Technologies tend to be hierarchically composed of many sub-technologies, and to build on each other in ways that are relatively stable and predictable over multiple years. This means there is some degree of predictability about technological trends. If there is a flurry of breakthroughs in an upstream technology, downstream technologies that use it as an important component, or which adapt its principles and uses for new contexts, are likely to see a flurry of breakthroughs in subsequent years. (Think of how we might be reasonably confident that all sorts of new and improved engine designs will be enabled by a new and improved composite)
There are ways to observe this hierarchy and use these relationships to make predictions. US patents are classified as primarily belonging to one of several hundred technology classifications (examples range from “Class 012: Boot and shoe making” to “Class 706: Data processing – artificial intelligence”). The hierarchical relationship between these technology classes can be observed in the citations of the patents belonging to these classes. Acemoglu, Akcigit, and Kerr (2016) build a directed network between different technology classes, where the strength of a link between two classes is given by the probability a patent in one cites the other.
Acemoglu, Akcigit, and Kerr show a statistically significant relationship between patent activity in upstream classes and the patenting of downstream classes (that is, the ones that historically cite this class heavily). Pichler, Lafond, and Farmer (2020) perform a similar exercise. In the figure below, they plot the correlation between the growth rate of patents in a given class, and the growth rate of the weighted average growth rate of upstream technology classes.
It turns out these correlations are robust enough to be used for forecasting. In one application, Acemoglu, Akcigit and Kerr use data from 1975 to 1994 to fit their statistical model, and then they use it to predict the number of patents in the following ten years. After adjusting for the influence of technology classification (some classes always patent more than others) and time (in most classes there tend to be more patent applications per year), they find a 10% increase in predicted patenting (based on the growth of patenting in upstream classes) is associated with an actual out-of-sample 3-4% increase in patenting.
Pichler, Lafond, and Farmer predict the growth rate of patenting as a function of upstream patenting activity using methods derived from machine learning. They fit a number of alternative models based on data from 1945-1987 to model the correlation between the growth rate of patenting in each technology class and prior patenting growth in upstream technologies. They then choose the model that makes the best forecast for 1988-2002. Finally, they use all the data from 1945-2002 to refit this model and predict out-of-sample patent growth rates, in each class, over 2003-2017.
How well does it do? To understand it’s performance, they need a benchmark. They replicate the whole process above, but for models that exclude data on upstream patenting. Instead, the benchmark predicts patenting in class x by the historical patenting activity of just class x (is patenting in this class rising or falling over time? Does it tend to move in booms and busts? And so on). Again, they find the model that best predicts out-of-sample from 1988-2002, re-estimate is with data up to 2002, and then forecast out-of-sample through 2017.
The results are in the figure below, with the relative performance of the models using upstream patent data in blue (the green is a model not discussed in this post). At it’s peak, models using data on upstream patenting gain nearly 40% in predictability relative to a benchmark.
It all boils down to this: historical patent citations allow us to identify the technology classes that lie “upstream” of any other class; and upstream patenting predicts downstream patenting in the future, out of sample, in two different papers.
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Acemoglu, Daron, Ufuk Akcigit, and William R. Kerr. 2016. Innovation Network. Proceedings of the National Academy of Sciences Oct 2016, 113 (41) 11483-11488; DOI: 10.1073/pnas.1613559113
Pichler, Anton, François Lafond, and J Doyne Farmer. 2020. Technological interdependencies predict innovation dynamics. arXiv:2003.00580