and the growth of HR departments, which she believes accelerated women’s inflated employment rates.

This is part of a broader argument on the right, drawing on Helen Andrews on the “feminization” of corporate culture and institutions, and the broader “longhouse” discourse. (As a response, I’d read Darby Saxbe at The Argument.) Stepman has a reference to the viral social media skits of the Australian skincare company The Breakout Hack, as is standard in these arguments. Andrews times her argument to the idea women took over fields in the late 2010s, and makes two empirical points: women have reached half of workers by taking over new institutions, and once “institutions reach a 50–50 split, they tend to blow past gender parity and become more and more female.”

The problem with this argument is that it doesn’t fit the key labor market economic data points. Women’s labor force participation plateaued in the mid-1990s and has been flat since. As a graphic from a Treasury report shows above, it is below that of peer countries. Before that there was no particular break where it accelerated; it followed a steady trend upward from the beginning of the 20th century, a familiar U-curve tied to capitalist development. There’s been no recent takeover of industries; the fields that already employed women simply grew faster. Specific industries tend to mean-revert their gender mix.

Let’s dig in.

Women’s Labor Force Participation Plateaued in the 1990s

The most basic problem with the “women are increasingly dominating the workplace” narrative is that they aren’t. As Figure 1 shows, prime-age women’s labor force participation rate, the share of women aged 25-54 active in the labor market by working or actively looking for work, rose at a consistent pace from the 1940s through the late 1990s and then stopped.1 It has been nearly flat for a quarter century, rising only about 1 percentage point in the very strong labor market of the past few years.

We can also see this in the employer data. The Current Employment Statistics, the employer survey behind the monthly jobs numbers, tracks total payroll employment and women’s employment at the industry level. Women’s share of total nonfarm jobs rose steadily from 32% in 1964 to 48% by 1995. Since then it has barely moved, reaching 50% in 2025. (We’ll talk about that 2 percentage point growth in a minute.) If the policies since the 1990s were supposed to turbocharge women’s entry into the labor force, there is no sign of it in any of the key data sources.

Maybe the aggregate numbers are flat because older workers mask what’s happening among younger cohorts, especially college-educated ones. We can check this directly with the CPS microdata.

I don’t see it. As Table 1 shows2, among college-educated 20-somethings, women’s labor force participation rate has been about 4 percentage points lower than men’s in 1992, 2019, and 2024 alike. Among non-college 20-somethings the gap narrowed, but only because men's participation fell.

What About the Kinds of Jobs Women Have?

Has the composition of jobs changed? Perhaps women aren’t entering at higher rates overall but are concentrating in specific sectors and pushing men out. The data argues otherwise.

To start, consider three major sectors:

For manufacturing, men lost more jobs in absolute terms as the sector collapsed. But women lost a larger share of their jobs, so women’s representation actually fell. Women’s share went from 32% to 29%.

Over 16 million jobs were created in private education and health services since 1990, more than any other supersector. Women gained 12.5 million of those jobs and men gained 3.8 million. But women’s share was 77% in 1990 and 77% in 2025. The sector grew enormously, but its gender composition didn’t change at all.

When it comes to professional and business services, presumably the sector where the elusive girlboss lives, jobs grew enormously, adding millions of jobs for both men and women. But women’s share of the sector was 47% in 1990 and 45% in 2025. It was essentially flat, even as both men and women gained a lot of jobs.

We can test this more granularly. Economists have a set of 250 private-sector sub-industries that they use to gauge the breadth of the labor market.3 These cover 100% of the private-sector economy. Of them, 209 give us the number of female employees. These cover 93% of the private-sector economy; the missing 41 are largely small and in manufacturing, which would likely amplify the results we’re about to see.

For each of these 209 industries, we can compare women’s share in 1990 to their share in 2025. The gender composition of industries is overwhelmingly determined by where they started. The correlation between an industry’s women’s share in 1990 and its share in 2025 is 0.955. The regression line sits just below the 45-degree line, which shows mean reversion, not acceleration. Industries that started most female-dominated saw their share decline slightly on average, while the most male-dominated saw slight increases. This is the opposite of a takeover.

Women’s share of total employment did tick up about 2 percentage points since the 1990s, reaching the 50 percent threshold Andrews flags. How? It could be because women are taking over more jobs across all industries, an increase within industries. Or it could be because sub-industries with more women are just growing faster than the rest, like health care. That would be an increase between industries, the economy shifting toward industries that already employed more women.

Economists call this a shift-share decomposition.4 We can use these sub-industries to ask why the aggregate women’s share of payroll employment rose over this period.

The within-industry component is -0.5 percentage points. That is, industries became slightly more male over this period. But the between-industry component is +2.3 percentage points. The entire aggregate increase, and then some, comes from the economy shifting toward industries that were already disproportionately female. Women did not move into new industries. Industries that already employed women simply grew faster. That's the story.

Ceilings and Floors

But aren’t men in crisis? As you can see from Table 1, non-college-educated men’s labor force participation has been declining for several decades. Men leaving the labor force have ended up in dark places. This is where many argue the resurgence of deaths from suicide, firearms, and overdoses shows up, dragging men’s life expectancy down.

Here’s how I think about this: we want to address the ceilings for women and the floors for men. What are the institutions that keep women’s labor force participation below that of women in other countries? And what is the floor for men who fall through in the wake of changes to capitalist development and technological change? How do we keep them from falling all the way through? We can do better than invoking girlbosses.

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For those interested:

The main explanation is that net immigration collapsed. Job growth fell dramatically even as the unemployment rate remained roughly steady. Two new major papers from both the Dallas Fed and the Federal Reserve argue that break-even job growth may even be around zero now because of this decline in immigration.

These analyses generally work by projecting population growth, participation rates, and unemployment, then multiplying them together. They’re important studies and they’re difficult to get right because they depend on the unknowable future of population growth and the unknowable present of steady levels of unemployment and participation. But one thing I’ve seen less of in real-time is the cross-section of immigration and job growth. Immigrants aren’t scattered randomly across job categories. What can we see of their evolution in the monthly data?

This is a quick pass, designed to put some methods into play and spark debate. On this first glance, we can see the expected collapse in job growth for industries with high levels of non-citizen workers. But additional things jump out. The recent rebound in employment numbers over the past several months looks to be driven by this group of industries. Yet the wages for workers in these industries slowed among the fastest, the opposite of what conservatives were hoping would happen.

Methods

We bring in three things here1:

First, the diffusion index from the Current Employment Statistics (CES). This is a set of 250 sub-industries that together comprise nearly 100% of private-sector employment, giving us a broad cross-section of job growth. Since CES is a survey of businesses, we get good numbers on employment level and changes, as well as their own detailed industry code (NAICS). But as a result, we don’t know anything about the workers (except their gender).2

Second, citizenship status from the Census’s American Community Survey (ACS). The ACS collects citizenship status and provides its own NAICS code. We pull the 2024 1-Year ACS Public Use Microdata straight from the Census website. From that, for every available NAICS code, we calculate the percent of workers who are not citizens.

Third, we then match those NAICS codes with non-citizen percentages to the CES NAICS industries in a crosswalk algorithm that tries to match the industries with as much specificity as possible.3 If there’s an error in this analysis or if it is fundamentally misguided, it happens in this step. The match isn’t perfect but it works reasonably well. Of the 250 industries, 136 match at the 4-digit NAICS level, 98 at 3-digit, and 15 at 2-digit, for a total of 249 matches out of 250. The one notable issue is construction, which only shows up once in the ACS at the highest aggregation level. So the 10 construction sub-categories in the diffusion index for jobs data all get the same high non-citizen share of 19%.

The Census definition of non-citizen here broadly includes “temporary migrants (such as foreign students), humanitarian migrants (such as refugees and asylees), and unauthorized migrants.” A last methodological choice here is to keep all non-citizens, so it reflects those here legally and illegally. There is excellent work from the people at e.g. Pew that tries to disentangle legal versus unauthorized immigration levels. However, the Trump administration has revoked student visas and has targeted green card holders for deportation proceedings. For our exercise, the pressure campaign the administration has pushed on non-citizens here legally causes me to include the entire group.

The Slowdown

With that in hand, we can plot each industry’s job growth slowdown against its non-citizen share. For each of the 249 industries, I compute average monthly job growth in 2024 and 2025, then take 2025 subtracting 2024, and put it on the y-axis. Negative here means the industry slowed down. X-axis is the percent of noncitizen workers.

There’s a clear downward slope, and in particular at the right side of the distribution it’s basically all negative. The industries with the most non-citizen workers nearly all slowed down. A regression is significant but has little R-squared explanatory power.

You can also see the industry we teased in the first paragraph, the one with the largest negative value on the y-axis. That’s home health care services, which went from 13,000 a month in 2024 to 1,000 a month in 2025.

Home health aides do work we know needs to be done. It won’t be automated or done with AI. It is labor-intensive. There’s no sense that immigrants contributing to it detract from national security or keep us from upstream innovation. As this excellent overview piece last December by Shannon Najmabadi at the Washington Post showed, home health care is getting squeezed by immigration restrictions, cuts to Medicaid and Medicare, and the growth of Medicare Advantage. What are we even doing here?

Slowdown, Then Speedup

I take the 249 matched industries and divide them into five quantile groups by non-citizen share.

There’s a little lumpiness here at the top end, but I think we can proceed. Let’s make this clearer. What was the job growth in 2024 versus 2025 for those five categories?

It’s the fifth quintile, with the highest percent of non-citizen workers, that has the biggest drop, from contributing 29,000 jobs a month in 2024 to -6,000 in 2025. There is a slowdown across the board, but Q5 stands out. What everyone assumes is happening actually shows up in the data.

How closely can we see a break early in 2025? And since last November, there’s been some pickup in job numbers; what’s driving that? To look into this, here’s 3-month average job growth for the five quintiles from 2023 through February 2026.

You can see this Q5 drop happening right as the Trump administration takes office. Yet the recent pickup is also mostly driven by the fifth quintile, the industries with the most non-citizen workers. Let’s redo the comparison above but add a third bar for average monthly growth since last October.

There’s a big shift. Q5 jobs have surged since October while jobs in Q1 are actually falling. What do we think is happening here?

When you’re looking at data this granular, you have to be careful about over-interpreting what could be random noise in small categories. So I create a breadth measure for Q5: the percent of Q5 industries with positive 3-month average job growth.

It’s actually picked back up to old, broader, levels. So the recovery is fairly broad-based across Q5 industries, not just a few big movers.

What Isn’t Happening: Wages

There are several possible explanations, but let’s get one out of the way. I don’t think the Q5 pickup is explained by workers being pulled over from Q1 industries, which are declining. Q5 industries pay about $32/hour on average versus $43/hour in Q1. I also don’t think it’s workers being pulled into the labor force because we don’t see it in the native-born employment-to-population data, which is shrinking.

If employers in high-non-citizen industries were bidding up wages to attract replacement workers from other sectors, we should see wage acceleration in Q5. Here’s what we see, with both nonsupervisory and total wage growth in Graphic 6-7 and 8 respectively:

Q5 nonsupervisory wage growth decelerated by 0.69 percentage points. This is even more driven by Q5 for all-employee wage growth. This is true if we use fixed-weights to deal with compositional issues as well. Friend of the Substack Matthew Klein has also noticed a weird wage divergence among industries; perhaps this is part of the explanation.

Jobs decreasing and wages slowing at the same time is something we’d normally associate with a negative demand shock. But that doesn’t obviously characterize what’s happened with the step-up in immigration enforcement. To check if a negative demand shock is in play, we look at average weekly hours for nonsupervisory workers:

We see that hours are increasing for Q5. (They are flat or recently increasing if we look at all hours.) So not an obvious demand shock.

One story is that this is tariffs. Q4 and Q5 have a lot of workers in construction, durable goods, and transportation, all of which have had a major slowdown in job growth. Trying to estimate this versus cyclical measurements is difficult given the lack of actual aggregate slowdown in unemployment rates. But all of these workers are being impacted by the administration’s trade war.

Another more worrying possibility is that employers are using the threat of ICE to slow wages, even as hours increase, which would mean the enforcement regime is bad even for the remaining workers. We’ll get more granular data in the years ahead, but consider this post a signal flare for all students of monopsony power looking for research topics.

The industries with the most non-citizen workers had the biggest job growth collapse, they’re now driving the rebound, and their wages are decelerating. The first fact is what the break-even papers predict. The second and third are harder to square with any simple story about labor supply. Something is happening to employer behavior in these industries, and it’s not the high-wage workforce the Trump administration promised.

The Van Hollen and Cory Booker tax bills have set off a debate in Democratic politics. Both bills raise taxes on rich people roughly $1.5 trillion. They then, in turn, use those revenues to do a middle-class tax cut. According to the Tax Foundation, Van Hollen targets the $40,000 to $140,000 range for a tax cut; Booker reaches up to $280,000. There’s been significant pushback on the agenda among economic wonks. But another question remains: how did we get here, where some consider middle-class tax cuts an affirmative Democratic agenda?

There’s a clear short-term motivation for the policymakers who’ve put forth these proposals. As Groundwork’s Alex Jacquez helpfully explains in a recent American Prospect piece by David Dayen and Ryan Cooper, this push is largely driven by consultants chasing the perceived political benefit of Trump’s No Tax on Tips. I’m skeptical that those pledges drove Trump’s 2024 win, which has plenty of other sufficient explanations. And even if they did, the right response, in my personal opinion, is to fight on better and more consequential terrain like healthcare.

As for the longer story of how we got here, I don’t have the full answer, but I want to explore it through two moments, one in 2016 and one around 2008. Each is usefully anchored by a contemporaneous editorial that identified the problem as it was happening. In 2016, the Hillary Clinton campaign proposed to fund paid leave not with a payroll tax, not as an extension of Social Security, but with taxes on the wealthy. That decision foreclosed options for building universal social insurance. But it built off the earlier one, around 2008, when then-presidential candidate Barack Obama pledged not to raise taxes for those making below $250,000.

The Bush Trap

By cutting taxes for everyone in the early 2000s with an expiration date, George W. Bush effectively created a ratchet. When the cuts were set to expire at the end of 2010, Obama was boxed in twice over. He had campaigned on a pledge not to raise taxes on families under $250,000, so he could only let the top rates snap back. But the economy was fragile, with unemployment near 9.5 percent, and letting any taxes rise risked pulling demand out of an economy that couldn’t afford it. The result was the December 2010 deal: all the Bush cuts extended, top rates and all, in exchange for unemployment insurance and a payroll tax cut.

