There has been an ongoing debate about whether Millennials and people in Generation Z are better off or worse off economically than are Baby Boomers. Edward Wolff of New York University recently published a National Bureau of Economic Research (NBER) working paper that focuses on one aspect of this debate—how the wealth of households headed by someone 75 years and older changed relative to the wealth of households headed by someone 35 years and younger during the period from 1983 to 2022.
Wolff uses data from the Federal Reserve’s Survey of Consumer Finances to measure the wealth, or net worth, of people in these age groups—the market value of their financial assets minus the market value of their financial liabilities. He includes in his measure of assets the market value of people’s real estate holdings—including their homes—stocks and bonds, bank deposits, contributions to defined contribution pension funds, unincorporated businesses, and trust funds. He includes in his measure of liabilities people’s mortgage debt, consumer debt—including credit card balances—and other debt, such as educational loans. Because Wolff wants to focus on that part of wealth that is available to be spent on consumption, he refers to it as financial resources, and he excludes from his wealth measure the present value of future Social Security payments and the present value of future defined contribution pension benefits.
The following figure from Wolff’s paper shows that, using his definition, both median and mean wealth have increased substantially from 1987 to 2o22. Note that both measures of average wealth declined during the Great Recession and Global Financial Crisis of 2007–2009. Median wealth declined by nearly 44 percent between 2007 and 2010. That median wealth grew much faster than mean wealth over the whole period indicates that wealth inequality.
Although the average wealth of all age groups increased over this period, the relative wealth of households 75 years and older rose and the relative wealth of households 35 years and younger fell. The following figure from the NBER Digest illustrates this shift. The 75 and over age group increased its mean net worth from 5 percent greater than the mean net worth of the average household in 1983 to 55 percent of the mean net worth of the average household in 2022. In contrast, the 35 and under age group saw its mean new worth relative to the average household fall from 21 percent in 1983 to 16 percent in 2022. Note, though, that there is significant volatility over time in the relative wealth shares of the two age groups.
What explains the relative increase in wealth among households 75 and over and the relative decrease in wealth among households 35 and under? Wolff identifies three key factors:
“[T]he homeownership rate, total stocks directly and indirectly owned, and home mortgage debt. The homeownership rate is the same in the two years for the youngest group but falls relative to the overall rate, whereas it shoots up for the oldest group both in actual level and relative to the overall average. The value of stock holdings rises for both age groups but vastly more for the oldest households compared to the youngest ones and accounts for a substantial portion of the elderly’s relative wealth gains. Mortgage debt rises in dollar terms for both groups but considerably more in relative terms for the youngest group.”
Perhaps surprisingly, Wolff finds that “despite dire press reports, educational loans fail to appear as a significant factor” in explaining the decline in the relative wealth of younger households.
This pair of ruby slippers worn by Judy Garland playing the role of Dorothy in the Wizard of Oz sold at auction last December for $32.5 million.
One of the most important ideas in economics is that an increase in the price of a good attracts new entrants into that market. In the short run, before there is time for new firms to enter an industry, an increase in price leads to a movement up the supply curve for the good (an increase in the quantity supplied). In the long run, a higher price leads to the entry of new firms (an increase in supply), which forces the price of the good back down to the level at which firms in the industry just break even. The following figure from Microeconomics, Chapter 12 illustrates the effects of entry in the case of the market for cage-free eggs. An increase in demand occurs when the price is $2.00 per dozen. The increased demand forces a price increase to $3.00, but, over time, entry of new firms forces the price back down to $2.00.
But what if the supply of a good is fixed, as in the case of collectibles such as the props used in a movie? In that situation, we would expect that entry is impossible. Demand for props used in movies—particularly classic movies—has soared in recent years leading to sharply increased prices. A pair of ruby slippers (as they are usually called even though they are actually shoes rather than slippers) used in the filming of The Wizard of Ozsold at auction in December for $32.5 million. Shown below is the “Rosebud” sled used in the filming of Citizen Kane—thought by some critics to be the greatest film ever made. It sold for $14.75 million in June of this year.
The model of an X-Wing Starfighter shown below was used in filiming the first Star Wars film and sold at auction for $3.135 million.
Unlike with cage-free eggs, these high prices won’t attract new entry because the value of these goods comes from their having been used in the making of classic films. (Of course, the high prices may lead some people who own similar props from these movies to offer them for sale—a movement up the supply curve, rather than a shift in the curve.)
