The innovation network

The innovation network

Innovation is normally regarded as a cumulative process, with new technologies building on existing knowledge – but our understanding of how progress in a particular area is influenced by knowledge in other, ‘upstream’ areas is bound. Using US patent data, this column identifies a well balanced ‘innovation network’ that serves as a conduit for cumulative knowledge development. Technological advances in a single field can advance progress in multiple neighbouring fields, but could have a stronger influence on more closely related areas.


Technological and scientific progress is normally depicted as a cumulative process with new innovation building upon existing stocks of knowledge (Romer 1990, Aghion and Howitt 1992), and existing literature provides evidence supporting this theory of ‘standing on the shoulders of giants’. Recent prominent empirical for example Furman and Stern (2012), Bloom et al. (2013), Williams (2013) and Glasso and Schankerman (2014). These findings are a significant foundation for effective policy to aid and enable innovation and economic productivity.

Not surprisingly progress, our knowledge of how progress in a particular area is influenced by prior knowledge in other, ‘upstream’ areas is bound. Previous work has tended to depict the inputs to innovation in confirmed field to be either restricted to the last knowledge for the reason that field, or as an aggregate stock of ideas spanning all fields. In a recently available paper, we show that instead you will find a stable ‘innovation network’ that serves as a conduit because of this cumulative knowledge development process, where knowledge spillovers are neither completely local nor universal (Acemoglu et. al 2016). Instead, technological advances in a single field can advance progress in multiple neighbouring fields, but could have stronger influence on more closely-related areas.

Approach and empirical results

The standard style of endogenous growth and technological progress depicts the flow of new ideas in confirmed field as a function of the prevailing stock of ideas for the reason that field and the quantity of resources assigned to producing new ideas. We extend this framework to measure reliance on the prevailing knowledge stocks in other technological fields, aswell, via an innovation network. We represent this innovation network in a flexible form where each technology field could derive inputs from other technological areas. This may include cases where knowledge broadly spills over onto other fields and cases where in fact the cumulative process is confined to a narrow field. We further enable knowledge diffusion processes that adjust with age invention.

We apply this framework to the united states Patent and Trademark Office (USPTO) database, which documents technology classifications for every US invention, and citations to the last patents on which the existing work builds. We utilize the group of patents granted between 1975 and 1994 to create the innovation network matrix. Figure 1 shows a good example of the innovation network matrix made of patents from 1975-1984 and the citations they receive of their first a decade. This figure highlights the heterogeneity in technology flows – for instance, patents in ‘Computers: Peripherals’ have a tendency to pull more from ‘Computers: Communications’ compared to the reverse, while ‘Computers: Communications’ builds more on electrical and electronic subcategories. This network is fairly stable across different schedules.

Figure 1 . Citation matrix, 1975-1984

We test the potency of the innovation network but interacting this network structure, held constant at these initial levels, with future patenting by technology field to observe how well we are able to predict future ‘downstream’ innovation. We look for a quite strong relationship between your predicted and actual values of future patenting – a 10% upsurge in expected patenting is connected with an 8-9% upsurge in actual patenting, with the precise elasticity dependant on whether we include own-field spillovers or use an external network only (the latter having clearer identification of effects). Figure 2 shows a visual representation of the effect using 484 USPTO patent classes, color-coded by parent technological category.

Figure 2 . Network strength at the patent class level

Additional tests confirm this relationship holds in panel estimation formats and in a battery of robustness checks. For instance, we document that the data flow is asymmetric, with innovation in upstream technologies predicting future downstream patenting, however, not the reverse. Other interesting extensions document the extent of knowledge spillovers in the technology space. Whereas models sometimes take polar cases just like a common knowledge stock for all technologies or fields building only upon their own work, the reality lies in-between – technologies have a tendency to build upon a few upstream classes offering innovation stimulants. We look for a robust connection of innovation to the ten most significant upstream patent classes, which diminishes afterwards. This network heterogeneity indicates that knowledge development is neither global, in the sense that fields collectively share an aggregate pool of knowledge, nor local, in the sense that every field builds only upon itself.

Overall, our research finds that upstream technological developments play a significant and measurable role later on pace and direction of patenting, with an advance in a single field providing a basis for further innovation in related areas. We find proof a well balanced innovation network with strong predictive power for future patenting. An improved accounting because of this innovation network and its own asymmetric flows can help us model the cumulative procedure for scientific discovery in a sharper manner. A fascinating path for future research is to consider whether large leaps behave just as as the advances studied here. We also think that this approach could be pushed to consider regional and firm-level variation, that may further help us understand the causal impact of patenting on economic and business outcomes.


Acemoglu, A, U Akcigit and W Kerr (2016) “Innovation network”, Proceedings of the National Academy of Sciences, 113(4).

Aghion, P and P Howitt (1992) “A style of growth through creative destruction”, Econometrica, 60(2): 323–351.

Bloom, N, M Schankerman and J Van Reenen (2013) “Identifying technology spillovers and product market rivalry”, Econometrica, 81(4): 1347–1393.

Glasso, A and M Schankerman (2014) “Patents and cumulative innovation: Causal evidence from the courts”, NBER Working Paper No 20269.

Furman, J and S Stern (2011) “Climbing atop the shoulders of giants: The impact of institutions on cumulative research”, American Economic Review, 101(5): 1933–1963.

Romer, P (1990) “Endogenous technological change”, Journal of Political Economy, 98(5): 1002-1037.

Williams, H (2013) “Intellectual property rights and innovation: Evidence from the human genome”, Journal of Political Economy, 121(1): 1-27.

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