When the cuts came up again at the end of 2012, Obama framed the fight around middle-class tax cuts. Then-White House comms director Dan Pfeiffer argued “President Obama today will push for extension of middle class tax cuts. Will the GOP join him to provide certainty for 98% of Americans?”

He ended up striking a different deal, making the Bush rates permanent under $400,000 and raising taxes above that. That decision locked in a tax architecture. The vast majority of the Bush tax cuts were now bipartisan law.

To see criticism of this from when it happened, read Matt Yglesias’s 2009 American Prospect piece, “The Next Tax Revolt.” In it, Yglesias argues that Obama’s pledge didn’t change the anti-tax framework so much as find a way to survive within it, reinforcing the right’s claim that public services aren’t worth paying for. He concludes (italics in original):

Progressive taxation is an important principle. But the idea that further changes to the tax code should exclusively target the wealthy is ultimately counterproductive. Making the case may be difficult, but refusing to try to make it amounts to conceding defeat. At the end of the day, persuading people to support a more active role for government means persuading all of them that such a government is worth paying for.

Those concerns about broader taxation were kept at a distance, in part, because an era of falling interest rates, often called ‘secular stagnation,’ made debt burdens significantly less onerous. The long Great Recession, where the Federal Reserve’s interest rates remained at zero percent for seven years, from the end of 2008 to the end of 2015, made the immediate concern of getting more fiscal spending into the economy central. Meanwhile, growing awareness of inequality pushed policy attention toward the top of the distribution. Ideas like the Buffett Rule, a proposed minimum tax on incomes over $1 million, inspired by Warren Buffett’s observation that he paid a lower rate than his secretary, captured the mood.

Each campaign cycle deepened the tax threshold promise and boxed in policymakers more. Secretary Hillary Clinton championed the Buffett Rule in 2016, and Obama’s pledge was increased to $400,000 by Vice-President Biden while running in 2020. But let’s stick with 2016, and a specific decision flagged by the writer Bryce Covert at the time.

Paid Leave and Payroll Taxes

By 2016 the worst of the recession was behind us (unemployment was just under 5 percent), and there could have been space to discuss broader taxation without wrecking the economy. But the Hillary Clinton campaign kept the pledge and added an important choice: moving paid family leave financing from payroll taxes to taxes on the wealthy.

The writer Bryce Covert laid out the case against this in an important 2016 Slate piece, “Hillary Clinton’s Plan for Paid Family Leave Is Bad Policy Design“:

The proposal in Congress would [fund paid leave] by creating a social insurance program akin to Social Security. The bill would levy a new payroll tax—0.2 percent, or about $1.50 a week—on both employees and their employers to fill up the fund, and then the fund would pay out benefits to everyone eligible to receive them when they wanted to take family leave.

Clinton’s problem with this model is that, technically, backing it would bar her from making a promise she’s gotten very keen on making: that she won’t increase taxes, not by one cent, on people who make $250,000 a year or less. “Hillary strongly believes that middle-class families deserve a raise, not a tax increase,” her paid leave proposal states.

The deeper problem, as Covert emphasized, was political durability. Social Security and Medicare have become politically untouchable precisely because people pay in and feel ownership over the benefits.1 Payroll taxes had also been a highly effective model for state paid leave programs, so the Clinton approach meant abandoning a design that was already working.

Social insurance is insurance against lost wages. Payroll taxes are part of the mechanism through which workers contribute to insure themselves against unemployment, disability, sickness, or old age across generations. From the beginning of the study of social insurance, scholars knew that giving birth to children also disrupted earned wages, and thus paid leave belonged in the system.2 It was part of the unequal gendered nature of the New Deal that, as political scientist Suzanne Mettler has argued, the system divided citizens so that men got full benefits and women didn’t. Bringing paid leave into Social Security thus had long-term political benefits.

But none of this was enough to break the no-tax pledge. And during this period, the wonk class had largely stopped making the civic case for payroll taxes. The New Deal understanding that social insurance carries a deeper meaning, that “keep your government hands off my Medicare” reflects something real about how people relate to programs they pay into, that universal contributory programs can be the foundation of democratic solidarity, had faded from the conversation, left largely to heterodox thinkers like Michael Lind. Covert is a friend and I remember discussing this with her at the time. Though I then saw the case for moving the tax burden to the rich, I increasingly think she got this argument right as it occurred.

That is not to say there isn’t a straightforward case for higher taxes on the wealthy funding a broad set of programs and social insurance. You hear from pundits that Americans want Scandinavian social insurance without Scandinavian taxes. I actually don’t buy that’s the right point. The comparison is harder than it looks, and healthcare is the main reason. As both the economists Saez and Zucman in The Triumph of Injustice and Matt Bruenig at People's Policy Project have shown, adding in employers’ U.S. health spending as a tax dramatically increases the regressivity of taxes and largely closes the international gap. That international difference is fundamentally about the way we fund health care.

But as Democrats shape their policy agenda going forward, there is something worth exploring here: whether social insurance can be expanded through two tracks. The rich pay their fair share in our deeply unequal society. But also we affirm a broader commitment that everybody pays in and everybody benefits. That is what was underpinned Social Security in the first place, and it may be what’s needed to get out of the trap we’re in now.

Yet there remain two central ironies. For all the Democratic reluctance to ask anything of the broad middle class, it was Trump who imposed broad-based taxation on working-class individuals through tariffs. He just did it in the worst possible way, for the worst possible reasons, based on lies, and with the revenue going nowhere useful. More, for all that George W. Bush was thrown into the trash heap of history in 2009, here we are in 2026, unable to increase middle-class taxes above the path he set in 2001. And, after the Iraq War we still live with3, we are now waging another full-on war against Iran, another country he named to the Axis of Evil in his 2002 State of the Union. His strategery of playing the long game won, and we all lost.

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2

In his classic Social Insurance (1913), the first textbook on the topic published in the United States, the actuary I.M. Rubinow translated a 1912 German taxonomy of the types of social insurance, with my red text box flagging what we’d now call paid leave among all the important types:

It’s both refreshing to know the central risks of the capitalist modes of production were well articulated in this early 20C book I recommend you check out, and depressing to think the insufficient progress made on addressing them since then.

I don’t think the severity of this has been conveyed well in the media. Coverage tends to focus on CPI inflation, which is the majority of inputs into PCE, but it is not the Fed’s target and is being pulled lower by its heavier weight on housing. Important methodological debates have taken up airtime. And when PCE is discussed, it’s generally in year-over-year terms, which smooths over how much things have heated up over the past three to six months.1

All of this is before the war with Iran, which started on February 28th. The problem existed before the energy shock that is now ongoing. WTI crude is running roughly $30 above its pre-war level, and the hit to production and consumer spending would on its own put the central bank on pause. As Bob Elliott notes, an oil shock is “like the opposite of a productivity boom.” He finds that historically “the best case scenario is a pause to easing and a worst case scenario is hikes into weakness” in order to maintain expectations.2 The fact that inflation was already a problem before this shock only makes the Fed's position that much harder.

Overall

Figure 1 is the chart I follow most closely, the breakdown of PCE inflation by type across several measures. The top line is total PCE inflation over several dates and intervals, and the subsequent lines are how much each part contributes to that total number.

PCE inflation has increased. In 2024, it ran at 2.73%, and there was a clear runway for roughly 0.3 percentage points of decline in housing, which did materialize. That would have gotten inflation closer to target at around 2.4%, with the central bank doing a careful dance through the last mile of disinflation.

Instead, inflation picked up. It ran 2.91% across 2025. Over the past three and six months it ran 3.47% and 3.19%, respectively. It has been accelerating, getting hotter the more recently you look. And that includes banking the housing disinflation that came through on schedule.

What can make this situation better?

Tariffs and Goods

Core goods is a new and significant contributor to inflation, and some of that is tariffs. As a kind of tax on consumption, tariff-driven price increases should be looked through as long as inflation expectations aren’t breaking, and they haven’t. There’s significant debate about how much of the pickup in goods inflation is tariff-driven. The Trump administration is in the awkward position of arguing that core goods inflation is global and structural rather than temporary and driven by their tariff actions, which, if true, means the Fed should probably be tightening.

If we assume half of goods inflation is tariffs, total PCE inflation still sits at or above 3% on the three- and six-month horizon. If we assume all of goods inflation is tariffs, and it would be 0% otherwise, overall inflation is still higher than 2024. We’ve lost ground going into 2026.

We can also just look at non-housing services inflation. This measure was originally emphasized to deal with goods prices spiking on shortages, and here too we see a spike in recent months. It’s contributing a full 2.04 percentage points, the entire target, over the past six months. There’s a problem here.

Market-Based Measures

Another move has been to emphasize the imputed elements of core non-housing services, particularly portfolio management services. Stephen Miran of the FOMC emphasized looking at market-based core inflation in a December speech, arguing it showed genuine cooling. Recent months have gone against that analysis.

As Figures 2 and 3 show, market-based core inflation and market-based core excluding housing have accelerated in recent months:

Footnote on creation below.3 Here in Figure 4 are those two series, along with market-based core services ex housing, showing the annualized percent increase in the respective index. So market-based core inflation increased 2.86% over the past six months, compared to 2.5% in 2024.

Every one of these measures has accelerated in recent months. Over the past three, six, or twelve months, they all run higher than their 2024 values (annualized). Miran, in his speech, described 2002-2007 as a period in which overall inflation was at target, and thus a good benchmark for these market-based measures. They are all running much higher than that time period. (Hat-tip to Omair Sharif making this point here.) The various supercore measures, the ones specifically designed to strip out noise and find the underlying signal among imputed prices, are all heating up faster than we’d expect.

Labor Market

Another argument is that the labor market is softening enough to bring inflation down on its own. I think this is more complicated than it looks. The unemployment rate has been essentially flat for almost a year, ranging roughly between 4.3 and 4.4 percent since last May. Average hourly earnings have moved sideways. There is weakness in hiring and openings, and I think young college graduates show a surprisingly high level of unemployment given the headline rate. Personally, I tend to think there are two unemployment numbers, one for those under 26, which has increased, and one for those above, which is more normal.

But the case that the labor market is decelerating fast enough to take pressure off nominal spending growth isn’t there in the data right now.

Fiscal Impulse

The oil shock is coming. But before that, there was another shock already in the pipeline: the One Big Beautiful Bill Act (OBBBA). Below in Figure 5 is the fiscal impulse tracked by the Brookings Institution Hutchins Center Fiscal Impact Measure, which I’ve followed closely over the years. There’s a government shutdown which is shifting Q4 2025 spending to Q1 2026. But they also note an increase in GDP from “the stimulative effects of the OBBBA on both purchases and taxes.”

Going into the year, the fiscal impulse from OBBBA will be substantial in Q1, and the tariffs do not offset it. Ironically, a massive gas price spike may have arrived just in time to choke off the stimulative effects of OBBBA which was already pouring into a higher-inflation environment. But fiscal impulse is still in play as the Fed looks ahead through 2026, and this is before any additional war spending for Iran, which could get very expensive and become a large additional fiscal impulse if the conflict continues.

How Far Off Is the Fed?

Figure 6 gives us a sense on where the Federal Reserve’s own Taylor Rule would land with a couple of assumptions. Assuming, as the Fed does, that r* is 1% and NAIRU is 4.2%, then the year-over-year value of PCE would have the Fed raising rates. Assuming all core goods inflation would be zero without the tariffs in a year-over-year number has rates basically right where they currently are (yellow line).

This graphic gives a sense of what the Fed was thinking; it understood itself as too tight (the blue Fed funds rate is above measured inflation). However there hasn’t been continued disinflation, and in recent months inflation has picked back up. This graphic reflects year-over-year inflation, which is lower than the inflation rate over the past six or three months, which would have rates from a Taylor Rule even higher. The more the Fed looks at recent information, the more they should be worried they got ahead of things.

There are things the Trump administration could do to try and ease this. Pardon the entire FOMC and back off independence, pick selective tariffs to execute, and stop deportation quotas might be a baseline if they want to wage an ongoing war against Iran. But they seem unlikely to try and prioritize anything, much less their crises putting pressure on aggregate supply while pushing demand.

Maybe this all turns over. Though note that in recent years the second half has been the cooler part of the inflation calendar, even with seasonal adjustments, which makes it running hot especially worrying. The decisions the central bank made made sense as they were happening. But at this point the Fed should not be in any hurry to cut further. That was true even before a major energy war landed on top of everything else.

If you're at the Powell press conference today, all your colleagues are going to have questions about Iran and criminal charges against Powell, so you want something unique. But you likely are stressed hunting down oil and energy sources for stories and having had to watch your kids home from school for the D.C. storm that never came. So steal this blog post for your question! Some questions that'll get attention:

• The three- and six-month PCE numbers, both overall and supercore, are running well above target and have been accelerating even before the energy shock.

  • At what point do shorter-horizon measures like these change the Committee's assessment of where inflation is headed?

  • How much would this acceleration have to continue in order for the Committee to consider rate hikes?

  • Looking back, does the Committee believe the December cut was premature given how inflation has evolved since?

• Your own Taylor Rule, using the Committee’s r* and NAIRU estimates, suggests rates should be at or above where they are now, whether or not you look through tariffs, and especially above with more recent PCE inflation data. How do you make the case that the current stance isn’t accommodative, especially this far from the inflation target?

• Even zeroing out all goods inflation, non-housing services is contributing 2 percentage points on a six-month basis. Is the tariff look-through framework relevant when the non-tariff components are the ones accelerating?

• According to estimates, the OBBBA is delivering a meaningful fiscal impulse into an economy where inflation was already reaccelerating. How does the Committee factor that into its outlook?

It’s gotten pushback from economists and Fed watchers, especially those who question whether policymakers would really sit on their hands in the face of a disinflationary spiral. You can read a lot about it online. I’m going to use this moment instead as an opportunity to start digging into the macroeconomics of AI in a more disciplined way. This is material I’m learning in public here, please feel free to leave critical and technical comments.

My hypothesis is that AI lowering wages will make it harder for the Federal Reserve to stabilize the economy, and will create an ugly situation in terms of labor share and consumption (both central in Citrini’s story). When I embedded that argument in a leading DSGE framework, one of those predictions held up and the other didn’t.

The simplest, representative-agent New Keynesian (RANK) model doesn’t get us very far. We need income distribution to matter for Citrini’s story, where workers have high marginal propensities to consume (MPCs), capital owners have lower MPCs, and shifting income from one to the other affects demand. At the same time, I don’t want us to get lost in the pyrotechnics of a full heterogeneous-agent New Keynesian (HANK) model, where a continuum of agents generates their own wealth distributions.

