A perennial media story this time of year looks at whether a Thanksgiving turkey dinner costs more or less than last year. Not too surprisingly, the answer depends on what side dishes you serve with the turkey. Deloitte provides tax, consulting, and other services to businesses. Their calculation of the cost of a Thanksgiving dinner over the past three years can be found here.
The following image shows the food that they include in their cost calculation. For that particular Thanksgiving dinner, the cost is slightly higher than in 2024, although slightly lower than in 2023.
Image from deloitte.com
The following image from the American Farm Bureau Federation, a lobbying organization for U.S. farmers, shows the food they include in their calculation of the cost of a Thanksgiving dinner.
Image from fb.org
For a Thanksgiving dinner with those side dishes, the price is about 5 percent lower this year than last year.
Image from fb.org
Note that the two estimates differ in the cost of the turkey. It’s not clear whether the difference is due to the size of the turkey or to differences in the price of the turkey. Related point: The Bureau of Labor Statistics (BLS) stopped collecting data on retail turkey prices in February 2020, at the start of the pandemic, and never resumed collecting them. Here’s the link to the BLS retail turkey price series on FRED. The series begins in January 1980 and ends in February 2020.
Justin Fox, in a column on bloomberg.com, notes that demand for turkey has been declining in recent years. The following figure uses data on turkey consumption per capita from the U.S. Department of Agriculture.
Turkey consumption peaked at 18.2 pounds per person in 1996 and has fallen to an estimated 13.1 pounds per person in 2025—a decline of about 28 percent. Is this decline an indication that people have moved away from eating turkey for Thanksgiving? Fox argues that it likely doesn’t. Note the rapid rise of turkey consumption between 1980 and 1990. Fox believes the surge in consumption was due to “both chicken and turkey [consumption increasing] as health concerns led many Americans to shun red meat starting in the late 1970s ….” In recent years, though, “red-meat consumption has steadied … chicken consumption has continued to rise, and turkey is losing out. Maybe people just don’t like how it tastes.” Glenn and Tony agree that, alas, turkey is often dry—although, admittedly, skilled cooks claim that it isn’t dry when prepared properly.
So, turkey may be holding its own at the heart of Thanksgiving dinners, but seems to be struggling to get on the menu during the rest of the year.
Most large firms selling consumer goods continually evaluate which new products they should introduce. Managers of these firms are aware that if they fail to fill a market niche, their competitors or a new firm may develop a product to fill the niche. Similarly, firms search for ways to improve their existing products.
For example, Ferrara Candy, had introduced Nerds in 1983. Although Nerds experienced steady sales over the following years, company managers decided to devote resources to improving the brand. In 2020, they introduced Nerds Gummy Clusters, which an article in the Wall Street Journal describes as being “crunchy outside and gummy inside.” Over five years, sales of Nerds increased from $50 millions to $500 million. Although the company’s market research “suggested that Nerds Gummy Clusters would be a dud … executives at Ferrara Candy went with their guts—and the product became a smash.”
Image of Nerds Gummy Clusters from nerdscandy.com
Firms differ on the extent to which they rely on market research—such as focus groups or polls of consumers—when introducing a new product or overhauling an existing product. Henry Ford became the richest man in the United States by introducing the Model T, the first low-priced and reliable mass-produced automobile. But Ford once remarked that if before introducing the Model T he had asked people the best way to improve transportation they would probably have told him to develop a faster horse. (Note that there’s a debate as to whether Ford ever actually made this observation.) Apple co-founder Steve Jobs took a similar view, once remaking in an interview that “it’s really hard to design products by focus groups. A lot of times, people don’t know what they want until you show it to them.” In another interview, Jobs stated: “We do no market research. We don’t hire consultants.”
Unsurprisingly, not all new products large firms introduce are successful—whether the products were developed as a result of market research or relied on the hunches of a company’s managers. To take two famous examples, consider the products shown in image at the beginning of this post—“New Coke” and the Ford Edsel.
