Nobel Prize in Economics Awarded to Harvard’s Claudia Goldin for Work on Gender Gaps
Economic historian and labour economist has tracked the changing fortunes of women in the workplace
3 min
BOSTON—Harvard University’s Claudia Goldin is a labor economist, teacher and mentor. She is now also a Nobel Prize winner for her groundbreaking research on women in the workforce.
Goldin was awarded the Nobel Prize in Economic Sciences on Monday, the third woman to receive the economics prize since the award started in 1969. The 77-year-old Harvard economist has spent decades analysing troves of data to produce research illuminating the history of women’s job-market experiences.
Goldin’s expansive work portfolio includes pieces on the drivers of female labor-force participation, the origins of the gender pay gap and hiring biases against women. Her paper, “Why Women Won,” which documented the evolution of women’s legal rights, published this month.
“Goldin’s discoveries have vast societal implications,” said Randi Hjalmarsson, professor of economics at the University of Gothenburg in Sweden.
Goldin was admittedly tired upon entering Monday’s press conference at Harvard. She was, after all, asleep when she received the early-morning call with the news of her Nobel Prize. Still, her passion regarding decades of research and relationship-building radiated as she spoke at a press briefing.
“The increase of women in economics is important for a host of reasons,” Goldin said. “For me personally it has been important because I have had the most wonderful co-authors.”
One such co-researcher, Claudia Olivetti of Dartmouth College, said Goldin’s body of work has shaped much of the current research on women and labor markets. Perhaps less well known, Olivetti said, is Goldin’s extraordinary mentorship of women.
Goldin “has been a source of inspiration to many women in economics, generously sharing her experiences and demonstrating the possibilities of success,” Olivetti said.
Some professors view themselves as researchers, rather than teachers. Not Goldin.
“I could never do research without doing teaching,” she said. “When I teach, I am forced to confront what I think is the truth.”
Goldin was the first woman to secure tenure in Harvard’s economics department. She follows Esther Duflo in 2019 and Elinor Ostrom in 2009 as female recipients of the economics Nobel Prize.
Goldin is married to Lawrence Katz, also a Harvard economist. Both are avid bird watchers and hikers, colleagues said. She has a 13-year-old golden retriever named Pika and no children.
Around the world, 50% of women have paid jobs, compared with 80% of men, although that gap is smaller in advanced economies. Across the developed economies, women earn 13% less on average and are less likely to play senior roles in the organisations they work for.
Goldin’s research questioned the assumption that women had steadily, or would inevitably, narrow those gaps. Using data that had previously attracted little attention, she established that far fewer women worked in paid employment in the early 1900s than in 1800, while that share rebounded as the 20th century advanced, albeit slowly.
Her writing includes 1990’s “Understanding the Gender Gap: An Economic History of American Women.” Examining 200 years of data, Goldin tracked the changing fortunes of women in the workplace as it changed from farm to factory to office.
She also identified some of the considerations that affected the decisions made by women about their participation in the workforce, as well as the constraints they faced at particular times. In one well-known paper, she examined the effect of the contraceptive pill on decisions about work and marriage.
The pay gap between male and female workers had long been attributed to differences in educational attainment, with women typically spending fewer years in formal education.
But that can no longer be true of many developed countries, where women are now better educated on average than men. Instead, Goldin’s work indicates that the gap in pay occurs with the birth of a first child, with women typically devoting more time to child care.
But darker forces are also at work. In one paper, Goldin and co-author Cecilia Rouse from Princeton University showed that the number of female members of the leading U.S. symphony orchestras rose sharply in the 1980s partly because of the adoption of “blind” auditions, where the candidate for an orchestra position auditioned behind a screen, concealing their gender or race from those doing the hiring.
In their paper, called “Orchestrating Impartiality: The Impact of ‘Blind Auditions’ on Female Musicians,” the authors found data across decades of hiring by symphonies both before and after the introduction of blind auditions to show that about a quarter of the increase in female members of orchestras over that time was due to blind auditions, suggesting previous bias.
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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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