A two-agent New Keynesian (TANK) model splits the difference. The model I’m going to use is from “Workers, capitalists, and the government: fiscal policy and income (re)distribution” (2021) by Cristiano Cantore and Lukas B. Freund. They are kind enough to include their Dynare .mod files, which makes replication and modification straightforward, a huge plus. Cantore and Freund describe their model as “tractable laborator[y] for understanding various macroeconomic experiments,” which is exactly what we need.

In this model there are two agents, a worker who works to earn labor market income and a capitalist who doesn’t work and receives profits. (I’m already sold.) Both are forward-looking rational agents. Workers can save in bonds but face portfolio adjustment costs. That friction makes them partially constrained and more responsive to current income than a permanent-income consumer, so they have a higher MPC. The rich capitalist class smooths their entire life’s income independently based on their permanent income.1

In order to explore this, I take their medium-scale model and do my best to insert a task-model loosely based on Acemoglu-Restrepo (2019) (especially read through these Jon Steinsson notes). We take a permanent innovation shock to the automation of tasks with three scenarios: one where the shock is fully labor-augmenting, benefitting workers, a second where it is fully labor replacing, and one in-between.

We end up with 43 endogenous equations that we send into MATLAB to be solved.2 (It’s ironic to model the collapse of software while relying on proprietary MATLAB.)

Here’s what we get:

In everyday language, red is where workers get screwed and blue is where workers fully benefit from AI. 0 is the steady-state, or what things were settled at prior to the shock. Don’t worry too much about units, we’re here to see the signs.

What do we see?

Interest rates: All three lines dip below zero, meaning the Fed cuts rates after the AI shock in each scenario. But the cut is dramatically larger under the blue scenario than the red one. This seems backwards. Shouldn’t we expect the scenario where the labor share falls to require deeper cuts?

What I think is happening is mechanical but unintuitive. In the labor-augmenting case, productivity rises faster than wages because of nominal rigidities. Unit labor costs fall sharply, generating deflationary pressure. The Taylor rule responds with aggressive cuts. In the displacement case, human marginal product falls along with wages. Deviations in marginal cost, which is what drives inflation in New Keynesian models, barely moves. So despite the collapse in labor share, there is little disinflation and therefore little pressure for rate cuts.

These kind of New Keynesian results, where inflation is largely about deviations of marginal costs, might feel like its own kind of science fiction. But it’s worth considering. It’s also funny to imagine the Federal Reserve staff who will have to explain to potential new bosses that AI doesn’t magically mean you get to do whatever you want to interest rates.

Workers: It’s not great. In the scenario where workers are augmented by AI they get more consumption and work fewer hours and the labor share fall isn’t that bad. In the displacement case, real wages barely rise, hours initially increase and then drift down, and labor share collapses. The modest rise in worker consumption comes primarily from higher labor supply rather than higher wages. They even, for some periods of the shock, work more hours than the baseline. Their labor share plummets, and their increased consumption is mostly from just working more hours.

I am still not sure if I’m doing justice to the AI shocks. I get a similar result when I instead just use a productivity shock to the model and make it so wages are fully inflexible, they are basically impossible to update. This extreme example gives us the same kinds of movements.3

From a standard business-cycle perspective, this remains within the reach of conventional macro stabilization. In the displacement scenario, inflation barely moves because marginal cost barely moves, so the Taylor rule doesn’t face dramatic tradeoffs.

That said, I’m still uneasy. If AI were sufficiently disinflationary, or if nominal rigidities behaved differently than in this calibration, we could find ourselves closer to the zero lower bound and facing a more prolonged demand shortfall than this baseline suggests.

I don’t like AI art. But one thing I always do to test the picture and video models is prompt it with “the world as if John Maynard Keynes’s ‘Economic Possibilities for Our Grandchildren’ came true.” In the two videos above you can see Sora, OpenAI’s video producer, from its public launch in October 2025 (top) to a video yesterday (second), each with that prompt, and chart the progress.

So I feel a visceral pain seeing a DSGE model that gives us periods where workers are compelled to work more hours as a result of AI-based skyrocketing productivity. My worry now is less that AI causes a recession. It’s that AI can raise output while worsening workers’ position, a distributional transformation without a traditional macro crisis.

That distributional question of who captures the AI dividend and how it feeds back into demand and political economy is where the real work lies. I’ll be digging into that more in the months ahead.

2

Code here. For the sickos, here’s the whole thing. 9-11, 13, 15, 33-34 are the lines where our additional task-based elements show up:

3

Funny enough here the Fed cuts less because output is so high because workers are working a lot more hours with their wages frozen. But as a quick check with a completely different methodology it gives us similar results.

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But much of the discussion has been caught between people building applications as fun personal hobbies on one end, and massive enterprise software on the other. Most of us who use these professionally will live somewhere in the middle. (The discourse is also wedged against whether AI will cause large-scale unemployment or otherwise destabilize society.)

I’ve integrated these terminal tools into my workflow over the past two months. Below are three specific ways I’m using them that are genuinely new, and where I’m not going back to how I worked before. Terminal AI compresses the setup and robustness-checking phase of knowledge work. I’ll also explain what makes them different from the browser-based chat tools, and whether I, Mike Konczal, am about to be automated out of a job. My relevant background is in a footnote here.1 This is not a setup guide, though I’ll try to explain ways these examples might be applicable to you. That will be helpful, as these tools are not going away.

Use 1: Real-Time Analysis, Without the Prep Work

I maintain a set of R files, the statistical programming language, for instant analysis of the economic data releases. Those numbers generally come out at 8:30 a.m. several times a month, and by 8:35 a.m. I need to know the big takeaways.

I’m not trading on this information, so I don’t need to know within milliseconds. But I do make statements that other political actors use, and accuracy is vital. For instance, I was interviewed by the New York Times shortly after the jobs numbers last week, and I want to be able to say something useful, interesting, and, most important, true.

My old workflow was that I’d take one or two hours the day before the major monthly data releases and manually code a few new graphics based on what I expected would be important. Maybe I post them or maybe I don’t, but they help me keep track of how the economy is moving, and they force me to think of new pieces I may have been missing.

Now I just ask Code/Codex to write them. It’s usually 95% of the way there on the first go. I check the math and get it fully there with one or two more exchanges.

What used to be 4–5 hours of work in an average month is now 15 minutes. The reduction in time allows me to ask even more questions and prepare more materials. I have a lot of experience in this, so I’m able to proofread the code and I double-check that it is doing what I want it to do.

Why not just use the Claude or ChatGPT webpage for this? I used to do this. But there is significant copy-and-paste overhead. The old way meant copying code out of the browser, pasting it, running it, copying any error messages or changes that needed to be made back into the chat, and repeating. With the terminal tool, the LLM writes the code and runs it in the same folder. It can see whether what it did worked, investigate failures on its own, and I see the result after every pass without touching a clipboard.

Why not just let the AI find the interesting result? The LLM actually does a poor job of this, as it lacks the context for what people are searching for or what stands out. It can summarize news reporting on those numbers after they circulate, but influencing that coverage is the point of the exercise.

So if your job involves monitoring something that changes on a schedule, such as earnings releases, polling averages, clinical trial updates, or monthly sales figures, and you currently spend the hours before and after each release manually refreshing Excel models or rerunning Tableau dashboards, this is the use case that will feel most immediately different. The tool doesn't just help you go faster. It makes it cheap to add new angles of analysis instead of being locked into what you could cover in limited prep time.

Use 2: Building Out a Report

Last Monday, I published a blog post about the affordability crisis. Check it out! Near the beginning are three graphics I made on the price, spending, and indexing of essential items. To do this I created a folder on my desktop and dropped in the relevant data files. Then I gave this opening prompt:

Note I ask it to create a qmd file (Quarto Markdown), which can render to html, pdf, or slides with minimal changes. You’ll probably be using a similar method in the future.

It got very close on the first pass. I scanned the code to check the methodology, spot-checked one item from each by manually calculating it (knowing the algorithm means that if one is correct, all of them are).

From there the prompts are mostly about cleaning up:

I’m just talking into the microphone at this point, deciding which essentials to include (some categories overlap in ways the LLM wouldn’t know) while simultaneously tweaking colors, legends, titles, and sizing, while it re-renders the graphic.

One thing I worry about is that if I’m not mucking around with the data myself, I’ll miss important side observations. But even in this pass I notice things: cigarettes show a huge price increase (it’s very funny that the LLM thought cigarettes are an essential), and I wonder if it’s driven by excise taxes, the same way tariffs are mechanically increasing the price of goods in 2025 and 2026. Something to investigate.

A benefit here is that stress-testing became trivial. Want to check whether your finding holds under a different index, or a different date range? If you are using Excel this is a nightmare, and even with statistical programming it’s a slog. But I get this done in a minute and can be more confident my results will hold under scrutiny.

Doing this part of my blog post last week would previously have taken 1-2 hours, with an additional 1-2 hours if I wanted to stress-test the indexes to make sure the results are robust. It took me 15 minutes to do this here.

So any future project that involves empirical work will be built out of a folder where the terminal LLM is executing work that I check. The terminal allows it to do much more complicated work, faster.

So if your job requires you to produce regular analytical reports, for clients, for leadership, or for publication, you will use this to cut out difficult first steps. Often the bottleneck is not the writing, it’s getting from raw, unorganized data to a defensible visual that you trust and can defend. The terminal tools compress that step dramatically, and more importantly, they make it cheap to check whether your finding survives different methodological choices. That robustness-checking step is the one most people skip under deadline pressure. Now you don’t have to.

You Bought a New Car and Auto Inflation’s Really Taken Off

I really want to emphasize how much of the work for any report is this basic setup, the data-wrangling and first-pass results to see if the results are worth exploring in full. This can be time-consuming, especially if it doesn’t pan out. Unless you try it, I can’t describe to you how fast the terminal LLM can get through this stage, and keeps the building blocks in place to iterate on.

Two examples I explored recently. There’s an interesting new paper from Louie, Mondragon, and Wieland on whether supply constraints actually explain house price and quantity growth across U.S. cities. I wanted to get into the guts of the paper before I formed an opinion and wrote about it in the future here. But the actual data, merged across multiple datasets of prices, quantities, and restrictions, all from different sources, each with its own formatting and naming conventions for local areas, is a nightmare.

I put the data in a folder with the paper and had the tool merge it all in a single 10-minute session. It closely reproduced their key graphic, giving me a foundation to explore their work more fully. (I was told by one of the authors that the research assistant who led that data merging had a lot of big feelings about this.) Data merging in the browser tool, by contrast, had never worked reliably for me; but being able to iterate in place, with the LLM reacting to exactly what went wrong and adjusting, got it done.

Or take the standard “Phillips curve” style analysis of inflation. From around 2022-2023, a lot of people were running these regressions adding variables that followed the timing of inflation, like global supply chain stress metrics or the vacancies-to-unemployment ratio. I was frustrated because those don’t predict inflation out-of-sample and basically anything with an up-and-down pattern during those years would “predict” inflation.

Back then I had some code that estimated inflation with those variables along with other things that just happened to pop in 2021-2022. First, a dummy variable with a value of 1 in 2021-2022 and 0 every other year. Second YouTube views for Olivia Rodrigo’s “good 4 u” video, which debuted in May 2021, just like the inflation spike. What would be the best predictor of inflation?

I never got around to finishing it all at the time. But then James Stock and Mark Watson, the godfathers of empirical macro, published a paper in late 2025 finding that basically only COVID deaths explain the inflation in a component analysis. It’s a big victory for the cost-push shock side of the inflation debate. We should do a full post on it, as the entire episode traces back to a single supply-side shock. That made me want to dig my idea back out.

Sure enough, Codex one-shotted it.

It got the data and did the analysis with one call.2 From the R-squared above, reflecting a few clean-up calls, Olivia Rodrigo does a better job estimating inflation than vacancies-to-unemployment. (The unit is log(good 4 u views + 1)). If you put them all together, only the dummy variable and the “good 4 u” variable are significant.

You will, no doubt, have more normal and productive projects you want to explore and see if they are worth developing fully. The real work is finding good questions and understanding how to make the results rigorous. Using the terminal gets you to where you can focus on what matters the most.

Use 3: Anticipating Arguments You Might Not Know Ahead of Time

Keeping with that blog post on essentials: I do not know the complicated microeconomic derivations as well as I should. What differentiates Hicksian from Marshallian demand does not come naturally to me. It’s been almost twenty years, and I still occasionally have nightmares about that one chart from Chapter 3 of Mas-Colell.

But my recent post on essentials (and a previous one on DoorDash) touches on how groceries and shelter behave differently from restaurants and delivery services as income changes. I wanted to understand how rigorous demand theory would assess what I’d written. So I asked the terminal to take a look:

AIDS here is the Almost Ideal Demand System, a standard model in microeconomics for estimating how consumer spending shifts across goods when prices and incomes change. (Angus Deaton and John Muellbauer published their groundbreaking work in 1980, roughly two years before they would have named it literally anything else.) It generated this analysis:

I have no idea if this analysis was executed correctly. But I do know that on a first pass, an approximation of the analysis doesn’t disprove my instinct that food and shelter behave like my post argues they do. My argument wasn’t conditional on this, and I don’t advertise it. But I now think I’m not going to take obvious incoming criticism on something I missed. And more importantly, this gives me an incentive to learn more and continue digging on this topic.

A lot of analytical work is anticipating good-faith skepticism. You know the argument you're making, but you're less sure what the specialist counterargument looks like. These tools let you run a first-pass stress test against these other frameworks, cheaply enough that you can do it before every major piece rather than only after you've already taken the incoming.

Is It Game Over?

All of this makes me better at my work. My software background helps, as I can proofread the code, double-check what it’s actually doing, and catch errors in the methodology. But I’m also wondering if I should be switching fields ASAP.

Who knows what the future holds? But I keep running a version of this test: I put the Survey of Consumer Finances data, a triennial survey by the Federal Reserve on household incomes, debts, and assets, in a folder, and ask the AI to find the most interesting results. It gives me simple summary statistics. I ask it to find both unique policy insights and business opportunities, and it gave me very generic ideas. I asked it to use linear regressions, figuring it could just p-hack something, and it gave me very little. I keep running a version of this, asking AI to find what’s interesting about a project independent of any direction from me, and it really can’t do it.

So this still looks like an extreme version of labor-saving technology. It makes people faster at a given set of tasks, and it lets you cover more ground with the same resources. Which effect dominates, fewer people or more output per person, is an open question. My experience, at least for now, is that it can complement people who know how to use it, but risks shortcutting those earlier in their careers before they’ve learned the building blocks.

