In the world of economics, two giants have shaped our understanding of growth: Joseph Schumpeter and John Maynard Keynes. For Schumpeter, the engine of capitalism is innovation—a process of “creative destruction” on the supply side that never allows the economy to settle. For Keynes, the focus was on demand; the fluctuations and “animal spirits” of investors dictate the economy’s pace in the short and medium term.
Traditionally, these two visions have seemed irreconcilable. What truly drives long-term growth? Is it the ability of firms to innovate or the capacity of consumers to buy? A new study by Önder Nomaler, Danilo Spinola, and Bart Verspagen, published by Cambridge University Press, proposes a bold synthesis through an evolutionary macroeconomic model. This model demonstrates that not only is it possible to unite them, but their interaction is the key to understanding sustainable growth.
The study breaks down the findings of their model—a simulated economic universe where firms compete, innovate, and sometimes fail—revealing how an economy can generate endogenous growth without the need for government policies to stabilize it.
Key conclusions
- Innovation is the engine of growth: The model shows that investment in R&D, which leads to productivity increases, is the fundamental force behind long-term economic growth.
- Demand adapts, it is not created: Unlike other models, here consumer demand adjusts endogenously to productivity growth, primarily through “consumption smoothing,” thus avoiding mass unemployment without government intervention.
- Corporate natural selection chooses the winning strategy: The model uses firm bankruptcy as an evolutionary selection mechanism. Non-viable R&D strategies are eliminated, allowing a stable and dominant innovation investment strategy to emerge for the economy as a whole.
- The financial environment is key: Financial conditions, such as the level of debt a firm can sustain before its bankruptcy risk increases, are crucial in determining the level of R&D investment the economy can support.
A Living Economic Model: Agents, Rules, and Evolution
To merge Keynes and Schumpeter, the researchers built a complex yet elegant macroeconomic model, known as a stock-flow consistent (SFC) agent-based model (ABM). Imagine a digital ecosystem with three types of actors:
- Firms: They produce goods, invest in capital, and, crucially, allocate a portion of their revenue to Research and Development (R&D). Each firm is born with a fixed “R&D strategy”—a percentage of its sales it will invest in innovation.
- Households: They supply labor, consume products, and save. Their consumption decisions depend not only on their current income but also on their accumulated wealth.
- Government: It plays a passive role, mainly issuing bonds and collecting taxes to cover its interest payments, without actively intervening to stabilize the economy.
The model simulates the economy in periods (similar to quarters), where thousands of transactions between these agents bring the Keynesian multiplier to life. Every purchase (whether by a household or a firm for investment) generates production, which in turn generates wages, and a portion of those wages becomes new consumption. It is a cycle that creates a circular flow of income.
How is Success Measured? Selection Through Bankruptcy
This is where Schumpeter’s genius enters with a Darwinian twist. The model does not assume that firms act rationally and optimally. Instead, firms either survive or die. The primary selection mechanism is bankruptcy.
A firm accumulates debt to finance its investments and R&D. If its leverage ratio (debt to capital) exceeds a critical threshold, its probability of going bankrupt increases dramatically. When a firm fails, it is replaced by a new one that imitates the R&D strategy of a successful firm with a low probability of bankruptcy.
This process of variety (occasional random mutations in strategies) and selection (bankruptcy and imitation) is purely evolutionary. The central question the study addresses is: can this chaotic, decentralized system self-organize to find an evolutionarily stable R&D strategy that generates long-term growth?
The Emergence of Intelligent Growth
Through Monte Carlo simulations, the researchers explored the model’s behavior under different conditions. The results are fascinating and offer a new perspective on economic dynamics.
The Economy “Chooses” an Optimal R&D Strategy
The most important finding is that, starting from a wide diversity of R&D strategies (from firms that invest nothing to those that invest aggressively), the system converges. Over time, natural selection eliminates inefficient strategies, and a dominant, relatively homogeneous R&D strategy emerges across the entire population of firms.
- If R&D is too low: Firms fail to improve their productivity. Their labor costs (wages) rise in line with the market average, causing them to lose competitiveness and leading to bankruptcy.
- If R&D is too high: The costs of innovation outweigh the productivity benefits, which increases the firm’s debt and, consequently, its risk of bankruptcy.
The system finds a “sweet spot”—an investment strategy that balances the costs of innovation with productivity gains, creating a stable, endogenous growth engine.
Demand Follows Innovation (Not the Other Way Around)
A crucial aspect of the model is how it avoids “technological unemployment.” Innovation increases labor productivity, meaning less labor is needed to produce the same output. How is an unemployment crisis averted?
The answer lies in consumption smoothing by households. In this model, households adjust the fraction of their accumulated wealth they spend based on the employment rate. If unemployment rises due to a productivity leap, households draw on their savings to maintain their consumption level, increasing autonomous demand. This stimulates production and stabilizes employment, creating an adjustment mechanism that allows demand to keep pace with productivity growth without fiscal stimulus.
Financial Conditions and Technological Opportunities Matter
The model does not produce a single outcome. The R&D level that proves to be “stable” depends critically on two factors:
- The financial selection environment: Parameters like the level of debt considered “safe” or the ease of accessing credit determine the harshness of the selection. A more lenient financial environment may allow less efficient R&D strategies to survive.
- Technological opportunities: The ease with which R&D investment translates into productivity gains (the “technological landscape”) is also key. If opportunities are low, selection will favor firms with little or no R&D investment, leading to economic stagnation.
Implications for Innovation and the Future
The model by Nomaler, Spinola, and Verspagen, though a simplification of reality, offers powerful lessons. It teaches us that economic growth is not a smooth equilibrium process but an emergent, evolutionary phenomenon driven by the constant interplay between supply-side innovation and demand-side adaptation.
For innovation leaders, this approach underscores that R&D strategies do not exist in a vacuum. Their success depends on the competitive and financial environment in which they operate. For economists and policymakers, it opens the door to thinking about the macroeconomy not as a machine to be fine-tuned, but as a complex ecosystem to be cultivated.
This work represents a significant step toward a deeper synthesis of Keynes’s and Schumpeter’s ideas, providing a solid foundation for future research that can incorporate more detailed financial systems and complex technological landscapes. Ultimately, it shows us that for an economy to thrive, innovation must be profitable, and demand must be flexible enough to absorb its fruits.
Contact
Bart Verspagen
Maastricht University
Email: b.verspagen@maastrichtuniversity.nl
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Reference (open access)
Nomaler Ö, Spinola D, Verspagen B. Evolutionary Selection and Keynes–Schumpeter Macroeconomics. Cambridge University Press; 2025.
Editor and founder of “Innovar o Morir” (‘Innovate or Die’). Milthon holds a Master’s degree in Science and Innovation Management from the Polytechnic University of Valencia, with postgraduate diplomas in Business Innovation (UPV) and Market-Oriented Innovation Management (UPCH-Universitat Leipzig). He has practical experience in innovation management, having led the Fisheries Innovation Unit of the National Program for Innovation in Fisheries and Aquaculture (PNIPA) and worked as a consultant on open innovation diagnostics and technology watch. He firmly believes in the power of innovation and creativity as drivers of change and development.
