The winner of the 2026 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel will not be revealed until Monday, but economists and analysts are already speculating on potential recipients. While the official announcement is imminent, the period leading up to it has sparked interest in the research that has yet to receive the prestigious Swedish medal.
Although the prize is commonly referred to as the Nobel Prize in economics, it was established by Sweden’s central bank in 1968 rather than by Alfred Nobel’s will. Selections typically favor older researchers whose work has demonstrated long-term impact, though committees also consider contemporary relevance and the development of robust empirical tools.
To gauge the odds, some have turned to prediction markets. However, the market for this year’s economics prize is notably thin. As of Tuesday, Oct. 6, trading volume on Kalshi reached only approximately $11,000. In stark contrast, the platform’s main market for the 2024 presidential election recorded roughly $670 million in volume. Due to the low activity, price movements on the Nobel market have been volatile and sensitive to small bets, suggesting the data reflects the views of a limited number of traders rather than a broad consensus.
Despite the limited liquidity, the rankings provide a lens into significant economic scholarship. As of late Tuesday afternoon, Ariel Pakes and Susan Athey were competing for the top spot on Kalshi’s list, with Robert Barro rounding out the top three.
Pakes, a Harvard University economist, is recognized for his foundational work in industrial organization. Alongside Steven Berry and James Levinsohn, he developed the BLP model in 1995, which analyzes how consumers choose between competing products. This framework has become essential for antitrust regulators evaluating whether proposed mergers might harm consumers by raising prices.
Athy, a professor at the Stanford Graduate School of Business, is a leading figure in the economics of technology. A recipient of the 2007 John Bates Clark Medal, she was the first woman to win the award, which honors economists under 40. Her research has pioneered the application of machine learning to causal inference, helping to determine the real-world effects of policies such as job-training programs. She has also been a vocal advocate against discrimination and harassment within the economics profession.
Barro, also at Harvard, contributed to modern macroeconomics through theories on how expectations shape economic outcomes. He is particularly known for his work on Ricardian equivalence, which suggests that government tax cuts funded by borrowing may not stimulate spending because consumers anticipate future tax increases to cover the debt.
Other prominent economists mentioned as potential candidates include Thomas Piketty and Emmanuel Saez, whose research on wealth inequality helped popularize the concept of the “one percent”; Raj Chetty of Harvard, known for his work on economic mobility and the American Dream; David Autor of MIT, who has extensively studied the impact of technology and trade, including the “China Shock,” on labor markets; and Janet Currie of Yale, whose research highlights the long-term economic consequences of childhood conditions.
While prediction markets and expert analysis offer intriguing possibilities, they are not definitive. The actual results will be announced next week, with further analysis expected in NPR’s Planet Money newsletter and The Indicator podcast.
The trading volume on these prediction markets is laughably low compared to political bets. Hard to trust any odds with only eleven grand at stake.
Is anyone else concerned about Ricardian equivalence getting considered seriously again? It feels like an outdated framework for today’s fiscal debates.
Susan Athey being the frontrunner would be a massive milestone. Her work on ML and causal inference is shaping the entire field right now.
BLP model is truly the backbone of modern antitrust analysis. Pakes deserves every bit of this attention for his foundational work.