The Primary Breadwinner Is Disappearing From More Homes
The economics of marriage are changing, but women still take on more of the unpaid labour
4 min
Nearly a third of marriages today have no primary breadwinner, as women continue to make strides toward greater equality at work and home.
About 30% of U.S. opposite-sex marriages are egalitarian in earnings, according to new data from Pew Research Center, meaning each spouse earns somewhere between 40% and 60% of the couples’ joint earnings. One of the main drivers of the shift is younger women making more money, said Pew.
The share of women earning more than their husbands has more than tripled from 5% to 16% over the last 50 years. In 1972, 49% of husbands were the sole breadwinner, meaning the husband had positive earnings and the wife had no earnings. By 2022, that share had dropped to 23% of opposite-sex marriages.
But the larger financial contributions by women don’t mean that relationships are more equal or women are better off in every realm of life, said Richard Fry, senior researcher at Pew Research Center.
Even when women earn as much as their husbands, they still put in around two more hours a week on caregiving than their husbands do, plus another 2.5 hours more on housework, according to Pew. In those same relationships, men spend nearly 3.5 more hours on leisure activities, such as watching television or playing video games, than their wives do.
Women’s economic role in marriages continues to rise despite a persistent gender pay gap and declining labor-force participation, Mr. Fry said. “In spite of some trends that would suggest to me that women’s economic role would not be growing, what we found was ‘No, it still is,’” he said.
Financial advisers and researchers say the changing money dynamic can cause marital strife, or in some cases, divorce.
Changes in breadwinner status “can lead to a lot of frustrations and arguments and resentment,” said Stacy Francis, president and chief executive of wealth-management firm Francis Financial and founder of a financial-education nonprofit.
When Ms. Francis, who often works with breadwinning women, surpassed her husband in earnings, she said the pair celebrated. After years of bearing the burden of bringing home most of the bacon, her husband was somewhat relieved to turn the job over to her, she said.
But Ms. Francis, now 48, soon found herself spending more time in the kitchen, throwing herself into the local parent-teacher association and planning her son’s prom—all, she said, in an effort to somehow compensate for other work and time spent away.
“It made me feel less feminine to earn more than my husband,” she said. “I realised, looking back, that I myself had to get comfortable with that role.”
Men remain the breadwinner in most marriages, meaning they earn more than 60% of the total earnings, Pew found.
The marriages with the highest total income are those in which both spouses are bringing in money. Marriages in which women are the primary breadwinners earn more than those in which men hold the same role: $145,000 in median income compared with $121,000 for marriages overall, according to the Pew data. A primary breadwinner in Pew’s research occurs when one spouse earns more than 60% of the household earnings.
Sole-breadwinner couples, or marriages in which one spouse has earnings and the other has none, make significantly less, with median incomes of around $75,000. Such couples also are more likely to be below the poverty line.
When women are the sole breadwinners, men spend more time on caregiving and a more equal amount of housework, compared with egalitarian marriages. But women still spend roughly the same amount of time on caregiving and household work, regardless of whether they are in egalitarian marriages or are sole or primary breadwinners, Pew found. Women without children are more likely to be the primary breadwinner than those with children.
Spouses within same-sex couples, however, tend to split the domestic labor more equally than their heterosexual counterparts, research shows.
Some researchers say one reason for the housework divide is that most of these gender roles have been built up over generations. There is a fear from some women that stopping this work could risk their marriage.
“We still see that there are remnants and large cultural issues associated with the sensitivity of women’s economic success, as a thing that destroys relationships,” said Johanna Rickne, professor of economics at the Swedish Institute for Social Research at Stockholm University.
Both husbands and wives can work to address these imbalances, said Jennifer Clark, a 34-year-old digital marketer based outside Chicago.
While her husband, a director of an audio-production company, has earned more than Ms. Clark for much of their 10-year marriage, she sets the monthly budget and manages household finances.
“It doesn’t feel like he has a larger share of the finances even though he is earning that money,” she said.
Throughout their marriage, Ms. Clark worked in freelance and part-time roles while her husband had full-time jobs. During those periods, she said, she bore a greater share of the household and caregiving responsibilities for their two children. But talking about their finances and making decisions together helped them remain equal partners.
“I would say I’ve always had a pretty good sense of financial autonomy, even with money I didn’t necessarily earn, because we make those decisions collaboratively,” she said.
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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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