According to a recent article in the Los Angeles Times (a subscription may be required), some unscrupulous people have attempted to enter the market for collectible movie props by creating counterfeits. According to the article, 3D printers have made it easier for scammers to create duplicates of movie props. For instance, according to the article, earlier this year an auction house advertised that it would be offering for sale the only surviving Han Solo DL-44 blaster used in the filming of the first Star Wars movie. (Shown in the image below, which was generated by ChatGPT.) The auction house estimated that the blaster could sell for more than $3 million.
Collectors carefully analyzed the photos shown on the auction site and discovered several discrepancies between the prop being offered for sale and the actual prop used in the film. The auction house concluded that the prop was a counterfeit and withdrew it from sale.
The article quoted Jason Henry, who is a television producer and who makes online videos on movie collectibles, as saying:
“As the prices go up, the supply is going up as well, which doesn’t seem like that’s how it should be. And it’s not because there’s actually more of the original pieces, there’s actually just more fakes or questionable pieces as we like to call it, that are flying into the market.”
It’s unfortunate, but unsurprising to an economist, that in this case the very strong incentive to enter a profitable industry has led some people to break the law by creating counterfeit movie props.
Ordinarily, on the first Friday of a month the Bureau of Labor Statistics (BLS) releases its “Employment Situation” report (often called the “jobs report”) containing data on the labor market for the previous month. There was no jobs report today (October 3) because of the federal government shutdown. (We discuss the shutdown in this blog post.)
The jobs report has two estimates of the change in employment during the month: one estimate from the establishment survey, often referred to as the payroll survey, and one from the household survey. As we discuss in Macroeconomics, Chapter 9, Section 9.1 (Economics, Chapter 19, Section 19.1), many economists and Federal Reserve policymakers believe that employment data from the establishment survey provide a more accurate indicator of the state of the labor market than do the household survey’s employment and unemployment data.
Economists surveyed had forecast that today’s payroll survey would have shown a net increase of 51,000 jobs in September. When the shutdown ends, the BLS will publish its jobs report for September. Until that happens, employment data collected by the private payroll processing firm Automatic Data Processing (ADP) provides an alternative measure of the state of the labor market. ADP data covers only about 20 percent of total private nonfarm employment, but ADP attempts to make its data more consistent with BLS data by weighting its data to reflect the industry weights used in the BLS data.
How closely does ADP employment data track BLS payroll data? The following figure shows the ADP employment series (blue line) and the BLS payroll employment data (red line) with the values for both series set equal to 100 in January 2010. The two series track well with the exception of April and May 2020 during the worst of the pandemic. The BLS series shows a much larger decline in employment during those months than does the ADP series.
The next figure shows the 12-month percentage changes in the two series. Again, the series track fairly well except for the worst months of the pandemic and—strikingly—the month of April 2021 during the economic recovery. In that month, the ADP series increases by only 0.6 percent, while the BLS series soars by 13.1 percent.
Finally, economists, policymakers, and investors usually focus on the change in payroll employment from the previous month—that is, the net change in jobs—shown in the BLS jobs report. The following figure shows the net change in jobs in the two series, starting in January 2021 to avoid some of the largest fluctuations during the pandemic.
Again, the two series track fairly well, although the BLS data is more volatile. The ADP data show a net decline of 32,000 jobs in September. As noted earlier, economists surveyed were expecting a net increase of 51,000 jobs. During the months from May through August, BLS data show an average monthly net increase in jobs of only 39,250. So, whether the BLS number will turn out to be closer to the ADP number or to the number economists had forecast, the message would be the same: Since May, employment has grown only slowly. And, of course, as we’ve seen this year, whatever the BLS’s initial employment estimate for September turns out to be, it’s likely to be subject to significant revision in coming months. (We discuss why BLS revisions to its initial employment estimates can be substantial in this post.)
Modern industrial capitalism’s bounty has been breathtaking globally and especially in the U.S. It’s tempting, then, to look at critics in the crowd in Monty Python’s “Life of Brian” as they ask, “What have the Romans ever do for us?,” only to be confronted with a large list of contributions. But, in fact, over time, American capitalism has been saved by adapting to big economic changes.
We’re at another turning point, and the pattern of American capitalism’s keeping its innovative and disruptive core by responding, if sometimes slowly, to structural shocks will play out as follows.