Last summer I was so conflicted about giving up RStudio for Positron, a VS Code fork that integrates Python and Quarto more naturally into R development. Now I keep Positron open to check while I work in the terminal. Whatever happens with AI bubbles and buildouts, the economics of my own work have changed permanently. I can do in twenty minutes what used to take half a day. This means I ask more questions, check more assumptions, and cover more ground. I still believe the tools can’t identify what’s interesting or draw the right conclusions on their own. But they’ve made exploring what’s right a lot cheaper.

useR!: EconTwitter to the White House

I gave a short 15-minute talk last summer at the useR! conference for the R statistical programming language, titled “From #EconTwitter to the White House: Real Time Economic Data with R.” (A sentence I still can't quite believe is autobiographical.) The pitch is four reasons to use R, convincing analysts not to use Excel and economists not to use Stata.

There are a lot of fun tidbits from my experiences in and out of government working on data releases that you might enjoy.

Slides here. Even as more statistical analysis will be done through LLM terminal prompts rather than working directly in IDEs like RStudio, the reasons to base your code in R are still relevant. The ease of the tidyverse, the grammar of graphics framework, and the variety of data libraries available make it a great foundation to iterate with AI tools.

Yale Budget Lab

I was on a panel this past week at the Yale Budget Lab and the Tobin Center for Economic Policy titled “Tariffs, AI, and Our Economic Futures,” looking at the first year of the Trump second term. Moderated by business journalist Alexandra Scaggs, with Michael Faulkender (a Trump first-term Treasury official and Deputy Secretary of the Treasury, the number two, from January to August 2025) and Natasha Sarin (Budget Lab President, and former Biden Treasury official).

Around 32m55s, I pointedly bring up the IRS giving ICE access to ITIN tax records for undocumented workers, which Faulkender signed off on while at Treasury. There’s a back and forth among the panelists that is interesting if you follow the IRS/ICE story, one that ends with a chill coming over the room. (You’ll know when it happens. This was recorded before the Washington Post broke a story about related improper breaches.) There’s also a debate starting around 10m44s about why exactly Trump is doing tariffs the way he is, where I push back on the administration’s rationale.

Columbia / Groundwork Collaborative Webinar

I did a webinar with the Initiative for Policy Dialogue at Columbia and the Groundwork Collaborative examining the impacts of Trump’s economic agenda.

It was chaired by Joseph Stiglitz, with Groundwork’s Alex Jacquez, University of Michigan economist Sarah Miller, and Bharat Ramamurti. It’s a great overview. Even as someone following this closely, I’m still learning and amazed at the damage the Trump administration is doing to healthcare. Sarah Miller’s comments (starting 29m) on healthcare were informative.

Stiglitz & Konczal in the Boston Globe

Stiglitz and I keep our buddy cop routine going with an opinion piece in the Boston Globe covering much of what we discussed there: “The high cost of Trumponomics.” We take on Trump’s recent Wall Street Journal op-ed claiming his tariffs have brought about an economic miracle, and walk through why those claims are unmoored from the economy around us.

I was also quoted in the New York Times about recent data releases and the Washington Post on why affordability has become a campaign buzzword.

If you want me to appear or write for your next event, feel free to reach out!

Current Mood: Burn it down.

Current Music: St. Vincent’s excellent cover of Big Black’s “Kerosene.”

You’ve probably seen some version of Graphic 1 below, which shows real median household income from Census. After falling during the post-pandemic inflation surge, real incomes recovered. By 2024–2025, they had ended up higher than in 2019. This result holds across multiple ways of looking at this data, including hourly wage data. Yet consumer sentiment is stuck near historic lows, about as pessimistic as during the financial crisis and the depths of the Great Recession.

These two facts coexist. And the politics of “affordability” has rushed into the gap between them, as politicians and advocates try to speak to people’s persistent anxiety about prices and living costs.

But there has also been a subtle, yet real, pushback against this focus. That pushback usually starts from the observation that today’s affordability debate is inseparable from the 2021–2024 inflation episode. From there, two critiques follow.

The first is a money-illusion story: people fail to recognize that their incomes rose alongside prices, so their distress reflects confusion rather than material harm. This argument often emphasizes that incomes at the bottom of the distribution rose faster than those at the top, producing a durable wage compression. And yet polling consistently finds that lower-income households report more dissatisfaction with inflation, not less.

The second critique follows naturally. If what people really want is their old price level back, that is simply not something policymakers can deliver. Broad-based price declines tend to occur only in deep recessions, and even then only modestly. So it is said to be dangerous politics and bad economics to make affordability central. Doing so risks promising something impossible, or worse, flirting with economic collapse as a policy goal. Matthew Yglesias has made a version of this argument, affordability is “just high nominal wages” and “basically just anger at inflation,” as have others.

This conversation echoes what you often hear about the so-called “vibecession.” The term was originally coined by the writer Kyla Scanlon in 2022 in a nuanced way, to describe self-reinforcing pessimism. Today, it’s more often weaponized to imply that consumer sentiment is untethered from material reality, a reflection of the circulating bad vibes.

One could correctly say that these affordability problems existed in 2019 and are independent of the inflation wave. But we should take the bait. Over the past five years I’ve been haunted and utterly consumed by a keen observer of the debates over inflation. And I think it’s worth being explicit about why a wave of inflation can generate real affordability problems.

These mechanisms point toward specific policy interventions, many of which have already bubbled up in political campaigns. And they also help explain why President Trump’s current policy agenda is depressing sentiment, by placing pressure precisely on these channels. There are several, but the first is what matters the most.

1. The Essentials Squeeze

The simplest story is that essentials have been squeezed: their prices have risen faster than overall inflation, even as households are forced to devote more of their budgets to them.

Consumer prices have increased 26.1 percent over the past six years. But not all price increases are alike. Graphic 2 has some prices that have increased faster than overall prices during that time, ones that are pretty important for people. Food, shelter, transportation, hospitals, and veterinarian services all pop out. They’ve increased faster than both overall prices as well as core services (which rose 27.8 percent during this period).

Last week we discussed DoorDash. From Graphic 2 above, we can see that the price of food away from home (which includes delivery services like DoorDash) increased faster than groceries (food at home), and people shifted their spending to groceries. Given that spending on groceries tends to decline with income, we can understand this shift as a penalty people experience. Even if incomes stay the same, vibes (i.e. utility) decline.

Let’s create two definitions of essentials. Core essentials are groceries and shelter. Essentials are groceries, shelter, healthcare and transportation. Taking the Consumer Expenditure Survey (CEX) data, we can see in Graphic 3 that most households are devoting a larger share of their budgets to these essentials than they did before the pandemic, especially at lower incomes.

Is that because these goods are getting cheaper? No. Using PCE data, we can construct price indices for these bundles, weighted by their consumption shares.1 As Graphic 4 shows, both measures of essentials inflation have run well above overall PCE inflation.

The green line is overall PCE inflation, the blue line is an inflation index of groceries and shelter, and the red is the blue plus healthcare and transportation, all indexed by their consumption weight. As you can see, and as hinted from the CPI data in Graphic 2, the essentials are running much faster than overall inflation. This was true before as well. From end of 2013 to 2019, core essentials rose about 14.3%, essentials rose about 10.5%, and overall PCE rose about 7.9%. But this was supercharged in the recent period.

When prices rise and budget shares still increase, standard demand theory tells us these goods are necessities. If utility comes from consumption above some baseline floor, then a rising share devoted to essentials leaves less room for discretionary consumption and lowers welfare, even if total income keeps pace with total prices.

This also helps explain why lower-income workers are angry about inflation even as their wages rose faster than average. Households traded down to cheaper and generic food brands, a trend coined “cheapflation,” which drove up the prices for the basics that lower-income families depend on most. And as Catherine Rampell noted at the peak of inflation, CEX data show that lower-income households typically consume more than they earn and face more volatile work hours, both of which magnify the pain from price spikes.

The essentials squeeze alone is enough to provide microfoundations for the vibecession and affordability crisis. But it’s not the only channel. Let’s look at other ways inflation creates welfare losses that survive rational expectations. I’ll sketch them briefly.

2. The Housing Tilt Problem

This one is big in the older literature but is oddly absent from today’s debate.

As Modigliani and Lessard (1975) showed, standard fixed-payment mortgages interact badly with inflation even when inflation is perfectly anticipated.2 Higher inflation raises nominal mortgage rates, preserving real interest rates. But because mortgage payments are fixed in nominal terms, higher nominal rates mean much higher initial real payments that then fall rapidly over time. Real payments are front-loaded.

MacGee and Yao (2025) show, using modern life-cycle models, that when borrowing constraints bind at origination, this front-loading tightens credit for first-time buyers. People with steep expected income growth but limited current earnings must qualify against today’s higher nominal payment even though that payment will shrink quickly in real terms as wages rise.

If this is too complicated, it’s just another reason the housing market has been a mess.

3. Planning Under Uncertainty

Binetti, Nuzzi, and Stantcheva (2024) find from surveys that the most commonly cited consequence of inflation is not any specific price increase, but the complexity inflation introduces into everyday decision-making. Eighty-five percent of respondents identify this as a major effect, and more than a third rank it as the single most important one.

When prices are stable, budgeting is boring. When prices are volatile, households must constantly plan when to buy, what to delay, how much to save, and how to interpret nominal changes. That cognitive effort is costly.3

4. The Cost of Money Is Part of the Cost of Living

Another issue is that we largely exclude borrowing costs from how we talk about inflation.

As Bolhuis, Cramer, Schulz, and Summers (2024) show, incorporating borrowing costs into measures of consumer sentiment explains a lot of the U.S. sentiment gap. From a household perspective this is obvious. The interest payment on a new mortgage is several times higher. Interest on new car loans is up sharply. People, especially lower-income ones, experience this as part of the cost of living.

Official inflation measures abstract from interest costs for good monetary and economic reasons. But that creates a gap between measured inflation and lived affordability.

There are others, which we might discuss in the future, but this is a solid grounding.4

Conclusion

When the prices of necessities rise faster than everything else, when housing becomes mechanically harder to access, when planning gets more cognitively costly, and when borrowing is more expensive, welfare can fall even if average real incomes recover.

The good news is that these binding constraints are solvable problems. We have many ideas for tackling housing, healthcare, and food costs. Simple steps like not slapping century-high tariffs on foods like bananas on the fantasy that they create leverage, or not cutting a trillion dollars from Medicaid to cut Amazon’s corporate tax bill in half, would be a great start.

But the first step is to believe that what people have been screaming about their lives for the past several years actually exists. Even a representative agent, forward-looking and fully aware of all the parameters surrounding them, can feel the vibecession.

Nothing gets the internet going quite like a round of hand-wringing about whether too many people, especially young ones, are spending too much money on delivery services, distorting their sense of budgeting and of how the economy actually works.

However, DoorDash Discourse is mostly wrong.

People are not, in aggregate, spending more on eating out and delivery. They are spending more on groceries at home and less on food away from home. This is especially true of young people.

The last round was kicked off over the weekend with “Freedom With a Side of Guilt: How Food Delivery Is Reshaping Mealtime” from Priya Krishna at the New York Times:

In 2024, almost three of every four restaurant orders were not eaten in a restaurant, according to data from the National Restaurant Association. The number of households using delivery had roughly doubled from 2019, just before the pandemic, the group said. And in a survey last year, about one-third of American adults told the association that they ordered food for delivery at least once a week. […]

That disconnect extends to restaurants, many of which have accepted a trade-off: Delivery helped keep them afloat during the pandemic and expanded their customer base, but they now have fewer in-house diners.

This predictably produced a wave of takes about personal irresponsibility and moral failure. A representative tweet comes from Matt Yglesias: “Part of the affordability crisis is pretty clearly people just refusing to be thrifty — you should not be spending a quarter of your salary on DoorDash.”

I have no doubt that food delivery has picked up and normalized since the pandemic. The relevant economic question, though, is not whether delivery exists. It’s what it is displacing. Is delivery mostly replacing groceries cooked at home? Or is it mostly replacing eating in restaurants?

Positive Analysis

We have a place to look. The Bureau of Labor Statistics Consumer Expenditure Survey (BLS CEX) is a nationally representative annual survey of consumer units that reports annual spending by detailed category. It is the primary federal source for household expenditure patterns and is widely used to understand cost burdens, to inform CPI weights, and to benchmark how budgets shift over time. The CEX makes it possible to compare budget shares across time and across demographic groups such as income quintiles and age.

The latest data for 2024 came out in December 2025, delayed because of the government shutdown. This data can help explain why people’s experience of the economy has been poor even when aggregate income growth keeps pace with inflation. A rising share of budgets devoted to necessities will make households feel worse off.

Here is “food at home” and “food away from home”1 as a percent of total consumption2, for all families and by income quintile.3

The dotted-line and middle number are the average for 2015-2019. Here’s those numbers in a simplified chart:

What do we have here? Start with the post-2019 period. Overall, Americans are spending about the same share of their total budgets on food. For the bottom 40 percent of households, the food share is modestly higher.

But compositionally, there has been a clear shift. Less of the food budget is going to food away from home (which includes delivery and takeout). And much more of the food budget is going to groceries.

In other words, society as a whole is reallocating food spending toward cooking at home, not away from it. DoorDash Discourse gets the direction wrong.

Normative Analysis

Is this shift necessarily bad? And does it support affordability as a real political and economic concern?

I think yes, for two reasons.

First, the shift is bigger for the bottom 40 percent of incomes. Lower-income households are doing much more of the adjustment toward food at home than higher-income households. This is not just a uniform cultural pivot toward staring at your phone rather than going to a restaurant. It is a stratified adjustment likely driven by constraint.

Second, the longer-run trend matters. Historically, as incomes rise, the share of spending devoted to food at home falls. You can see that above, which is the percent of the budget spent versus real total expenditures (the same is true for nominal) for each quintiles of each year from 1980 to 2024. There is a very strong decline in food at home as people get wealthier.4

That doesn’t just mean people dislike cooking. It means that, with more resources, people tend to allocate more toward convenience and variety. A lot has changed in preferences and attitudes these past seven years, but the fact that more of the budget has shifted to a good that declines with income should give us a strong presumption of disutility. The affordability crisis is real.

What About the Kids?

Ok, but can we still blame the kids?

The same exact thing happens, with the pattern being most pronounced among people under 25. They experienced the largest shift toward food at home. The 1.7 percentage point shift toward cooking at home for those under 25 is 2.5 times larger than the 0.7 shift for the general population.

The kids are alright. They're just broke and cooking at home to make up for it.

I want to look at the labor market with fresh eyes as we go into 2026. And the first place I want to do this is the debate over weakening prospects for people in their 20s with college degrees. This was a highly debated topic in 2025, with a focus on whether this was consistent with an overall weakening labor market or whether it was a sign of the impact of AI in the workplace.

