High-Earning Men Are Cutting Back on Their Working Hours
While most U.S. workers are putting in fewer hours, men in the top 10% of earners cut back their time on the job the most, according to a new study
4 min
American workers have cut the number of hours they spend in their jobs since 2019, but no group has dialled back its time on the clock more than young, high-earning men whose jobs typically demand long hours.
The top-earning 10% of men in the U.S. labor market logged 77 fewer work hours in 2022, on average, than those in the same earnings group in 2019, according to a new study of federal data by the economics department at Washington University in St. Louis. That translates to 1.5 hours less time on the job each workweek, or a 3% reduction in hours. Over the same three-year period, the top-earning 10% of women cut back time at work by 29 hours, which translates to about half an hour less work each week, or a 1% reduction.
High-earning men in the 25-to-39 age range who could be described as “workaholics” were pulling back, often by choice, says Yongseok Shin, a professor of economics, who co-wrote the paper. Since this group already put in longer hours than the typical U.S. worker—and women at the highest income levels—these high earners had longer work days to trim, Dr. Shin says, and still worked more hours than the average.
The drop in working hours among high-earning men and women helps explain why the U.S. job market is even tighter than what would be expected given the current levels of unemployment and labour force participation, Dr. Shin says.
“These are the people who have that bargaining power,” Dr. Shin says of the leverage many workers have had over their employers in a tight job market. “They have the privilege to decide how many hours they want to work without worrying too much about their economic livelihood.”
The paper published by the National Bureau of Economic Research, which isn’t yet peer reviewed, suggests high earners were more likely to benefit from flexible working arrangements, which could be a factor in reduced work hours.
Before the pandemic, Eli Albrecht, a lawyer in the Washington, D.C., area, says he worked between 80 to 90 hours a week. Now, he says he puts in 60 to 70 hours each week. That’s still more than most men in America, who averaged 40.5 hours a week in 2021, according to federal data.
Mr. Albrecht’s schedule changed when he shared Zoom school duties for two of his young children with his wife. He’s maintained the reduced hours because it’s making his relationship more equitable, he says, and gives him family time.
“I used to feel—and a lot of dads used to feel—that just by providing for the family financially, that was sufficient. And it’s just not,” Mr. Albrecht says.
The downshift documented by Dr. Shin and his colleagues occurred as many professionals have been reassessing their ambitions and the value of working long hours. Emboldened by a strong job market, millions of Americans quit their jobs in search of better hours and more flexibility.
Overall, U.S. employees worked 18 fewer hours a year, on average, in 2022 compared with 2019, with employed men putting in 28 fewer hours last year and employed women cutting their time by nine hours, data from the U.S. Census Bureau’s Current Population Survey show. The average male worker put in 2,006 hours last year, while the average female worker logged 1,758 hours.
Separate data from the Census Bureau suggests that men with families, in particular, are working less. Between 2019 and 2021, married men devoted roughly 13 fewer minutes, on average, to work each day, according to the American Time Use Survey, which hasn’t yet published 2022 figures. They spent more time on socialising and relaxing, as well as household activities, according to men surveyed by the Census Bureau. The amount of time unmarried men spent on work changed little during that same period.
As high-earning workers in the U.S. cut back, low-wage workers increased their hours, according to Dr. Shin’s research. The bottom-earning 10% of working men logged 41 hours more in 2022, on average, than in 2019. Women in the lowest earning group boosted their hours worked by 52 last year compared with 2019.
While women work fewer hours than men, the unpaid labor they perform outside of their jobs has been well documented. Many working mothers take what’s termed a “second shift,” devoting more time outside work hours to child care and housework.
Maryann B. Zaki, a mother of three who has worked at several firms, including in big law, recently launched her own practice in Houston, giving her more control over her hours. She says she’s noticed more men in her field opting for reduced schedules, sometimes working 80% of the hours normally expected—which can range from 40 to more than 80 a week—in exchange for a 20% pay cut. For the average lawyer, that would amount to a salary reduction of tens of thousands of dollars each year; such arrangements were initially offered to aid working mothers.
Responding to new expectations of work-life balance may be particularly vexing for industries already facing staffing shortages, such as those in medicine. Dr. Lotte Dyrbye, the chief well-being officer for the University of Colorado School of Medicine, said she often hears from early-career physicians and other medical professionals who want to work fewer hours to avoid burnout.
These medical workers are deciding that to be in it for the long haul requires a day every week or two to decompress, Dr. Dyrbye says. But as staff cut back their hours, it costs medical organisations money and may compromise access to care.
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Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
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