Nobel economics prize goes to researchers explaining innovation-driven economic growth
Nobel economics prize goes to researchers explaining innovation-driven economic growth
Three Economists Win Nobel for Explaining Why Some Nations Prosper While Others Stagnate
Prize honors breakthrough research revealing the hidden machinery of innovation and economic
STOCKHOLM—When Joel Mokyr's students asked if he might win the Nobel Prize in economics, the Northwestern University professor had a ready answer: He was more likely to be elected pope. He's Jewish, he reminded them.
On Monday morning, before he'd finished his coffee or walked his dog, the 79-year-old economic historian learned he'd beaten those odds. The Royal Swedish Academy of Sciences awarded him and two colleagues—Philippe Aghion of the Collège de France and Peter Howitt of Brown University—the Nobel memorial prize in economics for research that explains why innovation drives growth in ways economists previously couldn't measure or predict.
Their core insight seems deceptively simple: New technologies replace old ones, spurring economic progress. E-commerce killed shopping malls. Netflix buried Blockbuster. The steam engine made the horse and buggy obsolete.
But beneath this surface simplicity lies a profound complexity that eluded economists for generations—and helps explain why some economies surge ahead while others stagnate despite having smart people and capital.
The Knowledge Paradox
Mokyr's breakthrough came in recognizing that innovation requires more than just stumbling onto something that works. His research demonstrated that sustained technological progress demands scientific understanding of why things work—what he calls "propositional knowledge."
This distinction explains a historical puzzle: Why did the Industrial Revolution happen in 18th-century Europe rather than earlier civilizations that also produced inventions? Ancient Rome had rudimentary steam engines. Medieval China invented gunpowder, the compass, and printing. Yet neither society experienced sustained economic growth.
Mokyr's answer: The Enlightenment created something unprecedented—systematic scientific inquiry that transformed isolated inventions into a self-generating process of innovation. "The key to the Industrial Revolution was technology, and technology is knowledge," he wrote. What seems "so self-evident" today was something "economists actually rarely have dealt with explicitly."
The Math Behind Creative Destruction
While Mokyr excavated historical archives, Aghion and Howitt attacked the problem with mathematical models. Their groundbreaking 1992 work constructed equations capturing what economist Joseph Schumpeter called "creative destruction"—the process he deemed "the essential fact about capitalism."
The laureates accomplished something economists had struggled with for decades: translating Schumpeter's philosophical concept into rigorous mathematical equations that could predict when and how innovation drives sustained growth.
How the Model Works
Traditional growth models assumed technological progress happened exogenously—essentially falling like manna from heaven at a constant rate. Aghion and Howitt's insight was that innovation itself responds to economic incentives, creating a feedback loop.
Their model begins with a simple production function where output depends on labor and the quality of technology:
Y = A · L
Where Y represents total output, A measures productivity or technology quality, and L represents labor employed. If a factory employs 100 workers using technology with productivity A = 2.0, it produces 200 units. If innovation raises A to 2.5, the same workers produce 250 units—a 25% increase with no additional labor.
The complexity emerges in how productivity improves over time. Firms invest resources in R&D, racing to discover the next innovation. The probability of success depends on research intensity:
λ = φ · n
Where λ represents the arrival rate of innovations, φ measures research productivity, and n is the number of researchers employed. If an industry employs 50 researchers with productivity φ = 0.002, the annual probability of breakthrough is 10%. Doubling researchers doubles the innovation rate.
When a firm succeeds, productivity jumps discretely by a factor γ (gamma):
A(t+1) = γ · A(t)
This creates a "quality ladder"—each innovation makes the previous technology obsolete while setting a higher bar for the next breakthrough. The transition from horse-drawn carriages to automobiles might represent γ = 5.0 (a 400% improvement), while smartphone processor upgrades might show γ = 1.15 (15% improvement).
The Profit-Innovation Cycle
Here's where the model captures Schumpeter's key insight: monopoly profits from successful innovation fund the next round of research. But each innovator knows they'll eventually be destroyed by the next breakthrough, which limits investment incentives.
Combining these forces yields the model's central result—the economy's growth rate:
g = λ · ln(γ) = φ · n · ln(γ)
This deceptively simple equation encodes a profound insight: Growth depends on research productivity, the number of researchers, and the size of each technological leap. But these variables don't exist in isolation—they depend on institutions, property rights, education systems, and capital markets.
The model explains several non-obvious phenomena:
The Replacement Effect: Incumbent monopolists have less incentive to innovate than challengers, since they'd be replacing their own profits. A firm earning $10 million annually from current technology only gains incremental profit from innovation, while a challenger gains the entire $10 million.
The Step-by-Step Dynamic: When competitors are technologically neck-and-neck, innovation races are fierce. When one pulls ahead, both innovate less—the leader to protect monopoly rents, the laggard from discouragement.
The Growth-Inequality Trade-off: Higher innovation rates increase growth but also generate more creative destruction, creating winners and losers. Bigger technological leaps mean faster growth but more disruption as old firms are destroyed.