The magnitude, scope and speed of technological change surrounding generative artificial intelligence will bring forth a new social insurance aimed at long-term, not just cyclical, impacts of disruption. For individuals, it will include support for work, community colleges and training, and wage insurance for older workers. For places, it will include block grants to communities and areas with high structural unemployment to stimulate new business and job opportunities. Such efforts are a needed departure from a focus on cyclical protection from short-term unemployment toward a longer-term bridge of reconnecting to a changing economy.
These ideas, like America’s historical big responses in land-grant colleges and the GI Bill, combine federal funding support with local approaches (allowing variation in responses to local business and employment opportunities), another hallmark of past U.S. economic policy.
With a stronger economic safety net, the current push toward higher tariffs and protectionism will gradually fade. Protectionism is a wall against change, but it is one that insulates us from progress, too.
A growing budget deficit and strains on public finances will lead to a reliance on consumption taxes to replace the current income tax system; continuing to raise taxes on saving and investment will arrest growth prospects. For instance, a tax on business cash flow, which places a levy on a firm’s revenue minus all expenses including investment, would replace taxes on business income. Domestic production would be enhanced by adding a border adjustment to business taxes—exports would be exempt from taxation, but companies can’t claim a deduction for the cost of imports.
That reform allows a shift from helter-skelter tariffs to tax reform that boosts investment and offers U.S. and foreign firms alike an incentive to invest in the U.S.
These ideas to retain opportunity amid creative destruction will also refresh American capitalism as the nation celebrates its 250th anniversary. They also celebrate the classical liberal ideas of Adam Smith, whose treatise “The Wealth of Nations” appeared the same year. This refresh marries competition’s role in “The Wealth of Nations” and American capitalism with the ability to compete, again a feature of turning points in capitalism in the U.S.
Decades down the road, this “Project 2026” will have preserved the bounty and mass prosperity of American capitalism.
These observations first appeared in the Wall Street Journal, along with predictions from six other economists and economic historians.
This morning (September 5), the Bureau of Labor Statistics (BLS) released its “Employment Situation” report (often called the “jobs report”) for August. The data in the report show that the labor market was weaker than expected in August.
The jobs report has two estimates of the change in employment during the month: one estimate from the establishment survey, often referred to as the payroll survey, and one from the household survey. As we discuss in Macroeconomics, Chapter 9, Section 9.1 (Economics, Chapter 19, Section 19.1), many economists and Federal Reserve policymakers believe that employment data from the establishment survey provide a more accurate indicator of the state of the labor market than do the household survey’s employment data and unemployment data. (The groups included in the employment estimates from the two surveys are somewhat different, as we discuss in this post.)
According to the establishment survey, there was a net increase of only 22,000 nonfarm jobs during August. This increase was well below the increase of 110,000 that economists surveyed by FactSet had forecast. Economists surveyed by the Wall Street Journal had forecast a smaller increase of 75,000 jobs. In addition, the BLS revised downward its previous estimates of employment in June and July by a combined 21,000 jobs. The estimate for June was revised from a net gain of 14,000 to a net loss of 13,000. This was the first month with a net job loss since December 2020. (The BLS notes that: “Monthly revisions result from additional reports received from businesses and government agencies since the last published estimates and from the recalculation of seasonal factors.”)
The following figure from the jobs report shows the net change in nonfarm payroll employment for each month in the last two years. The figure makes clear the striking deceleration in job growth since April. The Trump administration announced sharp increases in U.S. tariffs on April 2. Media reports indicate that some firms have slowed hiring due to the effects of the tariffs or in anticipation of those effects.
The unemployment rate increased from 4.2 percent in July to 4.3 percent in August, the highest rate since October 2021. The unemployment rate is above the 4.2 percent rate economists surveyed by FactSet had forecast. As the following figure shows, the unemployment rate had been remarkably stable over the past year, staying between 4.0 percent and 4.2 percent in each month May 2024 to July 2025 before breaking out of that range in August. In June, the members of the Federal Open Market Committee (FOMC) forecast that the unemployment rate during the fourth quarter of 2025 would average 4.5 percent. The unemployment rate would still have to rise significantly for that forecast to be accurate.
Each month, the Federal Reserve Bank of Atlanta estimates how many net new jobs are required to keep the unemployment rate stable. Given a slowing in the growth of the working-age population due to the aging of the U.S. population and a sharp decline in immigration, the Atlanta Fed currently estimates that the economy would have to create 97,591 net new jobs each month to keep the unemployment rate stable at 4.3 percent. If this estimate is accurate, continuing monthly net job increases of 22,000 would result in a a rising unemployment rate.
