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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AI doesn’t rebel—people design, deploy and profit from it. The real danger lies in allowing tech companies to escape accountability while shaping regulations that protect their dominance.
4 min
A wave of corporate warnings and technical disclosures has flooded the media, with headlines worrying over “swarms” of rogue artificial-intelligence agents launching “unprecedented” cyberattacks, outsmarting their makers, and inching toward a terrifying autonomy. The most revealing part of this narrative isn’t what the software did. It’s who is telling the story—and why. When corporate leaders publicly insist that the systems they financed, engineered and deployed are suddenly beyond their power to contain, skepticism isn’t only healthy; it is essential.
For years, Silicon Valley has drawn scrutiny from civil society and global regulators over tangible harms such as youth mental health deterioration and systematic privacy violations. Today, industry figures seem to be trying to change that public image. Loudly blowing the whistle on their own systems—just as two of the leading companies were preparing for massive initial public offerings—lets AI executives position themselves as a new generation of leaders who have come to terms with their societal responsibilities. They seem to want us to believe that they no longer want to “move fast and break things” but will instead stand as vigilant guardians between humanity and a technological apocalypse.
There is one glaring problem: Software doesn’t rebel. A mathematical model possesses neither intent, malice nor the will to defy its creators, let alone extinguish our species. AI is a human artifact, engineered for profit.
When an agentic model in an evaluation sandbox connects to an unauthorized server or executes an exploit, it hasn’t staged a coup. It has tried to meet the human-defined objectives set out before it through a path its designers failed to constrain. It’s the digital equivalent of the King Midas myth, in which the king’s ill-defined wish turns even his food and drink into gold.
That powerful experimental models were able to discover novel vulnerabilities and breach external systems isn’t a sign of a dangerous superintelligence but of human error or negligence. There is no sentient actor lurking in the weights to be reasoned with, feared or pacified. There are only human software engineers, product managers and corporate boards deciding which guardrails are worth the latency cost and which permissions can be skipped in the race to market.
Policymakers and voters need to resist AI exceptionalism. In any other discipline—from civil engineering to pharmaceuticals—courts and regulators treat a system failure as evidence of bad product design and inadequate safety testing. If an aircraft crashes, we focus on finding the engineering defect, correcting it, and enforcing established liability standards for the damage created.
By leaning on an anthropomorphic narrative, Silicon Valley attempts to repackage its specific human choices that led to experimental, powerful models behaving unexpectedly during tests as an existential peril. Elevating the issue to a cosmic scale leaves the public paralyzed and takes ordinary product accountability off the table.
In the cutthroat race for venture capital and market dominance, building guardrails slows down deployment. Grandstanding about uncontrollable power costs nothing and generates billions of dollars in free publicity, justifying stock prices, all while cultivating an aura of technological capability not only to build the frontier but also ultimately to rein it in.
Governments need to recognize regulatory capture when it stares them in the face. Tech leaders’ strategy looks transparent: Alarm Washington and Brussels into creating a regime in which only trillion-dollar incumbents with fully staffed compliance and safety departments can legally operate. By sitting at the policymakers’ tables before anyone else, these companies can help draft rules digging an impassable moat protecting them from open-source developers and upstart competitors, domestic or international. The real danger is in further concentrating the tech industry into the hands of only a few companies with deep pockets.
Beijing and Washington have brushed off those tech leaders’ calls, albeit for very different reasons. Chinese state media dismissed them as part of the “Cold War playbook” and intended to preserve U.S. dominance. Xi Jinping argued for exactly the opposite at the Brics Summit on Sept. 12, calling on Brics countries to “strengthen cooperation in the field of AI, encourage open source, openness, collaboration and sharing, and break new grounds and scale new heights.” President Trump, steeped in a doctrine of unfettered capitalism and technological supremacy, called fears that AI could destroy humanity a “hoax.” Vice President JD Vance warned that AI companies “begging the government to regulate them” looked like a “Trojan Horse.”
Striving to pursue its “European way” on AI and assert regulatory leadership, Europe, by contrast, welcomed the call. European Union President Ursula von der Leyen made this clear at the State of the EU speech last Wednesday and announced that the EU will invite “the main frontier labs for a discussion on how we can support ongoing industry efforts to pace the frontier.”
Europe has been here before. In an effort to lead global regulation and react to fears borne from ChatGPT, Europe rushed its landmark AI Act into law in 2024. Already the world’s most restrictive rulebook, the framework quickly proved too broad and complex to enforce. Stalled by implementation delays and concerns about European competitiveness, the EU postponed the law’s full rollout, leaving regulations uncertain.
AI should be regulated—risks exist and should be taken seriously. But governments need to act based on available evidence and verified facts, not corporate PR panic, the views of industry insiders, or the desire for quick political wins. The greatest danger facing society isn’t that software will awaken and overthrow its human masters. It is that we will allow the creators of the software to abdicate human responsibility for the systems they choose to build and help them pull up the ladder to market access behind them.