The Policy Puzzle: When Government Help Hurts
Perhaps the most surprising insight from the laureates' work is that government support for innovation involves far more complex trade-offs than previously understood.
The Aghion-Howitt model exposed a fundamental tension. Companies underinvest in R&D because they can't capture all the social benefits—their innovations enable future innovations, creating spillovers beyond what any single firm can monetize. This suggests government should subsidize research.
But when one company's innovation displaces another's, the private profits can exceed the social gains, especially if the new product is only marginally better. This suggests R&D investment might already be too high, and subsidies could make it worse.
Which force dominates? The answer depends on specific market conditions, the nature of competition, and the institutional environment. There's no universal prescription.
Patents: The Goldilocks Problem
Strong patent protection increases monopoly profits, encouraging innovation by ensuring inventors can recoup investments. But it also slows knowledge diffusion, reducing the research productivity that enables follow-on innovations.
The laureates' empirical research found that patent protection stimulates innovation most effectively in countries with higher market freedom and competitive pressures. In less competitive environments, strong patents can entrench monopolies without spurring additional innovation.
"Strong protection of intellectual property rights is less important for countries struggling to catch up to the global technology frontier, who rely on copying or adapting existing technologies," Howitt noted. Developing economies benefit more from technology transfer than from protecting domestic innovators who don't yet exist.
For advanced economies at the technology frontier, optimal patent policy must thread the needle: strong enough to incentivize breakthrough innovations, but not so strong that it prevents competitors from building on existing knowledge.
The Competition Paradox
One of the laureates' most counterintuitive findings concerns competition policy. Conventional wisdom held that more competition always increases innovation. The Aghion-Howitt framework revealed an inverted-U relationship.
At low competition levels, increasing competitive pressure spurs innovation—firms must innovate to survive. But at very high competition levels, further increases can reduce innovation because firms can't capture enough profit to justify R&D investments. The relationship peaks at moderate competition levels.
Their empirical work confirmed this prediction: product market reforms increase innovation primarily in industries with moderate competition and strong patent protection—not in highly competitive industries with weak intellectual property rights.
This explains why breaking up monopolies can boost innovation, but creating perfectly competitive markets might paradoxically stifle it.
Tax Policy and Unintended Consequences
The laureates' framework helps explain why R&D tax credits—now used by most OECD countries—can be effective but aren't a panacea.
Recent empirical studies building on their theoretical work found that R&D tax incentives have substantial effects on innovation, with tax price elasticities around 2.6 for smaller firms. But the model reveals hidden costs: tax subsidies can raise researchers' wages rather than increasing R&D volume. One study found that 30% of the subsidy flows to higher salaries rather than additional research.
Aghion and Howitt have identified a broader package of effective growth policies: subsidies to basic research, education, health, and infrastructure; balanced tax policy; property rights protection; competition policy that prevents incumbents from blocking new entrants; and insurance policies that protect firms and employees during economic shocks so R&D survives downturns.
The Measurement Problem
Perhaps the most counterintuitive aspect of their work addresses a modern puzzle: Why don't revolutionary technologies always show up in economic statistics?
A decade ago, many economists argued that recent innovations—smartphones, social media, even the internet—delivered less economic impact than earlier breakthroughs like automobiles or airplanes.
Mokyr pushed back with a deceptively simple observation: Many transformative innovations are nearly free, making them invisible to traditional economic measurements. He pointed to Spotify as an example. "I spent a large amount of my graduate student budget on vinyl records," he told The Associated Press in 2015, later accumulating over 1,000 CDs. Now he accesses vastly more music for a nominal monthly fee.
The value created—the difference between his old spending and current subscription cost—doesn't register properly in GDP calculations. Yet the welfare gain is enormous. This measurement problem masks the true impact of digital innovation, creating a false narrative of stagnation.
A Warning About Creative Destruction's Dark Side
A sophisticated reading of the laureates' work reveals something troubling: The same mathematical machinery that drives prosperity can drive destruction.
Consider military competition during World War II and the Cold War. The Sherman tank made the M3 Lee obsolete. The jet fighter destroyed the propeller plane's dominance. The ICBM rendered strategic bombers partially redundant. Each generation of weapons ruthlessly replaced the previous one, driving relentless innovation.
In several ways, military competition exhibits creative destruction in its most concentrated form:
Innovation size (γ) is enormous: When the P-51 Mustang fighter appeared in 1943, it didn't merely improve upon earlier designs—it represented a γ perhaps as high as 2.0 or 3.0 in combat effectiveness. The atomic bomb represented an even more discontinuous leap.
Innovation rate (λ) accelerates dramatically: During WWII, aircraft designs iterated every 12-18 months. The competitive pressure—literal survival—drove innovation rates far higher than peacetime markets typically achieve.
Research scale (n) grows massive: The Manhattan Project employed 130,000 people at its peak. The Cold War sustained this for decades, with entire Soviet cities devoted to weapons research.
Competition is unambiguous: In warfare, the competitive test is clear. The better tank wins; the inferior design is literally destroyed on the battlefield.
The Critical Difference
The Aghion-Howitt framework reveals that creative destruction can be mathematically identical yet economically opposite depending on one critical factor: what is being optimized.