Pepsi and Coke have been in an intense rivalry for decades. In the 1980s, Pepsi began to gain market share at Coke’s expense as a result of television commercials showcasing the “Pepsi Challenge.” The Pepsi Challenge had consumers choose from colas in two unlabeled cups. Consumers overwhelming chose the cup containing Pepsi. Coke’s management came to believe that Pepsi was winning the blind taste tests because Pepsi was sweeter than Coke and consumers tend to favor sweeter colas. In 1985, Coke’s managers decided to replace the existing Coke formula—which had been largely unchanged for almost 100 years—with New Coke, which had a sweeter taste. Unfortunately for Coke’s managers, consumers’ reaction to New Coke was strongly negative. Less than three months later, the company reintroduced the original Coke, now labeled “Coke Classic.” Although Coke produced both versions of the cola for a number of years, eventually they stopped selling New Coke.
Through the 1920s, the Ford Motor Company produced only two car models—the low-priced Model T and the high-priced Lincoln. That strategy left an opening for General Motors during the 1920s to introduce a variety of car models at a number of price levels. Ford scrambled during the 1930s and after the end of World War II in 1945 to add new models that would compete directly with some of GM’s models. After a major investment in new capacity and an elaborate marketing campaign, Ford introduced the Edsel in September 1957 to compete against GM’s mid-priced models: Pontiac, Oldsmobile, and Buick.
Unfortunately, the Edsel was introduced during a sharp, although relatively short, economic recession. As we discuss in Macroeconomics, Chapter 13 (Economics, Chapter 23), consumers typically cut back on purchases of consumer durables like automobiles during a recession. In addition, the Edsel suffered from reliability problems and many consumers disliked the unusual design, particularly of the front of the car. Consumers were also puzzled by the name Edsel. Ford CEO Henry Ford II was the grandson of Henry Ford and the son of Edsel Ford, who had died in 1943. Henry Ford II named in the car in honor of his father but the unusual name didn’t appeal to consumers. Ford ceased production of the car in November 1959 after losing $250 million, which was one of the largest losses in business history to that point. The name “Edsel” has lived on as a synonym for a disastrous product launch.
Screenshot
Image of iPhone Air from apple.com
Apple earns about half of its revenue and more than half of its profit from iPhone sales. Making sure that it is able to match or exceed the smartphone features offered by competitors is a top priority for CEO Tim Cook and other Apple managers. Because Apple’s iPhones are higher-priced than many other smartphones, Apple has tried various approaches to competing in the market for lower-priced smartphones.
In 2013, Apple was successful in introducing the iPad Air, a thinner, lower-priced version of its popular iPad. Apple introduced the iPhone Air in September 2025, hoping to duplicate the success of the iPad Air. The iPhone Air has a titanium frame and is lighter than the regular iPhone model. The Air is also thinner, which means that its camera, speaker, and its battery are all a step down from the regular iPhone 17 model. In addition, while the iPhone Air’s price is $100 lower than the iPhone 17 Pro, it’s $200 higher than the base model iPhone 17.
Unlike with the iPad Air, Apple doesn’t seem to have aimed the iPhone Air at consumers looking for a lower-priced alternative. Instead, Apple appears to have targeted consumers who value a thinner, lighter phone that appears more stylish, because of its titanium frame, and who are willing to sacrifice some camera and sound quality, as well as battery life. An article in the Wall Street Journal declared that: “The Air is the company’s most innovative smartphone design since the iPhone X in 2017.” As it has turned out, there are apparently fewer consumers who value this mix of features in a smartphone than Apple had expected.
Sales were sufficiently disappointing that within a month of its introduction, Apple ordered suppliers to cut back production of iPhone Air components by more than 80 percent. Apple was expected to produce 1 million fewer iPhone Airs during 2025 than the company had initially planned. An article in the Wall Street Journal labeled the iPhone Air “a marketing win and a sales flop.” According to a survey by the KeyBanc investment firm there was “virtually no demand for [the] iPhone Air.”
Was Apple having its New Coke moment? There seems little doubt that the iPhone Air has been a very disappointing new product launch. But its very slow sales haven’t inflicted nearly the damage that New Coke caused Coca-Cola or that the Edsel caused Ford. A particularly damaging aspect of New Coke was that was meant as a replacement for the existing Coke, which was being pulled from production. The result was a larger decline in sales than if New Coke had been offered for sale alongside the existing Coke. Similarly, Ford set up a whole new division of the company to produce and sell the Edsel. When Edsel production had to be stopped after only two years, the losses were much greater than they would have been if Edsel production hadn’t been planned to be such a large fraction of Ford’s total production of automobiles.