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My loosely-held hot take a year ago would have been, echoing Joey Politano making this argument at Big Think, that AI would increase jobs in the short-run. My view is AI makes productive workers far more productive, but its unreliability makes it difficult to automate positions that require certainty over how outcomes are determined. But entry-level workers might be vulnerable, both because of the way the career ladder is structured and because bosses, wanting to cut corners, would try to haphazardly replace workers with AI there first.

And we did see unemployment increase for new college graduates. But, as friend of the blog Guy Berger has noted, unemployment is also higher for young people without a college degree, consistent with a weakening labor market and low hiring. Overall unemployment is up, from 3.5% in the summer of 2023 to around 4.4% now, and we expect that impacts younger people more.

Is there a way to distinguish if it’s increasing more than we’d expect?

Regression Relationship Approach

Let’s build up to how we’re going to approach this. There are many unemployment rates, whether for race, education, gender, or age. But they all tend to track the headline unemployment rate you hear about in the news, called U-3 unemployment, even over decades.

Let’s take two groups: (1) men and (2) workers aged 45-54, and see if their rise in unemployment tracks what we’d expect given the overall increase. (Note that the data sourcing will be important throughout this: here the data is from the BLS itself, seasonally-adjusted and monthly.)

Let’s run a regression on their individual (log) unemployment rates versus the overall (log) unemployment rate for the years 1994 to 2019. We get an equation, which we then use to project forward to 2025. We take the actual increase in U-3 unemployment and, given the historical relationship, we ask: what would we expect the specific unemployment rate for those two subgroups to be?1

Figure 1.a shows the full history back to 1994. The dotted lines are projections based on the log-log relationship between each group’s unemployment rate and overall unemployment, trained on data through 2019. This is a pretty good fit across multiple business cycles.

Figure 1.b zooms in on the post-pandemic period, where we are projecting forward based on the 1994-2019 relationship. Both men and 45-54 year-olds track almost perfectly with their expected values. The labor market slowdown since 2023 has increased unemployment across the board, but these groups are rising exactly as much as their historical relationship with U-3 would predict. Nothing unusual here.

Multiple Groups

Let’s extend this across all demographic groups. I take seasonally adjusted monthly unemployment rates from the BLS by gender, race, age, and education, regress each against the overall unemployment rate 1994 through 2019, and calculate the average difference between actual and predicted values over the most recent 2 months:

A positive value means higher than expected from 4.4% unemployment. These results are consistent across many date ranges. Most groups cluster right around zero, with their unemployment rates exactly where you’d expect given the current level of overall unemployment. But young people (16-19 and 20-24) and BA+ stands out, with unemployment notably higher than its historical relationship would predict.

Many, including myself, have noted the statistically significant increase in black unemployment over the past year. Why doesn’t that flag above? Let’s look at this more closely:

Here we can see that black unemployment has sadly returned to its longer-term trend. The tight labor market and full employment environment of 2021-2024 especially benefited black workers, with the black unemployment rate falling much further than historical patterns would predict, and the black-white unemployment gap collapsed to record lows. It’s a terrible fact that this progress has reversed in this past year. As the labor market has cooled, black unemployment has unfortunately returned to its historical, and far too high, relationship with overall unemployment.

Young People

Now to focus on younger people. Let’s take data from the New York Fed’s The Labor Market for Recent College Graduates website. This longer-standing measure was given a lot of attention last year in an article from Derek Thompson. These are 22-27 year olds. Recent grads have a completed BA or higher; young workers are everyone else. The researchers make their own seasonal adjustment. This is their creation from the microdata. Let’s run the same regression from 1990 to 2019, their starting years, and project:

(EDIT: as requested by readers, added a new figure that shows the relationship back to 1990, and extends the recent figure back to 2017. Still same analysis.)

As you can see, people without college degrees who are 22-27 more or less track exactly what we’d expect given the labor market slowdown. But young college is much higher than our trendline we’d expect from the slowdown.

To be clear: college-educated unemployment is still lower than non-college unemployment, and everyone's unemployment is up. Let’s not get tripped up on this on social media. What's unusual is the gap between where college unemployment should be historically and where it actually is today.

Now, as John Burn-Murdoch and Guy Berger have each noted in different ways with excellent analysis, college-educated workers are likely to be new entrants to the labor force in their 20s compared to those entering the labor force earlier.2 So in a slowing labor market it might not be correct to compare these two age groups.

To dig into this, I take the BLS microdata from IPUMS and create my own categories. I use 12-month rolling values instead of seasonally adjusting the data. I take 3-year age bands (so, e.g., 22-24 for age 23) for college-plus and high-school-plus (HS+) without a BA. The College+ series starts at ages 21-23, and the HS+ series starts at ages 18-20. I then run a log-log regression on each from 1989 to 2019 and take the average difference between the projected and the actual over the past 3 months (ending in September, to avoid the month of missing data). I then chart it across the age spectrum:

To read this graphic, for someone who is 23 (the age-range 22-24) with a BA+, they have unemployment that is over 2 percentage points more than we’d expect based on their historical relationship with overall unemployment. That excess fades by their late 20s. This gap is also high for non-college workers in their early 20s, but their peak is lower than the college one and it collapses faster.3

So my current read is that young people have higher unemployment than we’d expect at 4.4% overall unemployment. It’s especially higher at its peak and throughout their 20s for people with a college degree. Their recent unemployment rate is historically a surprise. The bad kind of surprise.

This was organized before Senator Elizabeth Warren called out the Abundance movement in a speech about the party’s future, which kicked off fighting on X, as well as posts by Ben Winsor of the Open Markets-affiliated Liberty and Power Substack and Jonathan Chait of The Atlantic.

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Given the interest in the topic, I thought I’d share the notes I prepared, written up after reflecting on the discussion.

On tensions between Abundance, as popularized by Ezra Klein and Derek Thompson’s book, and more populist approaches, I think this is mostly a solved problem as we enter 2026.

Democratic campaign veterans like James Carville and David Plouffe emphasize populist messaging focused on affordability and economic pain. Virginia Governor Abigail Spanberger and New York City Mayor Zohran Mamdani each spent their first days announcing serious efforts to identify ways to streamline housing production. Spanberger launched her Commission on Unlocking Housing Production, and Mamdani created his LIFT and SPEED task forces. We are arriving at a synthesis that emphasizes both challenging high prices and bad actors while also prioritizing longer-term supply-side issues.

This isn’t surprising. Whether creating an effective new agency in the CFPB or rebooting an older one in the FTC, progressives care about how well the government works. There is also a long-standing streak of YIMBYism across parts of the left.1

But when it comes to rank-and-file progressives, there’s still a genuine confusion and discomfort with Abundance. This isn’t just a positioning fight within the Democratic Party. To keep things grounded, here are three specific questions that point to bigger issues.

1. “Does Abundance require the federal government to overrule state-level AI regulation?”

The Abundance Institute is one of the leading policy advocates calling for the federal government to block state-level AI regulations, a live issue during the tax bill debate and again recently with a potential executive order.

This led to funny moments where Abundance people were mad that someone might assume the Abundance Institute speaks for Abundance. The Institute had to clarify that they actually had the name first, years before the book. This created some genuine confusion: who exactly speaks to the boundaries of this movement?

If Abundance means YIMBYism, building more housing and preempting exclusionary local zoning, I am 100 percent a supporter and encourage people to contribute. If Abundance means e/acc, letting tech and AI rip across every domain regardless of consequences, I am not in favor and encourage people to be critical. When I encounter Abundance in Washington, D.C., I’m never sure which of these two I’m going to get. It takes time and cognitive work to figure out which is which, time most people don’t have.

A movement driven by magazine writers and academics will find it difficult to police the boundaries of what’s included and excluded. When the Center for American Progress writes convincingly against state AI preemption, we know how that stands institutionally. If an Abundance-affiliated writer argues against it, does that count for the movement? There are big stakes here: $150 million in AI lobbying is being deployed against elected officials, including on this basic preemption question. How should progressives understand what gets included and excluded?

2. “Does Abundance require endorsing charter schools?”

You can sometimes see Abundance thinkers say that the theory calls for charter schools. Setting aside your own views on charters, they are clearly a different matter than the YIMBYism policies Abundance has been associated with. But they do fit if Abundance is meant to be a centrist omnicause.

The omnicause is a term for the gravity well that pulls politics into ideological alignment. If you are progressive or conservative on one topic, you tend to end up the same on all topics. There are interpersonal, institutional, and technological (e.g. social media) reasons this happens. Right now you can read many Abundance authors and thinkers argue that the term should function as an omnicause for centrists.

This is how I understood Abundance in early 2024, well before the book came out. Abundance was pitched then as a self-conscious moderating “faction” within the Democratic Party. The surprise bestselling success of the book and the increased prominence of YIMBYism in Democratic electoral politics were in some ways a temporary detour away from this idea.

To avoid sounding conspiratorial, consider two prominent YIMBYs who are uncomfortable with this move. Editor-in-chief of The Argument Jerusalem Demsas writes: ‘Don’t make abundance the moderate omnicause.’ She worries that Abundance will get “watered down into a sort of umbrella term used by vaguely pro-business centrists who want a new slogan.” If this happens, Abundance will lose the more radical implications of YIMBYism (and the practical political anchor it provides) and instead become a buzzword for people who want to moderate the party.

And former policy director for California YIMBY, Ned Resnikoff, had a Roosevelt Institute paper, “Lessons from YIMBYism: Taking ‘Abundance’ Back to Its Fundamentals,” on how to apply YIMBY principles across economic topics. The piece starts with an important intervention about how Abundance has become so “ideologically capacious,” often “pursuing entirely different and mutually exclusive objectives” where it isn’t “possible to build a coherent synthesis that accommodates” them all. As a result, he’ll just stick with describing YIMBYism instead:

I use the term YIMBYism instead of abundance throughout to emphasize that this essay is about a particular policymaking approach, and not about the larger ideological debates that have become part of the abundance discourse.

I think Resnikoff has ended up at the correct place. YIMBYism has important insights across domains. The rest of Abundance, though, can feel like a confusing grab-bag of priorities.

3. “Are the Affordable Care Act and lowering prescription drug prices part of Abundance?”

A recurring move among some Abundance advocates (it’s in the book) is to characterize the Affordable Care Act as a pure demand subsidy, throwing money at coverage without any effort to manage supply or delivery.2 This is confusing. From creating the Center for Medicare and Medicaid Innovation, to implementing bundled payment initiatives, or launching the Hospital Readmission Reduction Program and the Medicare Shared Savings Program, the ACA spent enormous energy on healthcare delivery and “bending the cost curve.”

It’s the right of every writer to say their idea has never been tried before. But if more direct measures to bending the cost curve are written out of Abundance, where does this leave us? Take a voter worried about their kid seeing a doctor. Imagine a candidate responding that their main effort will be removing certificate of need requirements for new hospitals and taking on the AMA to expand residencies. And that’s it. How does that help this parent now? And how can they be certain it helps them later? Will new hospitals actually get built? Will insurers pass along that supply as actual access? Will it translate into care?

These are good ideas, but supply-side reforms work on long timelines, and the benefits and their distribution are uncertain. The causal chains are long, and voters are right to be skeptical of politicians promising that they will automatically do things and help. And as Bharat Ramamurti notes, without some measure to directly address people where they are, it is difficult to pass these supply-side reforms.

The Affordable Care Act, whatever its limitations, got over 20 million people coverage while reducing projected healthcare spending by hundreds of billions of dollars relative to baseline. This looks like Abundance to me: better outcomes at lower prices by reducing unnecessary spending and harnessing the government’s capacity to operate at scale. If the ACA gets read out of the Abundance framework, what should progressives, who want to expand Medicare and create public options, make of the agenda?

The book itself seems conflicted on drug prices. Klein and Thompson note that European countries achieve lower costs because their governments negotiate prices, versus our “hodgepodge of private and public insurers who do not coordinate and do not effectively negotiate,” which they characterize as weak “state capacity.” But they don’t follow through on this logic. And the broader Abundance conversation is even more muddled. I’ve spoken with people adjacent to this world who suggest that real Abundance means abandoning the prescription drug price controls in the Inflation Reduction Act, with the Democrats deprioritizing lowering drug prices through negotiation because of the risk to pharmaceutical innovation.

If Abundance is a supply-side complement to traditional liberal priorities like healthcare, then that’s valuable. But it can come across as if it’s meant to displace or subordinate those priorities instead.

It’s not surprising that progressives are confused by what Abundance is offering. If it’s YIMBYism plus supply-side complements to traditional liberal goals, that’s a coalition worth building. If it’s a centrist omnicause that wants to replace liberal priorities with smaller goals unlikely to win politically or deliver substantively, and whose only checks against business capture are writers otherwise busy getting people to click, like, and subscribe, progressives are right to be skeptical it’s a good direction to go.

I enjoy writing empirical pieces that surface new arguments, work that’s too specific for traditional outlets but too substantial for social media. Twitter/X no longer supports that kind of work, and Substack does. And 2026 is going to be a consequential year, so I want a place where I can be part of the public conversation as it happens.

My initial goal is to write one post a week. Sometimes I’ll do more. I want to grow my readers. I don’t know what a realistic target is, but my stretch goal is to triple to 9,000 subscribers by year’s end. So please consider subscribing and adding me to your recommendations.

I plan to follow the secret of blogging as described by Max Read:

But the key lesson, the thing I would impart to any aspiring bloggers, content creators, or newsletter proprietors, is that the cornerstone of internet success is not intelligence or novelty or outrageousness or even speed, but regularity. There are all kinds of things you can do to develop and retain an audience [...] but the single most important thing you can do is post regularly and never stop.

Which in practice means I’ll stress less before hitting publish, posting without fear of perfectionism, knowing I will write again soon.

This Substack uses a sans-serif body text font because empirical and ideological work should feel clean, modern, and legible, and because I’m building toward a futurism of endless economic possibilities.

I’m turning on paid subscriptions, though I will keep everything free. Consider subscribing if you want to support this endeavor.

Disclosures

If I’m asking you to read, engage, promote, and support this Substack, I should make some disclosures. My full-time job is with the Economic Security Project, where I’ve led their policy and research team since March 2025. I expect to make more than 90 percent of my income from that job. This Substack is on my own time and independent of that work.

I recently served in the White House in 2024. That means I can state that I “favorably adjudicated Tier 5 investigation; Top Secret/Sensitive Compartmented Information (TS/SCI) clearance granted.” So this Substack is not compromised and displays “unquestionable loyalty to the U.S.” (My favorite part was an F.B.I. agent asking me about being on the editorial board of Dissent Magazine, and me getting to clarify that “Dissent was founded as the non-Stalinist magazine of the mid-century Left.”) It also means my personal finances are public. They are very boring, and I’m flagging them here so you know there’s no financial shenanigans in what I write.