In the civilian economy, productivity A measures welfare-enhancing output—food production per farmer, cars manufactured per worker, entertainment consumed per dollar. When A rises from 1.0 to 1.5, society becomes 50% wealthier. Everyone can potentially be better off.
In military competition, A measures destructive capacity—kills per soldier, targets destroyed per sortie, cities vaporized per warhead. When military A rises from 1.0 to 1.5, society hasn't become wealthier—it's simply become 50% better at destruction.
The mathematics are identical: g = φ · n · ln(γ) describes the rate at which capability improves. But in one case, g measures prosperity growth; in the other, it measures the growth rate of destructive power.
The Soviet Paradox
Consider the stark historical example: The Soviet Union exhibited extraordinary military innovation during the Cold War, with enormous γ values, massive n values, high λ, and substantial φ. The equation g = φ · n · ln(γ) was operating at full throttle. Soviet military capability growth was substantial.
Yet Soviet civilian living standards stagnated. Resources poured into raising military A (destructive capacity) rather than civilian A (productive capacity). The growth happened in the dimension of how effectively you could destroy, not how well you could produce.
The mathematics worked perfectly. The growth just pointed in the wrong direction.
The Modern Warning
This distinction becomes urgent when considering AI development, biotechnology, cyber capabilities, and other dual-use technologies:
- Autonomous vehicles ↔ Autonomous weapons
- Medical AI ↔ Battlefield AI
- Genetic therapies ↔ Genetic weapons
- Facial recognition for convenience ↔ Facial recognition for surveillance
In each case, the equation operates in both domains. High n (many researchers), substantial φ (rapid improvements), large γ (revolutionary capabilities), yielding high g (fast growth).
But growth of what? Welfare-enhancing productivity or destructive capacity?
As Mokyr put it in his 2016 book A Culture of Growth: "Economic growth is always and everywhere a cultural phenomenon." The institutions and values that direct innovation toward prosperity rather than destruction determine whether creative destruction serves human welfare or merely demonstrates mathematical inevitability.
The laureates gave us the equations that describe how innovation drives change. Those equations work identically whether the change improves human welfare or destroys it. The mathematics don't choose their own objectives—human institutions must.
The Europe Question
The prize arrives as these insights gain urgent policy relevance. Former European Central Bank President Mario Draghi recently warned that Europe faces a widening productivity gap with the United States in digital technology.
Aghion emphasized Monday that Europe possesses "fantastic basic research" but struggles to commercialize discoveries. "We need to harness the full power of innovation," he said, highlighting the gap between scientific knowledge and entrepreneurial execution.
This points to another non-obvious finding: Innovation isn't just about research funding. It requires venture capital ecosystems, risk-tolerant institutions, and mechanisms that connect scientific advances to market applications—the very infrastructure that Mokyr's historical work shows took centuries to develop.
Europe's challenge isn't just funding research—it's creating the full ecosystem that the equations implicitly require: venture capital to fund researchers, institutions that maintain high research productivity, and markets that enable the full technological leap when innovations succeed.
The Stakes
"Economic growth cannot be taken for granted," said John Hassler, chair of the Nobel committee. "We must uphold the mechanisms that underlie creative destruction, so that we do not fall back into stagnation."
For most of human history, stagnation was indeed the norm. The laureates' work reveals that growth requires a delicate institutional ecosystem—one that can be disrupted or lost. Their research demonstrates that innovation can be encouraged, but only through policies calibrated to specific contexts, recognizing complex trade-offs, and accepting that sustained growth requires sustained political commitment to creative destruction's disruptive power.
Mokyr plans to continue researching despite approaching his 80th birthday this summer. "This is the type of job that I dreamed about my entire life," he said before hanging up to walk his dog in Chicago.
The prize—11 million Swedish kronor, or roughly $1.2 million—will be split, with half going to Mokyr and half shared between Aghion and Howitt. They'll receive their awards December 10 in Stockholm, alongside this year's other Nobel laureates.
Since its establishment by Sweden's central bank in 1968, the economics prize has been awarded to 99 individuals. Only three have been women.
Sources
Primary Source: Manenkov, Kostya, Mike Corder, Paul Wiseman, Christopher Rugaber, and David McHugh. "Nobel economics prize goes to 3 researchers for explaining innovation-driven economic growth." San Diego Union-Tribune (Associated Press), October 13, 2025.
Nobel Prize Official Materials: The Royal Swedish Academy of Sciences. "The Prize in Economic Sciences 2025." Press release and scientific background, October 13, 2025.
Key Works by the Laureates: Aghion, Philippe, and Peter Howitt. "A Model of Growth Through Creative Destruction." Econometrica 60, no. 2 (1992): 323-351.
Mokyr, Joel. A Culture of Growth: The Origins of the Modern Economy. Princeton, NJ: Princeton University Press, 2016.
Mokyr, Joel. "The Intellectual Origins of Modern Economic Growth." Journal of Economic History 65, no. 2 (2005): 285-351.
Additional works and references available in original source documentation.
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