As the following figure shows, the monthly net change in jobs from the household survey moves much more erratically than does the net change in jobs from the establishment survey. As measured by the household survey, there was a net increase of 288,000 jobs in August, following a net decrease of 260,000 jobs in July. As an indication of the volatility in the employment changes in the household survey note the very large swings in net new jobs in January and February. In any particular month, the story told by the two surveys can be inconsistent. as was the case this month with employment increasing much more in the household survey than in the employment survey. (In this blog post, we discuss the differences between the employment estimates in the two surveys.)
The household survey has another important labor market indicator: the employment-population ratio forprime age workers—those aged 25 to 54. In August the ratio rose to 80.7 percent from 8.4 percent in July. The prime-age employment-population ratio is somewhat below the high of 80.9 percent in mid-2024, but is still above what the ratio was in any month during the period from January 2008 to February 2020. The increase in the prime-age employment-population ratio is a bright spot in this month’s jobs report.
It is still unclear how many federal workers have been laid off since the Trump Administration took office. The establishment survey shows a decline in federal government employment of 15,000 in August and a total decline of 97,000 since the beginning of February 2025. However, the BLS notes that: “Employees on paid leave or receiving ongoing severance pay are counted as employed in the establishment survey.” It’s possible that as more federal employees end their period of receiving severance pay, future jobs reports may report a larger decline in federal employment. To this point, the decline in federal employment has had a small effect on the overall labor market.
The establishment survey also includes data on average hourly earnings (AHE). As we noted in this post, many economists and policymakers believe the employment cost index (ECI) is a better measure of wage pressures in the economy than is the AHE. The AHE does have the important advantage of being available monthly, whereas the ECI is only available quarterly. The following figure shows the percentage change in the AHE from the same month in the previous year. The AHE increased 3.7 percent in August, down from an increase of 3.9 percent in July.
The following figure shows wage inflation calculated by compounding the current month’s rate over an entire year. (The figure above shows what is sometimes called 12-month wage inflation, whereas this figure shows 1-month wage inflation.) One-month wage inflation is much more volatile than 12-month wage inflation—note the very large swings in 1-month wage inflation in April and May 2020 during the business closures caused by the Covid pandemic. In August, the 1-month rate of wage inflation was 3.3 percent, down from 4.0 percent in July. This slowdown in wage growth may be another indication of a weakening labor market. But one month’s data from such a volatile series may not accurately reflect longer-run trends in wage inflation.
What effect might today’s jobs report have on the decisions of the Federal Reserve’s policymaking Federal Open Market Committee (FOMC) with respect to setting its target for the federal funds rate? One indication of expectations of future changes in the FOMC’s target for the federal funds rate comes from investors who buy and sell federal funds futures contracts. (We discuss the futures market for federal funds in this blog post.) As we’ve noted in earlier blog posts, since the weak July jobs report, investors have assigned a very high probability to the committee cutting its target by 0.25 percentage point (25 basis points) from its current range of 4.25 percent to 4.50 percent at its September 16–17 meeting. This morning, as the following figure shows, investors raised the probability they assign to a 50 basis point reduction at the September meeting from 0 percent to 14.2 percent. Investors are also now assigning a 78.4 percent probability to the committee cutting its target range by at least an additional 25 basis points at its October 28–29 meeting.
Image generated by ChatGPT 5 of a 1981 IBM personal computer.
The modern era of information technology began in the 1980s with the spread of personal computers. A key development was the introduction of the IBM personal computer in 1981. The Apple II, designed by Steve Jobs and Steve Wozniak and introduced in 1977, was the first widely used personal computer, but the IBM personal computer had several advantages over the Apple II. For decades, IBM had been the dominant firm in information technology worldwide. The IBM System/360, introduced in 1964, was by far the most successful mainframe computer in the world. Many large U.S. firms depended on IBM to meet their needs for processing payroll, general accounting services, managing inventories, and billing.
Because these firms were often reliant on IBM for installing, maintaining, and servicing their computers, they were reluctant to shift to performing key tasks with personal computers like the Apple II. This reluctance was reinforced by the fact that few managers were familiar with Apple or other early personal computer firms like Commodore or Tandy, which sold the TRS-80 through Radio Shack stores. In addition, many firms lacked the technical staffs to install, maintain, and repair personal computers. Initially, it was easier for firms to rely on IBM to perform these tasks, just as they had long been performing the same tasks for firms’ mainframe computers.