I use AI daily for research work, particularly with data analysis. I’ve used R for 20+ years and spent time as a software engineer, so it’s easy for me to sanity-check AI code in real time.

For posts, I write a first draft myself, and then do three AI passes. The first is as an editor, asking what needs more work, what can be tightened, and what phrasing is unclear. I’ve been writing as a contributor for over 15 years, so I have a sense of what great editing looks like, and approach AI in that way. The second pass is fact-checking and anticipating objections. The third is proofreading.

I think AI images are fascistic and will try not to use them.

Hope you join me on this trip. As I am learning, please leave any suggestions for what makes a Substack work well in the comments.

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With my colleague Becky Chao, I wrote an Affordability Framework (pdf) for my new job at the Economic Security Project. Even as we began drafting, it was clear that affordability was becoming a central political focus, but also one in need of a fresh economic understanding of the problem.

It was well-received, with write-ups in Vox and Politico. I discussed it on The Realignment podcast, and Becky discussed it on the Liberal Currents podcast. Mark Schmitt wrote about the political dynamics of affordability and got what we were trying to do. I plan on expanding the framework further; expect more in the coming months. If you haven’t yet, check it out.

Another Thing I Enjoyed Writing

For Democracy Journal, I wrote The Abundance Doctrine, a joint book review of Abundance and Why Nothing Works. Rereading it now, I think it gets both the strengths and limits of the argument right. Law professor Noah Kazis gave it first place in his ‘review of the initial reviews’ competition.

Best Help to Stop Doomscrolling

I took the social media apps off my phone, which is a big help. But the biggest improvement was reviving a very Web 1.0 technology, the RSS feed. I started using Inoreader, though any reader works. More sites than you’d think have RSS feeds. Substacks all have them on the main page with /feed, such as here, for this one.

Take an hour or two to move your favorite reads over and customize it. You regain control over what you see and how you see it. Nothing is trying to hijack your attention or push you to engage beyond simply reading.

The late Aaron Swartz helped develop RSS at the age of 14, a technology closely aligned with his lifelong commitment to an open, decentralized internet. Aaron was a friend, not a close one, but one I did some work with and still miss. He is still remembered fondly by those who knew him, as recent pieces from Ryan Grim and Henry Farrell show. It makes me angry that the state prosecuted him for downloading files, something AI companies now do openly, at scale, in the service of turning the internet into something more enclosed and less human. Rebuilding an RSS feed is a small act to remind you of an earlier, better version of the internet.

Best Low-Stakes Personal Crisis

I was born in 1979, and turned 46 this year. I used to just think of myself as a very young Gen-Xer until a decade ago, when Anna Garvey coined the term “Oregon Trail Generation” to describe those of us born between roughly 1977 and 1983. (I will not be using the term Xennial.) Others have written about this cohort before: old enough to remember a pre-internet childhood, young enough to come of age alongside the web, but before social media colonized daily life. As Anne Helen Petersen describes that experience, “In 1999, the internet was not yet real life.” This in-between state fit.

But I think this year all of us have to pick a side. Gen-X, as a political and cultural bloc, is now widely coded as Trumpy and MAHA, and in 2024 they were among his strongest-leaning cohorts. That politics feels alien to me. Obviously this isn’t true of everyone, but it has become a dominant Gen-X posture, one I increasingly don’t recognize as my own.

Looking at it now, it makes sense that Gen-X’s vague distrust of authority, an ironic refusal of commitments, and a lowering of expectations have turned into measles outbreaks and deportation detention camps. It’s telling that many of the people who went Trumpy from liberal, left, or moderate spaces are now in their mid-50s, the core Gen-X years. For at least some, what once passed as ironic distance hardened into a politics comfortable with cruelty so long as it carried the aesthetic of anti-authority. (And the best book on this happening, Doppelganger by Naomi Klein, came from a Gen-X leftist who could see it happening in real time.)

Meanwhile, the millennial reconsideration has infiltrated my social media. As a recent ICYMI podcast described it, “millennial cringe” became “millennial optimism.” There are all these posts about living in Brooklyn and writing online in the early 2010s. And it’s like, yes, my blog did move me across the country to Greenpoint in 2010 to write online for a policy startup, and I even wrote a cringe, earnest blog post announcing it.

So the award for the best low-stakes personal crisis is whether or not to mentally rebrand as an elder millennial, perhaps the eldest millennial.

Best Fight I Failed to Break Up Between My Kids

<Scene> My two daughters, then 5 and 3, were in the backseat of the car while I was driving. 5 had a new interest in the Ghostbusters, just the theme song, not the actual movie. 3 loves to play a game where she says “no” to everything, even factual descriptions of the world. I, on request from 5, started playing the theme from Ghostbusters.

5, to 3: This song is about the Ghostbusters.

3: No it’s not.

5: It is! It’s the theme song for the Ghostbusters.

3: No, it is not.

5, getting upset: But it is!

3: No.

Me, to 3: Just tell your sister this song is about the Ghostbusters.

3: No, it is not.

Me: I think it’s a commercial in the movie? He’s saying to call the Ghostbusters.

3: No.

<5 has started to cry>

Me: The Ray Parker Jr. song Ghostbusters is on the movie soundtrack for the movie Ghostbusters. He wrote it for the Ghostbusters.

3: No.

<5 is wailing now, just absolutely lost it, tears down her face>

Me: Listen to the words. He’s not afraid of ghosts, busting ghosts makes him feel good. This is describing what it is like to be a Ghostbuster.

3: No. It isn’t.

5 stopped crying, after a while.

Watch I Wore Most This Year

I kept it basic. I bought a Casio G-Shock GW-M5610U-1 at the beginning of the year. This was a year of survival, of watching some pretty bad stuff happen. I wanted something stripped down for it. It’s a classic for a reason. I recommend the upgrade for solar charging and auto-time.

Last year was an early-1960s Omega Seamaster De Ville. I was (and still am, but less) daycare broke so I needed a dress watch that conveyed seriousness, but was also not crazy expensive. I originally found this in a small alleyway shop in Kensington. It was perfect, and I’ll be bringing it back out next year.

Meme of the Year: I’m a Mommy/Mamacita

@celebsnapzHuda telling nicolas that she is a singlemom#Lovelsland#nicloveislandusa #loveislandusa #huda #chelley #loveislandusaseason7 #loveisland #foryoupage#fyp #viral#foryou #fyp #loveislandusaseason7

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This short exchange from Love Island between contestants Huda and Nic, where Huda explains she’s a mom and Nic doesn’t get it, causing Huda to give an Oscar-worthy laugh to his questions, is my meme of the year. At a baseline, it required no understanding of the show, which I’ve never seen.

But it’s a perfect meme for two reasons. First, you can play it several different ways depending on your mood. You can do an ironic-yet-poignant address to yourself on becoming a parent:

@itscaseyjoeI just know 25 year old me is gaggged! How did this happen 😂 #loveislandusa

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You can use the remix for a crazy baby shower theme:

@nadiarassoulhad to use this sound at the baby shower 🤭 #mamacita #babyshower #loveisland

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But you can play it up wholesome, Peppa Pig can join.

But the other, more important, reason it is a perfect meme is that you can play it equally from both sides. You can enjoy it from the perspective of being confused that someone is a parent, while also being confused that someone would be confused to learn someone is a parent.

So you can plug in when your friends with no kids come to visit:

@courtneyyykingMommy? Mamacita?? #loveisland #loveislandusa #mamacita #imamommy @Love Island USA #huda #nic #peacock

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But also when someone without kids realizes that their coworker is raising a child on the same paycheck they get:

@elc_illawarraMOMMY…MAMACITAAAAA💃💃💃#workhumor #trending #loveislandusa #fyp

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Plus the more general trend of people having kids later, as noted by someone worried their 36-year-old sister has become a teen mom:

@cherie.brookeWe’re both teenagers wdym you’re pregnant? (it’s just a skit)

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In a year where the politics of kids went to extremes and have the potential to polarize, it’s good to know the best memes are able to navigate and communicate across these large boundaries. But I’m still not going to watch Love Island.

I am deeply worried that President Trump has brought about a collapse in the governing capacity of American political institutions. The post-1970s balance of power rested on a Congress interested in defending its powers, political parties not beholden to a single individual, and courts that were not fully polarized. Trump stepped into that vacuum of countervailing power and filled it with personalized executive rule, systematically dismantling agencies and weaponizing them under his control. What we took for granted about the state has fractured, and I haven’t seen anything close to finding an alternative.

But I am no longer worried that President Trump has created a durable political economy that realigns the electorate. At the beginning of the year I worried we were in a 1936 or a 1984 moment where, without the electoral blowout, Trump would execute an agenda that addresses the challenges of this stage of capitalist development while securing a new economic and political center of gravity.

That hasn’t happened. It now feels borderline absurd to say it could have happened after this past year. It’s not just the bad sentiment and polling numbers, or the weakening labor market. There’s no theory of the case for what they are doing and why they are doing it. The centerpieces of this administration, the tariffs and the deportations, are unpopular. The tariffs are politically radioactive. The justifications for the tariffs change month by month. President Trump has somehow been outmaneuvered by the Democrats into political ownership of rising health care costs in 2026. And that’s even before the trillion dollars in Medicaid cuts, which the smarter Republicans realize will fall disproportionately on the aspirational coalition they had hoped to assemble.

What Went Wrong?

Why is this? Trump’s own ego, grievance, and transactional corruption are no doubt part of it. Trump coins, Trump battleships, The Trump-Kennedy Center. Trump is building a new Mar-a-Lago on top of the White House’s East Wing while CEOs bring him personal gifts. It’s also been a mean and grim year, a parody of right-wing wokeness, under President Trump. Trump began his first presidency with the idea of “American carnage,” but the viciousness and vindictiveness the phrase invokes hasn’t fully materialized until now.

The administration’s staffing decisions reflect a generational and ideological shift, the groyperfication as John Ganz describes it, that prioritizes cultural grievance over policy development. Who cares about coming up with a GOP plan for health care anymore? DOGE letting a bunch of kids loose with AI, thinking they’d reboot the government and eliminate the deficit, turned out to be a failure except for the lulz along the way. But groyperfication goes further. Their vibes bubble has them believing their mission is to engage in a culture war whose outlines are confusing even to the most internet-brain-melted, far afield from the non-based, normie goals of trying to secure the coalition that elected Trump in 2024 on a permanent basis.

But beyond those, I think this is the inevitable result of a coalition that dreams to be several contradictory things at once: a libertarian’s dream of a hollowed-out state, a protectionist’s managed economy, a global hegemon unburdened by obligations, and a hardliner’s deportation police state. Politics is trying to manage these contradictory dreams at the same time, but in 2025 it seemed like it was trying to go in every direction at once.

The Hyundai-ICE raid is the example that stands out to me. In September 2025, ICE agents raided the Hyundai electric vehicle battery plant in Ellabell, Georgia, detaining 475 workers, mostly South Korean nationals, over allegations of visa fraud and unlawful employment. Here’s a clear conflict between two priorities. Some in MAGA want to reindustrialize America and build strategic regional allies to isolate China. Some want no immigrants at all and reject the idea that we should use visas to bolster our productivity and knowledge.

Instead of picking a path, the administration did both. They did the raid, humiliating those running the plant and the South Korean government, causing an international scandal. They then turned around and reversed course, with Trump saying he was “very much opposed” to the raid. It could have been handled more easily. If the administration wanted some action on the immigration front while keeping the factory running, they could have picked up the phone. Hyundai and South Korean officials would have answered. But they didn’t choose. They ended up trying to do both and did neither, causing major problems instead.

Everything Bagels

The inability to direct among different pulls of their coalitions calls to mind the writer Ezra Klein’s 2023 description of “everything-bagel liberalism,” where “sometimes [the government] tries to accomplish so much within a single project or policy that it ends up failing to accomplish anything at all.” This description didn’t particularly hit the intended target of the CHIPS semiconductor grants. There’s an excellent overview of the process of CHIPS by the people who executed it, a Substack called Factory Settings hosted by the Institute for Progress, and I’m very happy they are doing it. Knowing a bit second-hand about that process, it accurately reflects it, all the strengths and weaknesses. They are dishing the dirt.

The “everything-bagel” term was a contentious one inside liberal politics, because of the implied attack on high-road employment and the coalitional politics required to pass bills into laws. You can read rejoinders by Ben Beachy here and here. But I think the concept is very useful to abstract up a level from that: what are the higher-level conflicts within your coalition, and how do they get settled and prioritized?

Because “everything-bagel authoritarianism” does make sense to me. Let’s consider what some of the goals the MAGA Trump coalition wants to accomplish:

• Mass deportations beyond securing the border or targeting criminal offenders.

• Reindustrialize America as a matter of national security, innovation, and employment.

• A libertarian attack on the administrative state, to strip the government of any power to regulate, especially for Silicon Valley.

• Isolate China as a global rival, decoupling from Chinese supply chains.

• Stop peer nations from ‘free-riding’ on the United States, and have a more aggressive stance against traditional allies and the rules-based liberal international order.

These goals are in direct tension. One can, with leadership and smarts, navigate the tensions here. But nobody appears to be trying to do that.

One place we see this conflict is between the libertarian attack on the administrative agencies versus the interest in reindustrializing America. Attacks on and firings at agencies like the Department of Energy, Commerce, and the EPA come into direct conflict with ambitions around reshoring supply chains, accelerating domestic manufacturing, and outcompeting China in strategic sectors like batteries, semiconductors, and advanced materials.

Right out the door in March DOGE made major cuts of the staff responsible for the CHIPS implementation of semiconductors in the U.S. Commerce Department. Half of the Department of Energy’s Loan Program Office (LPO) took deferred retirement alongside employee caps being announced. As The American Prospect’s Ryan Cooper noted, there’s a dark irony to Elon Musk attacking LPO after he himself got an LPO loan for Tesla, one of its major success stories. Clean energy initiatives, which are where China is taking the global lead, are being eliminated, often in the middle of the process.

Beyond that, if we’re going to upskill the domestic labor force into manufacturing, who will do all the jobs those workers were previously doing? We were near full employment, so it is difficult to discuss moving some workers into fields unless we understand where they were coming from. There’s hand-waving assumptions that deportations will increase automation, or bring native-born workers out of the sidelines. I don’t see any reason to believe that, largely because the proponents aren’t offering grounded empirical arguments for them.

