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Ripples in the River of Knowledge

Technologies directly dependent on science are atypical, but they may have indirect impacts on most technologies

Published onApr 13, 2021
Ripples in the River of Knowledge
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In More Science Leads to More Innovation, we looked at four natural experiments where the “supply” of science was increased or decreased differently across scientific fields. When the supply of science increased, we saw more downstream technological innovation, and when the supply of science decreased, less. Here, I’ll argue those studies underestimate the influence of science on innovation.

Direct dependence on science is uncommon

To begin, while it’s true that more science leads to more innovation, the majority of technological innovations probably do not directly depend on recent science:

  • Citing academic articles in patents has become more common over time, but even in 2018, 74% of new patents did not cite any scientific journal articles.

  • In a survey from the 1990s, European inventors rated the importance of knowledge from scientific literature for developing innovations at 2.5 out of 5 (lower than they rated the importance of knowledge from customers/users and the patent literature). They rated the importance of universities and public research laboratories at just 1.4 out of 5.

  • In a 1994 survey, US R&D managers estimated only 20% of R&D projects relied on public research.

Instead, the dependence on science is unevenly distributed. The figure below illustrates the average number of citations to science per patent, by technical classification. Patents in chemistry/metallurgy, and human necessities (which include the biomedical and pharma sectors) cite science much more intensively than other fields. Fields like mechanical engineering barely cite the scientific literature at all.

Average Citations to Science per Patent by technical classification and grant year (Marx and Fuegi 2020)

This is broadly consistent with a 1994 survey of corporate R&D managers that found R&D projects in automobiles, general manufacturing, and electrical equipment relied on public research significantly less than in fields like biotechnology and pharmaceuticals.

Indirect Dependence on Science?

But just because an invention doesn’t rely directly on science doesn’t mean that science plays no role in it. Scientific knowledge and principles can become embodied in technologies that other technologies, in turn, use as components. For example, chemistry and metallurgy heavily cite the scientific literature. It may be that new chemistry production processes (which are patented) allow for the manufacture of new kinds of composites, that in turn allow for, say, the creation of new, more powerful engines. But then the patents for the composites and the engines may not cite science and the inventors of these things might not report any dependence on science, even though without the production processes enabled by the science they would be out of luck.

There is actually a way to measure this “distance from science” that we’ve discussed before. In the example above, although a new type of composite might not cite any scientific articles itself, it might cite the patents for the new chemistry production processes that do. And the engine patent might cite the composite patent, which in turn cites science-based production processes. Ahmadpoor and Jones (2017) use this basic idea to measure the “distance” from science of US patents by counting the smallest number of citation steps between a patent and a scientific article. A patent that cites a scientific article has a distance of 1. A patent that cites no science itself, but does cite a patent that cites a scientific article has a distance of 2. And so on. In Ahmadpoor and Jones’ sample of patents from 1976 to 2013, although only 16% of patents directly cite a scientific article, 61% of patents are “connected” to science via some kind of chain of citation (most often, a distance of 3).

There is some indirect evidence that this measure of distance from science is capturing something real. Patents that are “closer” to science as measured in this way have some of the characteristics of patents that directly cite science. For example, as discussed in other articles, patents that cite scientific articles are more valuable than those that do not, and also more likely to be traded. But it’s also true that, looking at patents that don’t directly cite science, those that are closer to science are still more valuable and more likely to be traded than those that are farther from science.

Most of the natural experiments discussed in More Science leads to More Innovation pertain to patents with a distance of 1 (the closest to science). Indeed, half of them explicitly measure the link between science and technology via a citation from a patent to a journal article (which means, by definition, they have a distance of 1). But if there is a knock-on effect for patents further from science (distance 2 or greater), they probably miss it.

Upstream = Closer to Science?

Another strand of literature gives us some good reasons to think there are significant knock-on effects. US patents are classified as primarily belonging to one of several hundred different technology classifications (examples range from “Class 012: Boot and shoe making” to “Class 706: Data processing – artificial intelligence”). As discussed in Upstream Patenting Predicts Downstream Patenting, when patents in one technology class frequently cite another class, we say the cited class lies “upstream” of the citing class. As the article title says, it turns out that upstream patenting predicts downstream patenting. In other words, a surge of patents in some class x will tend to be followed by a surge of patents in some class y, if class y patents historically cite class x ones. (Note though, this line of work is about prediction not causation; while it is consistent with a flurry of innovation causing more innovation downstream - which seems a sensible thing to believe - it could be the historical link is caused by some other factor)

So we have two related but different ways of measuring indirect knowledge flows among patented technologies. Some papers have measured the distance from science, via the shortest citation chain to a scientific paper. Others have defined upstream and downstream relationships among technologies based on the total share of citations that flow from one technology class to another. A natural question is the extent to which the two line up. That can tell us something about how science indirectly impacts technology.