By 1983, the IBM PC had overtaken the Apple II as the best-selling personal computer in the United States. In addition, IBM had decided to rely on other firms to supply its computer chips (Intel) and operating system (Microsoft) rather than develop its own proprietary computer chips and operating system. This so-called open architecture made it possible for other firms, such as Dell and Gateway, to produce personal computers that were similar to IBM’s. The result was to give an incentive for firms to produce software that would run on both the IBM PC and the “clones” produced by other firms, rather than produce software for Apple personal computers. Key software such as the spreadsheet program Lotus 1-2-3 and word processing programs, such as WordPerfect, cemented the dominance of the IBM PC and the IBM clones over Apple, which was largely shut out of the market for business computers.
As personal computers began to be widely used in business, there was a general expectation among economists and policymakers that business productivity would increase. Productivity, measured as output per hour of work, had grown at a fairly rapid average annual rate of 2.8 percent between 1948 and 1972. As we discuss in Macroeconomics, Chapter 10 (Economics, Chapter 20 and Essentials of Economics, Chapter 14) rising productivity is the key to an economy achieving a rising standard of living. Unless output per hour worked increases over time, consumption per person will stagnate. An annual growth rate of 2.8 percent will lead to noticeable increases in the standard of living.
Economists and policymakers were concerned when productivity growth slowed beginning in 1973. From 1973 to 198o, productivity grew at an annual rate of only 1.3 percent—less than half the growth rate from 1948 to 1972. Despite the widespread adoption of personal computers by businesses, during the 1980s, the growth rate of productivity increased only to 1.5 percent. In 1987, Nobel laureate Robert Solow of MIT famously remarked: “You can see the computer age everywhere but in the productivity statistics.” Economists labeled Solow’s observation the “productivity paradox.” With hindsight, it’s now clear that it takes time for businesses to adapt to a new technology, such as personal computers. In addition, the development of the internet, increases in the computing power of personal computers, and the introduction of innovative software were necessary before a significant increase in productivity growth rates occurred in the mid-1990s.
Result when ChatGPT 5 is asked to create an image illustrating ChatGPT
The release of ChatGPT in November 2022 is likely to be seen in the future as at least as important an event in the evolution of information technology as the introduction of the IBM PC in August 1981. Just as with personal computers, many people have been predicting that generative AI programs will have a substantial effect on the labor market and on productivity.
In this recent blog post, we discussed the conflicting evidence as to whether generative AI has been eliminating jobs in some occupations, such as software coding. Has AI had an effect on productivity growth? The following figure shows the rate of productivity growth in each quarter since the fourth quarter of 2022. The figure shows an acceleration in productivity growth beginning in the fourth quarter of 2023. From the fourth quarter of 2023 through the fourth quarter of 2024, productivity grew at an annual rate of 3.1 percent—higher than during the period from 1948 to 1972. Some commentators attributed this surge in productivity to the effects of AI.
However, the increase in productivity growth wasn’t sustained, with the growth rate in the first half of 2025 being only 1.3 percent. That slowdown makes it more likely that the surge in productivity growth was attributable to the recovery from the 2020 Covid recession or was simply an example of the wide fluctuations that can occur in productivity growth. The following figure, showing the entire period since 1948, illustrates how volatile quarterly rates of productivity growth are.
How large an effect will AI ultimately have on the labor market? If many current jobs are replaced by AI is it likely that the unemployment rate will soar? That’s a prediction that has often been made in the media. For instance, Dario Amodei, the CEO of generative AI firm Anthropic, predicted during an interview on CNN that AI will wipe out half of all entry level jobs in the U.S. and cause the unemployment rate to rise to between 10% and 20%.
Although Amodei is likely correct that AI will wipe out many existing jobs, it’s unlikely that the result will be a large increase in the unemployment rate. As we discuss in Macroeconomics, Chapter 9 (Economics, Chapter 19 and Essentials of Economics, Chapter 13) the U.S. economy creates and destroys millions of jobs every year. Consider, for instance, the following table from the most recent “Job Openings and Labor Turnover” (JOLTS) report from the Bureau of Labor Statistics (BLS). In June 2025, 5.2 million people were hired and 5.1 million left (were “separated” from) their jobs as a result of quitting, being laid off, or being fired.
Most economists believe that one of the strengths of the U.S. economy is the flexibility of the U.S. labor market. With a few exceptions, “employment at will” holds in every state, which means that a business can lay off or fire a worker without having to provide a cause. Unionization rates are also lower in the United States than in many other countries. U.S. workers have less job security than in many other countries, but—crucially—U.S. firms are more willing to hire workers because they can more easily lay them off or fire them if they need to. (We discuss the greater flexibility of U.S. labor markets in Macroeconomics, Chapter 11 (Economics, Chapter 21).)
