Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of U.S. state-level patenting
By Carolina Castaldi (email@example.com), School of Innovation Sciences, Eindhoven University of Technology
Koen Frenken, (firstname.lastname@example.org) School of Innovation Sciences, Eindhoven University of Technology
Bart Los, (email@example.com), Groningen Growth and Development Centre
We investigate how variety affects the innovation output of a region. Borrowing arguments from theories of recombinant innovation, we expect that related variety will enhance innovation as related technologies are more easily recombined into a new technology. However, we also expect that unrelated variety enhances technological breakthroughs, since radical innovation often stems from connecting previously unrelated technologies opening up whole new functionalities and applications. Using patent data for US states in the period 1977-1999 and associated citation data, we find evidence for both hypotheses. Our study thus sheds a new and critical light on the related-variety hypothesis in economic geography.
Review by Anna Missiaia
This paper by Carolina Castaldi, Koen Frenken and Bart Los was distributed by NEP-HIS on 30-03-2013. The paper is not, strictly speaking, an economic or business history paper. However, it provides some very interesting insights on how technological innovation and technological breakthroughs happen. This is a large and expanding field in economic history and on-going research on the economics of innovation, I believe, can be of interest to many of our readers.
The paper is concerned with the study of how innovation in a region is affected by the connections within its sectors in terms of shared technological competences. The term “variety” conveys this concept. The authors differentiate in two types of variety: related and unrelated variety. The former describes the connection among sectors that are complementary in terms on competences and can easily exchange technological knowledge. Unrelated variety, on the other hand, steams from sectors that do not appear to have complementary technology.
These two different types of variety are useful to distinguish for their effects on innovation. Related variety supports productivity and employment growth at regional level. However, unrelated variety is the one that causes technological breakthroughs, as it brings a completely new type of technology into a sector. In a subsequent stage, unrelated variety becomes related, being absorbed by the new sector.
The paper keeps these two types of variety separate and tests for their effects. The authors use patent data for US states in the period 1977-1999. The methodology implies regressing the number of patents as a proxy for innovation, on measures of related variety, unrelated variety, research and development investment, time trend and state fixed effects. Variety is measured by looking at the dispersion of the classification of patents within and between technological classes of the patents. The paper also proposes two different regressions, one using the total number of patents as dependent variable and one using the share of superstar patents, which represent patents that lead to breakthrough technologies. Superstar patents are distinguished from “regular” patents according to the distribution of their citations: superstar patents have a fat tail, meaning that they are cited more in later stages of their development compared to regular patents.
A nice contribution of this paper is to measure super patents through their statistical distribution of their citations instead of relying on superimposed criteria such as being on the top 1% or 5% of the citations. The idea here is to distinguish between general innovation (regular patents) and breakthrough innovation (superstar patents). Theory predicts that regular patents will be positively affected by related variety, producing general innovation, while superstar patents will be positively correlated with unrelated variety, producing breakthrough innovation. The empirical analysis nicely confirms the theory.
The possible shortcomings of the paper are related to the role of geography in the analysis. The sample is at US state level and the underlying implication is that variety in the state affects the number of patents registered in it. There could be, under this assumptions, some issues of spatial dependence. The authors touch upon this point in two parts of the paper: in the methodology section they explain that superstar patents tend to cluster in fewer states that general patents and this pattern requires a different approach for the two types of patents. It would be useful if this issue could be elaborated further by the authors in a future version of the paper.
As for the possible spatial dependence effect among explanatory variables, the authors try to control for the fact that R&D in one state could affect the patent output of neighboring states as well. They construct an adjacency matrix to capture the effect of the R&D effort of neighboring states.
The conclusion is that the analysis is robust to spatial dependence. In spite of this robustness check for spatial dependence, some concerns remain. Restricting the R&D effect only to neighboring states could be a limit, as the effect could not only go through physical proximity, but also through other types of connections: for example, the same firm could have different branches in different non-adjacent states, leading to an influence not captured by the adjacency matrix.
In short, this paper provides a very interesting insight on how two types of innovations can arise as measured by patent citations at regional level. The results are consistent with the theory and could be useful to future research in historical perspective. A further improvement of the paper could be to conduct more robustness check on the geographical aspects of these results, especially expanding them to non-adjacent states.