For example, we know that science tends to lead to more innovation in technology classes that directly depend on science. Are these technology classes, in turn, directly upstream of many other classes? If so, the results discussed in “upstream patenting predicts downstream patenting” would predict an increase in science-based innovation would lead to a second round of innovation in the technologies that lie immediately downstream. And if these classes are themselves upstream of many other classes, there would be a second reverberation, and so on.

In the figure below, I computed the average distance to science for US patents over 1976-2018, based on the Ahmadpoor and Jones method and using Marx and Fuegi’s dataset. On the horizontal axis, we have the average distance to science of the patents belonging to each of 307 different technology classes. Since I’m interested here in the indirect impact of science on technology, I limited my attention to technology classes with distance of 2 or greater: that is, technologies that do not typically cite scientific papers directly. Classes to the left are closer to science, classes to the right are farther away. On the vertical axis, we have the average distance to science of their upstream technology classes, weighted by citation share. The lower the dot, the closer to science are the classes cited.

Average distance to science for technology classes (horizontal axis) and weighted average of upstream technology classes (vertical axis). Author calculations.

Let’s look at an example. In the upper right corner, we have a red dot corresponding to Class 81: Tools. On the horizontal axis, we see the average patent in this class has a distance to technology of 4.5. That means the shortest distance to science for tool patents often involves citing a patent that cites a patent that cites a patent that cites a patent that cites a science article. On the vertical axis, we see the typical distance to science for technology classes that are heavily cited by tools patent is just 3.6. These upstream classes include classes like Class 29: Metal working (average distance to science 3.0) and Class 30: cutlery (average distance to science 4.1). The main take-away is that classes directly upstream of tool patents tend to be closer to science than tool patents.

The black line cutting through the middle of this figure demarcates the split between technologies that mostly cite technologies closer to science (dots below the line) and those that mostly cite technologies farther from science (dots above the line). For the classes displayed, 78% of them lie below this line – that is, the technologies that lie upstream also tend to lie closer to science. This difference isn’t uniform though. Looking only at technologies far from science, with a distance of 3 or greater, 95% lie downstream of technologies closer to science then they are! But looking at classes in the 2-3 interval, it’s basically 50/50. Essentially, that means technologies that are 1-2 citation steps removed from science largely cite each other; they don’t primarily build on technologies that are closer to science. But technologies further out do.

Indirect Impact of Science

So where do we stand? In More Science leads to More Innovation, we looked at pretty compelling evidence that increasing the supply of science tends to lead to more innovation. But that direct effect is concentrated in a relatively small share of technologies; only a quarter of patents directly cite scientific work. However, any innovation has spillover effects. As discussed elsewhere, the magnitude of the unintended benefits of science tend to be at least comparable to the intended benefits. In this post we focused on specific kind of spillover: an increase in innovation tends to lead to further innovation in “downstream” technologies (which we can identify based on citation patterns).

We have no reason to think that spillover effect wouldn’t hold if innovation increased because of science. Any field that sees an increase in innovation due to science will probably have some downstream fields that will also benefit. Initially, these downstream fields might be relatively “close” to science themselves, so they might also directly benefit from an increased supply of science. But eventually, the passing of technological concepts and improved components from upstream to downstream becomes a channel through which the fruits of science might also measurably flow.

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Cited in the above

More science leads to more innovation

Upstream patenting predicts downstream patenting

Science is good at making useful knowledge

How long does it take to go from science to technology

Cites the above

How to accelerate technological progress


Articles cited:

Marx, Matt, and Aaron Fuegi. 2020. Reliance on science: Worldwide front-page patent citations to scientific articles. Strategic Managements Journal 41(9): 1572-1594. https://doi.org/10.1002/smj.3145

Harhoff, Dietmar and Mariani, Myriam and Giuri, Paola and Brusoni, Stefano and Crespi, Gustavo and Francoz, Dominique and Gambardella, Alfonso and Garcia-Fontes, Walter and Geuna, Aldo and Gonzales, Raul and Hoisl, Karin and Le Bas, Christian and Luzzi, Alessandra and Magazzini, Laura and Nesta, Lionel and Nomaler, Önder and Palomeras, Neus and Patel, Parimal and Romanelli, Marzia and Verspagen, Bart. 2006. Everything You Always Wanted to Know About Inventors (But Never Asked): Evidence from the Patval-Eu Survey. CEPR Discussion Paper No. 5752: https://ssrn.com/abstract=924898

Roach, Michael, and Wesley M. Cohen. 2013. Lens or Prism? Patent Citations as a Measure of Knowledge Flows from Public Research. Management Science 59(2): 504-525. https://doi.org/10.1287/mnsc.1120.1644

Ahmadpoor, Mohammad, and Benjamin F. Jones. 2017. The Dual Frontier: Patented inventions and prior scientific advance. Science357(6351): 583-587. https://doi.org/10.1126/science.aam9527

Ashish, Arora, Sharon Belenzon, and Jungkyu Suh. 2021. Science and the Market for Technology. NBER Working Paper 28534. https://doi.org/10.3386/w28534

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