The flexibility of the U.S. labor market means that it has shrugged off many waves of technological change. AI will have a substantial effect on the economy and on the mix of jobs available. But will the effect be greater than that of electrification in the late nineteenth century or the effect of the automobile in the early twentieth century or the effect of the internet and personal computing in the 1980s and 1990s? The introduction of automobiles wiped out jobs in the horse-drawn vehicle industry, just as the internet has wiped out jobs in brick-and-mortar retailing. People unemployed by technology find other jobs; sometimes the jobs are better than the ones they had and sometimes the jobs are worse. But economic historians have shown that technological change has never caused a spike in the U.S. unemployment rate. It seems likely—but not certain!—that the same will be true of the effects of the AI revolution.
Which jobs will AI destroy and which new jobs will it create? Except in a rough sense, the truth is that it is very difficult to tell. Attempts to forecast technological change have a dismal history. To take one of many examples, in 1998, Paul Krugman, later to win the Nobel Prize, cast doubt on the importance of the internet: “By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.” Krugman, Amodei and other prognosticators of the effects of technological change simply lack the knowledge to make an informed prediction because the required knowledge is spread across millions of people.
That knowledge only becomes available over time. The actions of consumers and firms interacting in markets mobilize information that is initially known only partially to any one person. In 1945, Friedrich Hayek made this argument in “The Use of Knowledge in Society,” which is one of the most influential economics articles ever written. One of Hayek’s examples is an unexpected decrease in the supply of tin. How will this development affect the economy? We find out only by observing how people adapt to a rising price of tin: “The marvel is that … without an order being issued, without more than perhaps a handful of people knowing the cause, tens of thousands of people whose identity could not be ascertained by months of investigation are made [by the increase in the price of tin] to use the material or its products more sparingly.” People adjust to changing conditions in ways that we lack sufficient information to reliably forecast. (We discuss Hayek’s view of how the market system mobilizes the knowledge of workers, consumers, and firms in Microeconomics, Chapter 2.)
It’s up to millions of engineers, workers, and managers across the economy, often through trial and error, to discover how AI can best reduce the cost of producing goods and services or improve their quality. Competition among firms drives them to make the best use of AI. In the end, AI may result in more people or fewer people being employed in any particular occupation. At this point, there is no way to know.
“Artificial intelligence is profoundly limiting some young Americans’ employment prospects, new research shows.” That’s the opening sentence of a recent opinion column in the Wall Street Journal. The columnist was reacting to a new academic paper by economists Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen of Stanford University. (See also this Substack post by Chandar that summarizes the results of their paper.) The authors find that:
“[S]ince the widespread adoption of generative AI, early-career workers (ages 22-25) in the most AI-exposed occupations have experienced a 13 percent relative decline in employment … In contrast, employment for workers in less exposed fields and more experienced workers in the same occupations has remained stable or continued to grow. Furthermore, employment declines are concentrated in occupations where AI is more likely to automate, rather than augment, human labor.”
The authors conclude that “our results are consistent with the hypothesis that generative AI has begun to significantly affect entry-level employment.”
About a month ago, we wrote a blog post looking at whether unemployment among young college graduates has been abnormally high in recent months. The following figure from that post shows that over time, the unemployment rates for the youngest college graduates (the red line) is nearly always above the unemployment rate for the population as a whole (the green line), while the unemployment rate for college graduates 25 to 34 years old (the blue line) is nearly always below the unemployment rate for the population as a whole. In July of this year, the unemployment rate for the population as a whole was 4.2 percent, while the unemployment for college graduates 20 to 24 years old was 8.5 percent, and the unemployment rate for college graduates 25 to 34 years old was 3.8 percent.
As the following figure (also reproduced from that blog post) shows, the increase in unemployment among young college graduates has been concentrated among males. Does higher male unemployment indicate that AI is eliminating jobs, such as software coding, that are disproportionately male? Data journalist John Burn-Murdoch argues against this conclusion, noting that data shows that “early-career coding employment is now tracking ahead of the [U.S.] economy.”
Another recent paper written by Sarah Eckhardt and Nathan Goldschlag of the Economic Innovation Group is also skeptical of the view that firms adopting generative AI programs is reducing employment in certain types of jobs. They use a measure developed by Edward Felton on Princeton University, and Manav Raj and Robert Seamans of New York University of how exposed particular jobs are to AI (AI Occupational Exposure (AIOE)). The following table from Eckhardt and Goldschlag’s paper shows the five most AI exposed jobs and the five least AI exposed jobs.
