Endogenous Technological Change

Modern technological systems are “complex, interconnected, automated, and opaque.” So much so that, prior to 1990, the standard model of economic growth treated technology as an exogenous factor, that is, an external force that affects the economy but is not influenced by economic processes and thus not explainable by the model. In 1990, though, Paul Romer published a paper entitled Endogenous Technological Change that explained the production of new ideas (technology) as a product of economic activities and decisions. Romer described long-run economic growth as being built off of five properties:
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1. The accumulation of ideas is the source of long-run economic growth.
2. Ideas are non-rival.
3. A larger stock of ideas makes it easier to find new ideas.
4. Ideas are created in a costly but purposeful activity.
5. Ideas can be owned and the owner can sell the rights to use the ideas at a market price.
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In particular, Romer emphasized that:

“ideas”, though produced with capital and labor inputs, are different than ordinary goods and services along two dimensions: the extent to which they are rivalrous—whether they can be used by more than one actor at once—and excludable—how easy it is to prevent others from using them. Romer emphasized that ideas are non-rivalrous and, to a varying degree, excludable…

Romer also asserted that ideas go hand in hand with increasing returns to scale. They involve initially high costs, e.g., significant work for producing the blueprint (first copy) of a new product, but a more typical cost structure of (approximately) constant returns to scale for producing further copies. Hence, the overall production function is convex with falling marginal costs and one must therefore consider a departure from perfect competition. [Source: Scientific Background on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2018. Economic Growth, Technological Change, And Climate Change.]

Romer’s focus on idea production and the causes of technological change is now firmly established as a core area of economic analysis, but much is left to understand about questions such as how different kinds of research are guided by market forces, how technological change comes about in different settings (i.e., commercial markets versus universities), and how much regulation may be required to direct market-based R&D toward the development of ideas that are beneficial to the general welfare of society.

Many questions that are often lumped into a broad category of “innovation” can, following Romer, be more rigorously thought about as questions of endogenous technological change:
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1. How do a society’s problem-solving abilities change over time? How are these abilities impacted by internal politics, economic change, social change, and geopolitical competition?
2. What is the optimal design of institutions to support step function technological change? Are these designs different depending on whether the organization is profit-driven or mission-driven?
3. Does an aging society have a different perception of or commitment to the value of technological change?
4. Does a top-down innovation strategy work over the long-term?
5. Physicist Geoffrey West has noted, “we are living in an age where the time between innovations needs to be shorter than the average human lifespan. We are all going to live through multiple cycles of incredible innovation.” Is this pace sustainable? What does this mean for society?
6. Open innovation: what is it? does it make sense?
7. What is the best way to integrate the widely disparate time frames of technological change from technology development (nano) to rule development (glacial)?
   

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Below is a short bibliography containing a variety of materials related to the theme of technological change. Digitalis Research, the applied research arm of the Digitalis Group, is interested in hearing from you at info@digitalisresearch.com if this is an area of interest and possible collaboration.

Data Visualizations
Calculating Empires. Kate Crawford and Vladan Joler. 2023.
A Genealogy of Technology and Power Since 1500

Anatomy of an AI System. Kate Crawford and Vladan Joler. 2018.
The Amazon Echo as an anatomical map of human labor, data and planetary resources.

Reading List
Economics of Innovation Detailed Reading List. Matt S. Clancy. 2019
Reading list for an undergraduate course on the economics of innovation.

Books
Where Good Ideas Come From. Steven Johnson. Riverhead Books. 2010.
The natural history of innovation.

How Experiments End. Peter L. Galison. University of Chicago Press. 1987.
Offers insights into the ways in which experiment and theory interact.

The Idea Factory: Bell Labs and the Great Age of American Innovation. Jon Gertner. Penguin. 2012.
“The definitive history of America’s greatest incubator of innovation and the birthplace of some of the 20th century’s most influential technologies.”

Papers
Endogenous Technological Change. Paul M. Romer. Journal of Political Economy. 1990.
“The main conclusions are that the stock of human capital determines the rate of growth, that too little human capital is devoted to research in equilibrium, that integration into world markets will increase growth rates, and that having a large population is not sufficient to generate growth.”

Toward an Entrepreneurial Society: Why Measurement Matters. Carl J. Schramm. innovations. Winter 2008.
“Given the importance of innovation to individuals and societies everywhere, the global inadequacy of tools—even a rigorous vocabulary—to measure innovation and trace its effects is striking.”

Organization Design for Distributed Innovation. Carliss Y. Baldwin. Harvard Business School Working Paper 12-100. 2012.
“In the future, … the key problem for organization design will be the management of distributed innovation in … dynamic systems.”

Materialistic Genius and Market Power: Uncovering the best innovations. Jean Tirole & Glen Weyl. Institut d'Économie Industrielle Working Papers 629. 2010.
“What is the best way to reward innovation?”
   
Closed or open innovation? Problem solving and the governance choice. Teppo Felin & Todd R. Zenger. Research Policy. 2014.
“In this paper, we treat open innovation—in it's different forms and manifestations—as well as internal or closed innovation, as unique governance forms with different benefits and costs.”

The emergence and diffusion of DNA microarray technology. Tim Lenoir & Eric Giannella. Journal of Biomedical Discovery and Collaboration. 2006.
“The network model of innovation widely adopted among researchers in the economics of science and technology posits relatively porous boundaries between firms and academic research programs and a bi-directional flow of inventions, personnel, and tacit knowledge between sites of university and industry innovation.”

Articles
Is Science Mostly Driven by Ideas or by Tools?. Freeman J. Dyson. Science. 2012.
“Is science driven by ideas or tools?”

Slow Ideas. Atul Gawande. The New Yorker. 2013.
“Why do some innovations spread so swiftly and others so slowly?


– Geoffrey W. Smith

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