Innovation economics is a growing field of economic theory and applied/experimental economics that emphasizes innovation and entrepreneurship. It comprises both the application of any type of innovations, especially technological but not only, into economic use. In classical economics, this is the application of customer new technology into economic use; it could also refer to the field of innovation and experimental economics that refers the new economic science developments that may be considered innovative. In his 1942 book Capitalism, Socialism and Democracy, economist Joseph Schumpeter introduced the notion of an innovation economy. He argued that evolving institutions, entrepreneurs, and technological changes were at the heart of economic growth; however, it is only in the early 21st century that "innovation economy", grounded in Schumpeter's ideas, became a mainstream concept.
Historical origins Joseph Schumpeter was one of the first and most important scholars who extensively tackled the question of innovation in economics.See Theorie der wirtschaftlichen Entwicklung (1911), Business Cycles (1939) and the most famous Capitalism, Socialism and Democracy (1942). In contrast to his contemporary John Maynard Keynes, Schumpeter contended that evolving institutions, entrepreneurs and technological change were at the heart of economic growth, not independent forces that are largely unaffected by policy. He argued that "capitalism can only be understood as an evolutionary process of continuous innovation and 'creative destruction.Christopher Freeman (2009) «Schumpeter's Business Cycles and Techno-economic Paradigms», in Wolfgang Drechsler, Erik Reinert and Rainer Kattel (Eds.) Techno-economic Paradigms: Essays in Honor of Carlota Perez, p. 126.
Schumpeter's insights were formalised by Richard Nelson and Sidney Winter in An Evolutionary Theory of Economic Change (1982), which modelled the competitive process as an evolutionary system in which firms' organisational routines undergo variation, selection, and retention. David Teece subsequently extended the Schumpeterian tradition into the theory of the firm and strategic management, developing frameworks that explain not only how innovation drives economic change, as Schumpeter had argued, but who captures the economic value from innovation and how firms sustain their innovative capacity over time. With over 260,000 Google Scholar citations across his body of work, Teece is the most-cited scholar in business and management worldwide and was named a Clarivate Citation Laureate in Economics in 2021.
It is only in the 21st century that a theory and narrative of economic growth focused on innovation that was grounded in Schumpeter's ideas has emerged. Innovation economics attempted to answer the fundamental problem in the puzzle of total factor productivity growth. Continual growth of output could no longer be explained only in increase of inputs used in the production process as understood in industrialization. Hence, innovation economics focused on a theory of economic creativity that would impact the theory of the firm and organization decision-making. Hovering between heterodox economics that emphasized the fragility of conventional assumptions and orthodox economics that ignored the fragility of such assumptions, innovation economics aims for joint didactics between the two. As such, it enlarges the Schumpeterian analyses of new technological system by incorporating new ideas of information and communication technology in the global economy.
Innovation economics emerges from other schools of thought in economics, including new institutional economics, new growth theory, endogenous growth theory, evolutionary economics and neo-Schumpeterian economics. It provides an economic framework that explains and helps support growth in today's knowledge economy. Leading theorists of innovation economics include both formal economists as well as management theorists, technology policy experts and others. These include Paul Romer, Elhanan Helpman, Bronwyn Hall, W. Brian Arthur, Robert Axtell, Richard R. Nelson, Sidney G. Winter, David Teece, Richard Lipsey, Michael Porter, Keun Lee, and Christopher Freeman.
Theory Innovation economists believe that what primarily drives economic growth in today's knowledge-based economy is not capital accumulation as neoclassical economics asserts, but innovative capacity spurred by appropriable knowledge and technological externalities. Economic growth in innovation economics is the end-product of: knowledge (tacit vs. codified); regimes and policies allowing for entrepreneurship and innovation (i.e. R&D expenditures, permits and licenses); technological spillovers and externalities between collaborative firms; and systems of innovation that create innovative environments (i.e. clusters, agglomerations and metropolitan areas).