They divide all occupations into quintiles based on the exposure of the occupations to AI. Their key results are given in the following table, which shows that the occupations that are most exposed to the effects of AI—quintiles 4 and 5—have lower unemployment rates and higher wages than do the occupations that are least exposed to AI.
The Brynjolfsson, Chandar, and Chen paper mentioned at the beginning of this post uses a larger data set of workers by occupation from ADP, a private firm that processes payroll data for about 25 percent of U.S. workers. Figure 1 from their paper, reproduced here, shows that employment of workers in two occupations—software developers and customer service—representative of those occupations most exposted to AI declined sharply after generative AI programs became widely available in late 2022.
They don’t find this pattern for all occupations, as shown in the following figure from their paper.
Finally, they show results by occupational quintiles, with workers ages aged 22 to 25 being hard hit in the two occupational quintiles (4 and 5) most exposted to AI. The data show total employment growth from October 2022 to July 2025 by age group and exposure to AI.
Economics blogger Noah Smith has raised an interesting issue about Brynjolfsson, Chandar, and Chen’s results. Why would we expect that the negative effect of AI on employment to be so highly concentrated among younger workers? Why would employment in the most AI exposed occupations be growing rapidly among workers aged 35 and above? Smith wonders “why companies would be rushing to hire new 40-year-old workers in those AI-exposed occupations.” He continues:
“Think about it. Suppose you’re a manager at a software company, and you realize that the coming of AI coding tools means that you don’t need as many software engineers. Yes, you would probably decide to hire fewer 22-year-old engineers. But would you run out and hire a ton of new 40-year-old engineers?“
Both the papers discussed here are worth reading for their insights on how the labor market is evolving in the generative AI era. But taken together, they indicate that it is probably too early to arrive at firm conclusions about the effects of generative AI on the job market for young college graduates or other groups.
This opinion column originally ran at Project Syndicate.
While recent media coverage of the US Federal Reserve has tended to focus on when, and by how much, interest rates will be cut, larger issues loom. The selection of a new Fed chair to succeed Jerome Powell, whose term ends next May, should focus not on short-term market considerations, but on policies and processes that could improve the Fed’s overall performance and accountability.
By demanding that the Fed cut the federal funds rate sharply to boost economic activity and lower the government’s borrowing costs, US President Donald Trump risks pushing the central bank toward an overly inflationary monetary policy. And that, in turn, risks increasing the term premium in the ten-year Treasury yield—the very financial indicator that Treasury Secretary Scott Bessent has emphasized. A higher premium would raise, not lower, borrowing costs for the federal government, households, and businesses alike. Moreover, concerns about the Fed’s independence in setting monetary policy could undermine confidence in US financial markets and further weaken the dollar’s exchange rate.
But this does not imply that Trump should simply seek continuity at the Fed. The Fed, under Powell, has indeed made mistakes, leading to higher inflation, sometimes inept and uncoordinated communications, and an unclear strategy for monetary policy.
I do not share the opinion of Trump and his advisers that the Fed has acted from political or partisan motives. Even when I have disagreed with Fed officials or Powell on matters of policy, I have not doubted their integrity. However, given their mistakes, I do believe that some institutional introspection is warranted. The next chair—along with the Board of Governors and the Federal Open Market Committee—will have many policy questions to address beyond the near-term path for the federal funds rate.
Three issues are particularly important. The first is the Fed’s dual mandate: to ensure stable prices and maximum employment. Many economists (including me) have been critical of the Fed for exhibiting an inflationary bias in 2021 and 2022. The highest inflation rate in 40 years raised pressing questions about whether the Fed has assigned the right weights to inflation and employment.
Clearly, the strategy of pursuing a flexible average inflation target (implying that inflation can be permitted to rise above 2% if it had previously been below 2%) has not been successful. What new approach should the Fed adopt to hit its inflation target? And how can the Fed be held more accountable to Congress and the public? Should it issue a regular inflation report?
The second issue concerns the size and composition of the Fed’s balance sheet. Since the global financial crisis of 2008, the Fed has had a much larger balance sheet and has evolved toward an “ample reserves model” (implying a perpetually high level of reserves). But how large must the balance sheet be to conduct monetary policy, and how important should long-term Treasury debt and mortgage-backed securities be, relative to the rest of the balance sheet? If such assets are to play a central role, how can the Fed best separate the conduct of monetary policy from that of fiscal policy?