The other place is on the global stage, something I’ll say I follow the dynamics of less closely. But perhaps because I don’t, the dissonance of trying to use economic statecraft to both isolate China while also attacking peer countries stands out even more to me. If the goal is to actually try and build alliances to keep China’s exports in check, why spend the last year mocking Canada as a potential 51st state of the USA and threatening to purchase Greenland? You see this in the National Security Strategy (NSS), which shifted from China in the first term to the “civilizational erasure” of Europe in the second.

Consider India. As a result of Trump’s actions this year, India now faces the highest tariffs when exporting to the United States (50%) of anyone else in the world. This is higher than China (47%). Do we want companies to decamp from China to India or not? This has sent the Modi government to meet with China for the first time in seven years to try and normalize relations, directly against decades of U.S. foreign relations interest. I understand anti-India sentiment is normalized among the younger groyper staffer class, but what is even the goal here? And who is trying to navigate it?

These goals are genuinely in conflict. You can’t simultaneously gut the Commerce Department while executing complex semiconductor policy, or deport workers while reshoring labor-intensive manufacturing, or isolate China while alienating Europe and India.

It can’t be said enough: if you want to dismantle a liberal, pluralist democracy by empowering a strongman who promises to purge corrupt elites, bypass a paralyzed legislature, and subjugate civil society and the business class to personal loyalty tests administered by the Executive, you need to be able to tell parts of your far-right coalition “no.” That is the core of the everything-bagel analysis. But they aren’t doing that. Which means the libs might have a chance?

This isn’t just the general malaise surrounding the economy right now. The Trump administration came in with four plans to reorganize the labor market. None of them came to pass. Next week I’ll sketch out why these ideas failed. For now, it’s worth laying them out clearly, because the gap between the theory and the reality explains a lot of what we’re seeing.

First: Rebalancing Health Care Jobs

At the start of 2025, the Trump administration argued that the economy was far weaker than it appeared. The headline job numbers, they claimed, were misleading, propped up almost entirely by health care and government employment.

Stephen Miran, then chair of the Council of Economic Advisers, put it bluntly in March:

“73% of all jobs created in [the last] two years were due to government and government-adjacent sectors [...] like education, sectors like health care [and] the short-term pain is coming from the reorientation of the economy, from the government to the private sector.”

Treasury Secretary Scott Bessent echoed these sentiments in February. According to Reuters, he argued that “growth in the past 12 months has been concentrated in public and government-adjacent sectors such as health care and education.”

Moving workers was a broader strategy. Here’s how the Department of Government Efficiency (DOGE) put it in their ‘Fork in the Road’ materials encouraging federal workers to take deferred resignation: “We encourage you to find a job in the private sector as soon as you would like to do so. The way to greater American prosperity is encouraging people to move from lower productivity jobs in the public sector to higher productivity jobs in the private sector.”

I did not find this 2024 labor market story convincing at the time and wrote about it here. But they did. So their first order of business was reducing the proportion of jobs coming from government and health care, with the assumption, spelled out more in a minute, that they would create enough jobs to replace them. How did that go?

With health care jobs accounting for more than 100 percent of net job growth in some months, clean comparisons become tricky. But looking at private education and health care, the bugaboo of the early second Trump term, in 2019 they were 29% of job growth. For 2023 and 2024 it was 45% and 50% of job growth. In 2025 it has been 113% of job growth. If you include government jobs as well, the share rises from 73% in 2023–24 to 88% in 2025.

Health care continued to do the heavy lifting, while the rest of the labor market collapsed. The promised handoff from government-adjacent employment to private-sector dynamism never materialized.

Second: Tariffs to Reindustrialize

Tariffs were supposed to reindustrialize the country.

The administration argued that higher tariffs would reverse decades of decline in manufacturing and goods-producing employment. This was framed not just as an economic adjustment, but as a historic restoration of American industry.

The Liberation Day tariffs materials made this explicit: “The President’s reciprocal trade agenda means better-paying American jobs making beautiful American-made cars, appliances, and other goods.”

Commerce Secretary Howard Lutnick pushed the vision even further. In a widely circulated interview, he described a coming wave of reshored production:

“[An] army of millions and millions of human beings screwing in little screws to make iPhones -- that kind of thing is going to come to America. It’s going to be automated and great Americans, the tradecraft of America, is going to fix them, is going to work on them [...] it’s going to have the greatest resurgence of jobs in the history of America to work on these high-tech factories, which are all coming to America.”

This was always framed as a longer-term project. But even on its own terms, the question is straightforward: did the administration at least begin to bend the trend lines?

No. That’s not happening.

Here is the data for 2025 with trend lines from December 2022 to 2024. I incorporate the preliminary annual BLS benchmark revision, which aligns monthly payroll estimates with more accurate administrative employment counts, to the earlier data, reducing 2024 job gains. (Data pulls and estimates here.) There is no visible break from the preexisting trajectory, let alone the beginnings of a resurgence. There’s actually further deceleration. This might be for the best, as goods-producing jobs pay, on average, less than private-sector service jobs. But whatever is meant to happen, it hasn’t started yet.

Three: Deportations for Native-Born Benefits

Beyond creating new jobs, the administration argued it could reclaim jobs through mass deportation. By removing foreign-born workers from the labor market, native-born workers would supposedly see their job prospects improve.

Vice President Vance has been especially explicit about this theory. In August, he tweeted: “I was told six months ago that Americans losing jobs and the foreign-born gaining jobs was an irreversible demographic fact.”

As poor Jed Kolko has had to spend 2025 explaining, the reported levels Vance references aren’t correct. But Vance is definitely right about one thing: that pattern isn’t irreversible. Another possibility is that both groups lose at the same time.

As shown above, it’s a worse year for native-born workers with their unemployment up. The administration believes that employment is a zero-sum competition between groups. But the evidence points to a shared exposure, to both prosperity and, as we’re now experiencing, to rising joblessness amid weakening demand and policy confusion.

Four: The Breadwinner Economy

I think the last is less discussed but is one of the more important things, and that’s the gender theory of MAGA economics. The idea that bringing back manufacturing jobs with high wages for native-born male workers would make them more attractive suitors to women, which in turn would raise the marriage rate, and then fertility rate, is hanging in the background here. I think the gender and culture war aspect of this helps explain why the broader conservative movement is willing to risk tariffs.

However, that’s not happening. With goods weakening and health care holding the labor market together, Trump has made it a worse environment for male employment. Starting in May, right after Liberation Day, men have gained 4,000 out of a total of 119,000 new jobs. Overall in 2025, women have gained 67% of jobs, compared to 51% in 2023 to 2024. The labor market is moving in the opposite direction of the one this theory requires.

I wish I could tell you that the reason people in their twenties can’t find jobs and the reason many of us are likely to be poorer over the next few years was more sophisticated than “we’re going to turn all the girlbosses into tradwives once they see all the manly men at the USA iPhone-screwing factory.” But I don’t think it is.

The bleak irony is that even after sacrificing real prosperity to chase this 4chan-level political economy, they still won’t achieve their goal. The jobs aren’t coming back, the wages aren’t rising, and family formation won’t be rescued by trying to rewind the labor market to a world that never existed in the first place.

Much of the coverage has focused on how divided the Fed appears. Joe Weisenthal had a column noting it is as if “that implicitly the Fed has already abandoned its target” and Matthew Klein wonders if the Fed has submitted to political demands.

A Target Range

Here’s a theory of what the Fed is thinking. I’m not entirely sure whether this reflects what Fed officials actually believe, a useful way to model how they behave, or simply what I think they should be doing. It’s just a take.

My theory is that the Federal Reserve acts as if they have a target range of between 1.6 and 2.4 for inflation, and their dovishness and hawkishness alternate as they get to either end of the range. Why is that? Here’s a chart I watch closely on PCE inflation:

And here it is marked up to follow a few points:

This is total PCE inflation, what the Fed targets at 2 percent, along with four contributors that account for the total. Start at the upper-right green-colored box. Across 2018 to 2019, inflation was 1.7% a year. When inflation was much lower earlier in the 2010s, they did a lot to try and boost economic activity, including Operation Twist and Quantitative Easing (QE). But when inflation was 1.7% they did not stress much about it being too low. They instead defaulted to having a dovish lens on events as they happen, such as letting unemployment drift into a rate below 4 percent alongside strong real wage growth, while giving speeches on how unknowable r* is.

Let’s jump forward to now, and discuss the red-colored box. Over the six months prior to September, inflation was 2.7%. Yet 0.44% of that is in core goods, which was zero in 2024 and zero in 2018-2019, and is largely attributable to the one-time impact of Trump’s tariffs. That puts underlying inflation right at 2.26%. 2.26% is not 2.0%. But 2.26% is not something you should cause a recession to end.

However, being at the top-end of the range does put a hawkish lens on new events as they happen, mirroring the dovish lens on being at the low-end in 2018-2019. So when the tariffs, for instance, show up, the Fed is rightfully concerned about them breaking expectations, or becoming more persistent, or also causing a shift in services to bring their respective price levels into balance. It’s too close to the upper-bound of the range to assume it’ll act like a tax and nothing else, because if something breaks here you are well outside the range. You’ll keep more of the headline inflation rate in your Taylor Rule calculations.

So what about this divisive vote? One implication of operating near the top end of an implicit range is that it creates sharp discontinuities in how different policymakers read the same data. A slightly different read puts you either inside or outside the range. Take the purple-colored box in the chart above, where over the past three months inflation is still in the ~2.8% range, but now it’s all in services, not goods. You can see this in the chart below. Normally you wouldn’t over-index on any three-month period. But we’re at the top-end of the range, making us more nervous about these short-term movements, so that’s why many want to hold.

Yet, we’ve also seen the labor market weaken in the last several months, with, as you can see below, negative job growth in two out of the last four months. Other labor market measures are going south. It’s unclear whether this is because demand is falling or potential is, because of deportations and tariffs.

Meanwhile there’s a third way to read the data. Some still work for the White House but are on leave, and others are aiming to get Powell’s job next year, pushing them in the more dovish direction in reading the data.

Ranges are Good

The macroeconomist Greg Mankiw had a good argument in 2024 that “A target of 2 for inflation is better than a target of 2.0.” From the piece:

I feel strongly that a target of 2 percent is superior to a target of 2.0 percent. The difference between these targets, of course, is the number of significant digits. If you recall some science class you had in high school, you likely learned that the number of digits a person reports should reflect the precision of his or her estimate. Central bankers often forget that lesson. They sometimes speak as if they are targeting an inflation rate of 2.000 percent.

It would be better if central bankers admitted to the public how imprecise their ability to control inflation is. They should not be concerned if the inflation rate falls to 1.6. That comfortably rounds up to 2. And they should be ready to declare victory in fighting inflation when the inflation rate gets back to 2.5. As the adage goes, that is good enough for government work.

Maybe the Fed should even ditch a specific numerical target for inflation and instead offer a range. It could say, for example, that it wants to keep the inflation rate between 1 and 3. Doing so would admit that central bankers are not quite as godlike as they sometimes feign.

I like ranges. Back at Team Macro at the Roosevelt Institute, the economist Justin Bloesch put out an excellent paper with the case for an inflation range of 2 to 3.5 percent. Explicit inflation ranges are used by Canada (“The Bank of Canada aims to keep inflation at the 2 per cent midpoint of an inflation-control target range of 1 to 3 per cent”) and Australia (“Australia’s inflation target is to keep annual consumer price inflation between 2 and 3 per cent”), early adopters of inflation targeting that are widely viewed as having strong macroeconomic track records. Ranges also help resolve some of the persistent “catch-up” questions that complicate strict point targets. It’s good that the Fed might be using it, at least in my imagination.

It’s the holiday season, let’s do a fun post that won’t be for everyone. But if you make it to the end, you can see how proper economic microfoundations can clarify the debate over pronatalism on the left. Last week, we dug into the fertility data and started to build a take that worked in practice. But does it work in theory?

The take I’m exploring is that we should be thinking of the slowdown in fertility in terms of marginal penalties that would be best served by focusing on the concrete, near-term barriers facing people in their late 20s and early 30s. Others have written along these lines. Rachel Cohen Booth writes about how the “economy isn’t built for the biological clock.” Suzanne Kahn notes, in a recent book review of After the Spike by Spears and Geruso, that a goal should be “lowering the opportunity cost of children” especially “by giving parents more time.”

But does this take survive contact with rational expectations? We stress test all potential takes around here, including hitting them with the question of microfoundations. Our take must remain valid even when we assume people are forward-looking actors who will actively change their behavior to anticipate and react to changing constraints and policies. I don’t want to be doing my new take on a podcast or a panel, and suddenly get blindsided by the Lucas Critique.

This is extra relevant as we discussed issues with total fertility rate (TFR), which you can find more about from Our World in Data and The Conversation, a measure that assumes current timing stays fixed and people don’t shift births around.1 Put differently, TFR doesn’t survive rational expectations, it’s a statistic that can’t keep up with how real people optimize against changing constraints. But how well does a theory of penalties survive this test?

Model Setup

We also have this graphic from our dive last week:

Between the 1980 and 1986 cohorts, we see a notable slowdown in when people start families, even though they eventually arrive at the exact same destination. They both start with ~0 kids at age 15 and end up with 1.83 kids by age 38. But the 1986 cohort takes a very different path, delaying those births.

Whether or not it has forgotten how to answer basic questions about the business cycle, current macroeconomics excels at modeling how an ethereal representative agent allocates decisions across an infinite future under perfect information. Though young people would probably say, if not phrased exactly this way, “I don’t understand the parameters of how to date on apps and raise a family in this moment,” that isn’t going to stop us here.

There’s a literature around how future-looking agents decide to have children, built around models by Becker and Barro (not that one). Usually, those models are trying to solve complex problems like intergenerational wealth, cross-country comparisons, or human capital accumulation. I want to keep our model of a penalty very simple, a toy model we could build on later. What does our hypothetical “penalty” do?

We imagine a representative agent for the yearly cohort, who can choose a fraction of a child each period. Today’s choice to have a kid, bₜ, adds to tomorrow’s number of kids, Bₜ₊₁. Now you love your children equally, of course. But your love, U(Bₜ), is twice-differentiable, with the first derivative strictly positive and <looking around to make sure the kids are out of earshot> the second derivative strictly negative. And, last, there’s a penalty, pₜ, to having a kid at time t, that can change over time. The penalty is quadratic, which means the more you have kids in a period, the more the penalty bites. The agent starts with no kids, knows all the things, and also knows at 38 they’ll have exactly 1.83 kids.

All pretty straightforward? Let’s write that out.

Baby Bellman

Now we can solve this baby equation with a baby Lagrangian or a baby Bellman equation. I want to use the baby Bellman equation.