Although very slow iPhone Air sales have caused Apple to incur losses on the model, the Air was meant to be one of several iPhone models and not the only iPhone model. Clearly investors don’t believe that problems with the Air will matter much to Apple’s profits in the long run. The following graphic from the Wall Street Journal shows that Apple’s stock price has kept rising even after news of serious problems with Air sales became public in late October.
So, while the iPhone Air will likely go down as a failed product launch, it won’t achieve the legendary status of New Coke or the Edsel.
Supports:Microeconomics, Macroeconomics, Economics, and Essentials of Economics, Chapter 4, Section 4.4
Image generated by ChapGPT
The model of demand and supply is useful in analyzing the effects of tariffs. In Chapter 9, Section 9.4 (Macroeconomics, Chapter 7, Section 7.4) we analyze the situation—for instance, the market for sugar—when U.S. demand is a small fraction of total world demand and when the U.S. both produces the good and imports it.
In this problem, we look at the television market and assume that no domestic firms make televisions. (A few U.S. firms assemble limited numbers of televisions from imported components.) As a result, the supply of televisions consists entirely of imports. Beginning in April, the Trump administration increased tariff rates on imports of televisions from Japan, South Korea, China, and other countries. Tariffs are effectively a tax on imports, so we can use the analysis in Chapter 4, Section 4.4, “The Economic Effect of Taxes” to analyze the effect of tariffs on the market for televisions.
Use a demand and supply graph to illustrate the effect of an increased tariff on imported televisions on the market for televisions in the United States. Be sure that your graph shows any shifts of the curves and the equilibrium price and quantity of televisions before and after the tariff increase.
An article in the Wall Street Journal discussed the effect of tariffs on the market for used goods. Use a second demand and supply graph to show the effect of a tariff on imports of new televisions on the market in the United States for used televisions. Assume that no used televisions are imported and that the supply curve for used televisions is upward sloping.
Solving the Problem Step 1: Review the chapter material. This problem is about the effect of a tariff on an imported good on the domestic market for the good. Because a tariff is a like a tax, you may want to review Chapter 4, Section 4.4, “The Economic Effect of Taxes.”
Step 2: Answer part a. by drawing a demand and supply graph of the market for televisions in the United States that illustrates the effect of an increased tariff on imported televisions. The following figure shows that a tariff causes the supply curve of televisions to shift up from S1 to S2. As a result, the equilibrium price increases from P1 to P2, while the equilibrium quantity falls from Q1 to Q2.
Step 2: Answer part b. by drawing a demand and supply graph of the market for used televisions in the United States that illustrates the effect on that market of an increased tariff on imports of new televisions. Although the tariff on imported televisions doesn’t directly affect the market for used televisions, it does so indirectly. As the article from the Wall Street Journal notes, “Today, in the tariff era, demand for used goods is surging.” Because used televisions are substitutes for new televisions, we would expect that an increase in the price of new televisions would cause the demand curve for used televisions to shift to the right, as shown in the following figure. The result will be that the equilibrium price of used televisions will increase from P1 to P2, while the equilibrium quantity of used televisions will increase from Q1 to Q2.
To summarize: A tariff on imports of new televisions increases the price of both new and used televisions. It decreases the quantity of new televisions sold but increases the quantity of used televisions sold.
Glenn Hubbard and Tony O’Brien begin by examining the challenges facing the Federal Reserve due to incomplete economic data, a result of federal agency shutdowns. Despite limited information, they note that growth remains steady but inflation is above target, creating a conundrum for policymakers. The discussion turns to the upcoming appointment of a new Fed chair and the broader questions of central bank independence and the evolving role of monetary policy. They also address the uncertainty surrounding AI-driven layoffs, referencing contrasting academic views on whether artificial intelligence will complement existing jobs or lead to significant displacement. Both agree that the full impact of AI on productivity and employment will take time to materialize, drawing parallels to the slow adoption of the internet in the 1990s.
The podcast further explores the recent volatility in stock prices of AI-related firms, comparing the current environment to the dot-com bubble and questioning the sustainability of high valuations. Hubbard and O’Brien discuss the effects of tariffs, noting that price increases have been less dramatic than expected due to factors like inventory buffers and contractual delays. They highlight the tension between tariffs as tools for protection and revenue, and the broader implications for manufacturing, agriculture, and consumer prices. The episode concludes with reflections on the importance of ongoing observation and analysis as these economic trends evolve.
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.
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