The third issue is financial regulation. What regulatory changes does the Fed believe are needed to avoid the kind of costly stresses in the Treasury market we have witnessed in recent years? How can bank supervision be improved? Given that regulation is an inherently political subject, how can the Fed best separate these activities from its monetary policymaking (where independence is critical)?
Addressing these policy questions requires a rethink of process, too. The Fed would be more effective in dealing with a changing economic environment if it acknowledged and debated more diverse viewpoints about the roles of monetary policy and financial regulation in how the economy works.
The Fed’s inflation mistakes, overconfidence in financial regulation, and other errors partly reflect the “groupthink” to which all organizations are prone. Regional Fed presidents’ views traditionally have reflected their own backgrounds and local conditions, but that doesn’t translate easily into a diversity of economic views. Instead of choosing Fed officials based on how they are likely to vote at the next rate-setting meeting, Trump should put more weight on intellectual and experiential diversity. Equally, the Fed itself could more actively seek and listen to dissenting views from academic and business leaders.
Raising questions about policy and process offers guidance about the characteristics that the next Fed chair will need to succeed. These obviously include knowledge of monetary policy and financial regulation and mature, independent judgment; but they also include diverse leadership experience and an openness to new ideas and perspectives that might enhance the institution’s performance and accountability. One hopes that Trump’s selection of the next Fed chair, and the Senate’s confirmation process, will emphasize these attributes.
In today’s episode, Glenn Hubbard and Tony O’Brien take on three timely topics that are shaping economic conversations across the country. They begin with a discussion on tariffs, exploring how recent trade policies are influencing prices, production decisions, and global relationships. From there, they turn to the independence of the Federal Reserve Bank, explaining why central bank autonomy is essential for sound monetary policy and what risks arise when political pressures creep in. Finally, they shed light on the Bureau of Labor Statistics (BLS), unpacking how its data collection and reporting play a vital role in guiding both public understanding and policymaking.
It’s a lively and informative conversation that brings clarity to complex issues—and it’s perfect for students, instructors, and anyone interested in how economics connects to the real world.
In June, the U.S. Census Bureau released its population estimates for 2024. Included was the following graphic showing the change in the U.S. population pyramid from 2004 to 2024. As the graphic shows, people 65 years and older have increased as a fraction of the total population, while children have decreased as a fraction of the total population. (The Census considers everyone 17 and younger to be a child.) Between 2004 and 2024, people 65 and older increased from 12.4 percent of the population to 18.0 percent. People younger than 18 fell from 25.0 percent of the population in 2004 to 21.5 percent in 2024.
The aging of the U.S. population reflects falling birth rates. Demographers and economists typically measure birth rates as the total fertility rate (TFR), which is defined by the World Bank as: “The number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates currently observed.” The TFR has the advantage over the simple birth rate—which is the number of live births per thousand people—because the TFR corrects for the age structure of a country’s female population. Leaving aside the effects of immigration and emigration, a TFR of 2.1 is necessary to keep a country’s population stable. Stated another way, a country needs a TFR of 2.1 to achieve replacement level fertility. A country with a TFR above 2.1 experiences long-run population growth, while a country with a TFR of less than 2.1 experiences long-run population decline.
The following figure shows the TFR for the United States for each year between 1960 and 2023. Since 1971, the TFR has been below 2.1 in every year except for 2006 and 2007. Immigration has helped to offset the effects on population growth of a TFR below 2.1.
The United States is not alone in experiencing a sharp decline in its TFR since the 1960s. The following figure shows some other countries that currently have below replacement level fertility, including some countries—such as China, Japan, Korea, and Mexico—in which TFRs were well above 5 in the 1960s. In fact, only a relatively few countries, such as Israel and some countries in sub-Saharan Africa are still experiencing above replacement level fertility.
An aging population raises the number of retired people relative to the number of workers, making it difficult for governments to finance pensions and health care for older people. We discuss this problem with respect to the U.S. Social Security and Medicare programs in an Apply the Concept in Macroeconomics, Chapter 16 (Economics, Chapter 26 and Essentials of Economics, Chapter 18). Countries experiencing a declining population typically also experience lower rates of economic growth than do countries with growing populations. Finally, as we discuss in an Apply the Concept in Microeconomics, Chapter 3, different generations often differ in the mix of products they buy. For instance, a declining number of children results in declining demand for diapers, strollers, and toys.
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