I actually think this recursive structure is a great way to think about fertility. Don’t get lost in the equation. V(B) is the value of having kids to our agent. How does it model when to have kids? A decision today, that’s the left part, and a decision tomorrow, that’s the right part. But what’s the decision tomorrow? It’s also V(…)! It’s the same equation, the same decision, but just repeated tomorrow.

Many often think “when’s the right time to have kids?” In the Bellman world, there’s just two time periods: today, and tomorrow, and tomorrow is just today all over again. But a little more exhausted, as you have to discount. You can literally have infinite time but you’ll still just have two times: today, and tomorrow, which is just today’s decision repeated again. It’s today all the way down. (There’s no good time, just have the kids.)

Baby Euler Equation

Yet people are shifting fertility around, and we have a way of determining how to assess that trade-off: the Euler equation. We take the baby Bellman equation, and take the baby first-order conditions. From there we apply the baby envelope theorem, which gives us the baby Euler equation:

Let’s focus on the Euler: The left side is b at time t, which is the decision to have a kid now. The thing that makes this unique is that b at time t+1 is on the right side of the equation; it is reflecting the decision today explicitly against the decision tomorrow. The logic is that you balance your decision-making so you are indifferent between moving just a fraction of having a kid between today and tomorrow.

Let’s talk about the term labeled “timing penalty,” which is the penalty tomorrow divided by the penalty today, that is added to the decision to have kids now. I’m going to assume you hate math and are mad you got here and take this slow, because it’s important. When the penalty today grows larger than the penalty tomorrow, the denominator gets bigger than the numerator; that number goes down. And you are adding it to b, the decision to have a kid, so it decreases having a kid now. If the penalty tomorrow gets larger than the penalty today, the opposite happens, the numerator grows faster and the number of kids today increases.

Or, in terms of life, when young women face higher penalties today, like unstable jobs, thin safety nets, career costs, expensive childcare, relative to what they expect tomorrow, they delay births. And when anticipating higher future penalties, like infertility or balancing aging parents, they shift births earlier.

Note if you don’t hate math you can just take the derivative of having a kid today with respect to the two penalties (noting p is positive and b is non-negative) and get the same result, a higher penalty today lowers today’s fertility and a higher penalty tomorrow increases it today:

With very simple assumptions, a rational agent will spread the timing to have children seeking out when the penalty is lowest. One can even try to estimate the penalty, which is an exercise for the footnotes.2 I believe my new take survives rational expectations, so I will continue to build on it.

Pronatalism Under Microfoundations

Currently there’s a lot of interest in “pronatalism,” though it’s often difficult to pin down the politics of it, especially on the left. But this is where microfoundations can clear the confusion, by examining the penalty in the baby Euler equation.

We can identify two different approaches to this equation. There are those who want to alleviate the penalty term, to reduce pₜ in those critical late-20s and early-30s years when the penalties are currently highest. This approach takes the equation seriously and asks what makes the denominator spike for young adults today, then works to address those constraints directly. Childcare, paid family leave, housing policy that builds, child allowances, healthcare, and workplace flexibility, all to move the marginal penalty. This path respects the optimization problem, acknowledging that people are rationally responding to real constraints, and seeks to lower those constraints so the penalty is smaller during these years.

Then there are those who want to suppress the ability to time, to remove b as a choice variable entirely. (Choice as in both the reproductive and Bellman sense.) This is happening with anti-choice campaigns to eliminate access to abortion and contraception. It’s also happening with the Trump administration broadly, most recently moving to reclassify nursing and other female-dominated fields as non-professional degrees, raising financial barriers to access. This is based on the conservative argument that preventing women from accessing higher education can increase fertility, one that, as Darby Saxbe shows, is wrong on its own arguments. You can’t make the timing choices that works for you if you have no control over your life or your economic future.

Both approaches seek to change fertility patterns, but through fundamentally different mechanisms. One lowers the barriers people face in having the families they want when they want them. The other removes the ability to choose, to time at all. One side trusts women to make decisions for themselves when constraints are eased. The other side sees women’s decision-making itself as the problem to be suppressed. If there’s a question of pronatalism in front of you, ask what side it’s on. And always ask yourself, what side are you on?

2

I’m going to pause here and say I am not confident that this next section is correct, and it may just get deleted. Dynamic macro numerical methods is not something I’ve allocated skill points toward. Outside the academy it seems to mostly allow you to work at the Fed, and there’s a small chance the Supreme Court is going to say that the new job of the central bank is to just do whatever random 24-year-olds in the West Wing tell them to do, just as the Founders intended. If any serious macro people want to read the code and give feedback I’d love it!

But we came this far. We have the recursive model, and we have two historical fertility paths that end up in the same place but with different timing. We don’t have anything random to keep it simple. Let’s take log utility and set β to 0.92 (it’s every two years for a period) and back out the implied penalty for each period. This isn’t really estimation, it’s just walking the implied penalty backwards.

I think this is correct, that this is the penalty sequence {pₜ} that creates the the birth pattern for birth year 1980 and 1986 for the model above. If so, notice that the penalty from 28 to 32 is higher for 1986. But note that this higher penalty is not that much higher. In fact, it’s as if it just doesn’t get easier at 30 than it was at 26. But many of those likely increased penalties (building more housing, above all) are addressable, if we want it.

With the government shutdown, there’s been no new economic data, and it’s been very boring without any new numbers to investigate. So I decided to get to the bottom of this fertility crisis everyone is always arguing about. I’ll say up front that I didn’t have a strong take on the topic. I’m not experienced in these debates, but I am careful with data and can share what I find.1

I decided to dig into recent microdata from IPUMS: the 2024 CPS Fertility Supplement. The survey is fielded every two years with Census, with the most recent round in 2024. Unlike some other statistics, which only show births in a given year, the supplement captures cumulative births across the life cycle, making it one of our better windows into long-run patterns in fertility. I find I can replicate the aggregate Census numbers from the microdata, and then we’re off.

One thing stands out: the eldest millennials have the same completed fertility rates as the youngest boomers. There’s been no drop within the 21st century of the number of kids a woman has once she reaches 44 years old. That stability might surprise you given the popular narratives about collapsing family formation, and it’s worth diving into the data.

Figure 1 shows the average number of births by age, taken as a cross-section across two years. In the graphs here and elsewhere, I use three-year averages around each age, though the results look the same either way. The underlying variable is “Number of live births ever had” and the question is “Altogether how many children (have/has) (name/you) ever given birth to?” For women at each age, we sum up all the births, and divide that number by the total number of women to get the average.

Completed Fertility

In the year 2000, the green line above in Figure 1, a 43-year-old woman, who is among the younger baby boomers, averaged 1.92 births. In the year 2024, the navy line, a 43-year-old woman, who is among the eldest millennials, averaged 1.92 births. (For age context, a voice of a generation, the fictional core millennial Hannah Horvath from HBO’s Girls, turns 36 this year.)

You have probably heard of total fertility in the news; more on that in a bit. But the term here is completed fertility, the number of children a woman has by the end of her reproductive years, usually measured as an average for the early-to-mid 40s. This stability is well known; other analyses confirm that completed fertility has been essentially flat since 2000 (Census 2024, Pew 2018, BGSU 2023). But it’s worth emphasizing that this holds all the way through the 2024 data.

What has changed is the timing of births. Remember this is a cross-section based on the year we are observing. In 2000, a 30-year-old woman had, on average, 1.34 children. In 2024, that number had dropped to 0.98. The two cross-years ended up in the same place, but they follow very different paths to get there.

Before you ask, is this all immigration? No. We can track nativity status in this data. For women 41 to 44 who have at least one native-born parent, the weighted average number of children was 1.88 in 2000 and 1.87 in 2024.

Now completed fertility used to be higher than it is today. So when did the drop happen? We can look at the average number of births for women ages 41 to 44 in each CPS Fertility Supplement, going back every two years to 1976, as shown in Figure 2:

The big drop happened some time ago. There has been some variation since 2000. It hits a low of 1.86 in 2006, and a high of 2.07 in 2016. (What’s going on with those born around 1974?) But the headline is how stable completed fertility has been for more than two decades.

The Biggest Generation Happening

One reason you’ve probably heard about a fertility crisis is that the total fertility rate (TFR), which is the average number of children a woman is expected to have over her lifetime based on today’s birth rates, has declined. But TFR is sensitive to and misled by timing shifts like the one above. As Matt Bruenig once wrote, it’s “kind of a nonsense statistic.” If births shift later, even if the total number doesn’t ultimately change, TFR mechanically falls for a period. A birth disappears for a specific year, and TFR can’t observe the later birth that offsets it.

Demographers separate fertility into quantum (the eventual number of children, like completed fertility) and tempo (the timing of childbearing, like TFR). Completed fertility is a quantum measure, and that can be misleading in real time as well because we only know the final tally once a cohort finishes childbearing. If there were a genuine collapse in fertility underway, the completed fertility metric would reveal it only after the fact.

This is relevant because the largest single-year cohorts in the United States right now are those ages 34 and 35. These are the 1990 and 1991 birth cohorts that are the later millennials. To get a sense of this, Figure 3 is a chart of the age distribution in the United States for 2024 from Census data:

Jeanna Smialek, who is in that cohort, had a great piece last year on this generation as an economic-distorting force moving through the life-cycle in the New York Times (titled “It’s Me, Hi, I’m the Problem. I’m 33”). What happens to them is probably going to be an important part of demographics going forward. We can follow 34-year-olds in 2024 to get a sense in Figure 4, and compare them to slightly older cohorts:

Figure 4 plots completed fertility on the y-axis for people born in 1980, 1986, and 1990, from the point of view of the year 2024. So 34-year-olds in 2024 (born 1990) have a completed fertility of 1.46. This is comparable to 38-year-olds now, who, when they were 34, had a completed fertility of 1.51. Note however, that they were lower than the 44-year-old cohort, who were at 1.74 at that age. But the 38-year-olds caught up to the trend of the 44-year-olds. The 34-year-olds seem to be catching up as well?

Take a second to understand Figure 4, because we’re going to extend it to every year born between 1980 and 1991. Figure 5 provides that spaghetti-style graphic:

This might be the graph of the post. You can see a shift happening here, as if the entire profile were shifted outward to the right. Yet it appears to still be forming a consistent line and converging. If true, it’s not resulting in fewer kids, but just having kids later. Using some estimates, it looks like, for a given average number of children, the age has shifted to the right by between 2-3 years.

By Generations

So there was a shift. How can we visualize how this happened? Let’s go big, and look at the change in fertility at each age within the 21st century. In Figure 6, I’m going to color-code the lines by generation. Some quick age bands: Baby Boomers are born through 1964; millennials begin in 1981. Generation Z begins in 1997, making the oldest Zoomers 27 in 2024.

In Figure 6, I’m rescaling each y-axis to highlight the relevant shape of each series, while keeping the x-axis consistent across all plots: the years 2000 through 2024, with data from every even year in the CPS Fertility Supplement. This lets us watch how the tempo slowdown is evolving across time. But it does mean paying attention to the scale of each y-axis, which varies.

How should you read this? Take the box for age 30. For a 30-year-old, from 2000 through roughly 2010, the pattern is stable: they consistently had between 1.3 and 1.4 children by that age. Then, right after 2010, the floor drops out. By 2020, the completed births at age 30 land around 1.0. That’s a big shift in a short window.

There’s a lot here. Where to begin?

• There’s a break between 2008 and 2014 that looks especially clear from ages 28 to 33. The Great Recession casts a long shadow. A recovery that may have been slower than the Great Depression isn’t a great way to keep demographic trends stable. Real median household income falls over 5% during this time, so it’s probably not higher wages delaying children. But smartphones also became ubiquitous during this period. This mix is one reason I’m always hesitant on “the slowdown is across peer countries, what can you do?” arguments. Whether it’s that recession or tech, there are things that link countries that are distinct events and policy choices.

• It appears that there’s been some flattening of the drop in 2022 to 2024. See the ages 26 to 32 for some sense of this, where it’s pretty flat 2020 through 2024. I try not to over-index on any single year, but it’s worth people investigating this more fully. Why is this? Perhaps hybrid work has allowed for more flexibility in building a family (see Bailey, Currie and Schwandt (2023)). Is this the “vibe shift”? Maybe income support and wage compression during the recovery played a role. Did the inflation wave make having a perpetual youth of low-cost services seem less attainable than starting a family? Having the cause of something that may or may not have happened already be overdetermined is part of the fun of this work.

• The decline in teenage pregnancy is a major story here and it rarely gets discussed. We have, as measured here, the average number of children for 17-year-olds around 0.13 in 2000. That’s around one in eight, and that goes to 0.024 by 2024. That’s a near-total disappearance of teen childbearing over a single generation. Any model of tempo or quantum that ignores this shift is missing a central piece of the puzzle.

You probably have your own questions and interest in the numbers. I wanted to see how well I can incorporate vibecoded apps into blog posts. So I decided to vibecode a visualization app to display the data multiple ways using Google AI Studio with Gemini 3, which was surprisingly easy (about 20 minutes start to end). However, I can’t display it here, but you can click here or on the graphic below to see it yourself at my website. Click on your age and year if you so dare!

Tools and Measures

For something that is so often framed as a priority among elites, it’s remarkable how little high-quality data we have on the full lifecycle of births and family situations. Even straightforward questions like “controlling for education, does income or student debt correlate with fewer children?” are hard to answer.

One reason we end up using total fertility is that it is a very well-recorded event when children are born. You aren’t leaving the hospital after having kids until the Social Security documents are filled out. Births are logged immediately, and the age of the mother is a clean, consistent datapoint. Interviewing women later in their lives about how many children they’ve had (as Census does here) is a completely different, and more difficult, exercise.

One genuinely bipartisan effort would be to expand and strengthen the CPS Fertility Supplement. We could make it more regular, more in-depth, and add questions the experts studying this think would be helpful. But right now, sadly, the Bureau of Labor Statistics is under political assault, exactly when we need this data to be better, not worse.

Conclusion

For anyone who wants the United States to keep growing, 1.92 children is below the replacement level of 2 or 2.1. The shift to later parenthood carries real tradeoffs, which will reshape family life and intergenerational ties for both individuals and society. And this analysis is specific to the United States.

But spending time in the data leaves me less alarmist than the prevailing rhetoric. The shift in births is recent and likely shaped by shocks like the Great Recession and technology. The shocks are common across countries, and that means there is real policy space to act. We could begin by not wrecking the economy in service of the crudest mercantilist fantasies. But my new take is, instead of framing declining birth rates as a civilizational crisis, we would be better served by focusing on the concrete, near-term barriers facing people in their late 20s and early 30s.