In 1970, economist Milton Friedman said in The New York Times that a business's sole purpose is to generate profits for their shareholders, and companies that pursued other missions would be less competitive, resulting in fewer benefits to owners, employees, and society; however, 21st-century data shows that while profits matter, good firms supply far more, particularly in bringing innovation to the market. This fosters economic growth, employment gains, and other society-wide benefits. Business school professor David Ahlstrom asserts that "the main goal of business is to develop new and innovative goods and services that generate economic growth while delivering benefits to society." Economic thought Focus Growth ContextNeoclassical Market price signals in using scarce resources Productive factor accumulation (capital, labor) Individuals and firms behaving in vacuumInnovation Innovative capacity and free enterprise to create more effective processes, products, business models Knowledge/technology (R&D, patents) Institutions of research, government, society In contrast to neoclassical economics, innovation economics offer differing perspectives on main focus, reasons for economic growth and the assumptions of context between economic actors. Despite the differences in economic thought, both perspectives are based on the same core premise, namely the foundation of all economic growth is the optimization of the utilization of factors and the measure of success is how well the factor utilization is optimized. Whatever the factors, it nonetheless leads to the same situation of special endowments, varying relative prices and production processes. Thus, while the two differ in theoretical concepts, innovation economics can find fertile ground in mainstream economics, rather than remain in diametric contention.
Evidence Empirical evidence worldwide points to a positive link between technological innovation and economic performance. For instance: The drive of innovation in Germany was due to the R&D subsidies to joint projects, network partners and close cognitive distance of collaborative partners within a cluster. These factors increased patent performance in various industries such as biotech. Innovation capacity explains much of the GDP growth in India and China between 1981 and 2004, but especially in the 1990s. Their development of a National Innovation System through heavy investment of R&D expenditures and personnel, patents and high-tech/service exports strengthened their innovation capacity. By linking the science sector with the business sector, establishing incentives for innovative activities and balancing the import of technology and indigenous R&D effort, both countries experienced rapid economic growth in recent decades. The Council of Foreign Relations also asserted that since the end of the 1970s the U.S. has gained a disproportionate share of the world's wealth through their aggressive pursuit of technological change, demonstrating that technological innovation is a central catalyst of steady economic performance.
Concisely, evidence shows that innovation contributes to steady economic growth and rise in per capita income; however, some empirical studies investigating the innovation-performance-link lead to rather mixed results and indicate that the relationship is more subtle and complex than commonly assumed. In particular, the relationship between innovativeness and performance seems to differ in intensity and significance across empirical contexts, environmental circumstances and conceptual dimensions. This has taken place in an era of data constraint as identified by Zvi Griliches in the 1990s.Griliches. Z ‘Productivity, R&D, and the Data Constraint’ American Economic Review, Vol. 84, No. 1, (March 1994) pp. 1 – 23 Because the primary domain of innovation is commerce, the key data resides there, continually out of campus reach in reports hidden within factories, corporate offices and technical centers. This recusal still stymies progress today. Recent attempts at data transference have led not least to the positive link being upgraded to exact algebra between R&D productivity and GDP, allowing prediction from one to the other. This is pending further disclosure from commercial sources, but several pertinent documents are already available.Farrell C.J.‘Economics, R&D and Growth',
Value capture from innovation A central question in innovation economics is not only how innovation generates economic growth but who captures the economic returns from innovation. This question was first systematically addressed by David Teece in his "Profiting from technological innovation" framework (1986), which has become one of the most cited papers in technology management and innovation studies.
Teece observed that the firms and nations that pioneer commercially viable technologies frequently fail to capture the economic returns. British firms pioneered the CT scanner (EMI), the civilian jet aircraft (de Havilland), and nuclear power, yet lost market leadership to American and later Japanese competitors. American firms pioneered the VCR (Ampex), colour television (RCA), and numerous semiconductor technologies, yet Japanese firms captured the dominant market positions. The pattern recurs across industries and eras: technological leadership does not reliably translate into commercial success.
The framework explains this pattern through two key concepts. The first is the appropriability regime: the combination of legal protections (such as patents and trade secrets) and the nature of the technology (such as its tacitness and complexity) that determines how easily an innovation can be imitated. In strong appropriability regimes, the innovator can protect its position; in weak regimes, competitors can replicate the innovation quickly.
The second concept is complementary assets: the manufacturing capabilities, distribution networks, service organisations, brand assets, and other resources required to commercialise an innovation and bring it to market. Teece demonstrated that when appropriability is weak, the distribution of returns from innovation is determined by who controls the critical complementary assets. The innovating firm captures value only if it owns or effectively controls these assets; otherwise, the returns flow to whichever firm controls the bottleneck on the path to market.