# 22.11 Disruptive innovation and spatial inequality

Tom Kemeny, Sergio Petralia & Michael Storper

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Abstract:

Although technological change is widely credited as driving the last two hundred years of economic growth, its role in shaping patterns of inequality remains under-explored. Drawing parallels across two industrial revolutions in the United States, this paper provides new evidence of a relationship between highly disruptive forms of innovation and spatial inequality. Using the universe of patents granted between 1920 and 2010 by the U.S. Patent and Trademark Office, we identify disruptive innovations through their rapid growth, complementarity with other innovations, and widespread use. We then assign more- and less-disruptive innovations to subnational regions in the geography of the U.S. We document three findings that are new to the literature. First, disruptive innovations exhibit distinctive spatial clustering in phases understood to be those in which industrial revolutions reshape the economy; they are increasingly dispersed in other periods. Second, we discover that the ranks of locations that capture the most disruptive innovation are relatively unstable across industrial revolutions. Third, regression estimates suggest a role for disruptive innovation in regulating overall patterns of spatial output and income inequality

# 20.27 Mapping General Purpose Technologies with Patent Data

Sergio Petralia

 

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Abstract:

This article develops a three-dimension indicator to capture the main features of General Purpose Technologies (GPTs) in patent data. Technologies are evaluated based on their scope for improvement and elaboration, the variety of products and processes that use them, and their complementarity with existing and new technologies. Technologies’ scope for improvement is measured using patenting growth rates. The range of its uses is mapped by implementing a text-mining algorithm that traces technology-specific vocabulary in the universe of all available patent documents. Finally, complementarity with other technologies is measured using the co-occurrence of technological claims in patents. These indicators are discussed and evaluated using widely studied examples of GPTs such as Electric & Electronic (at the beginning of the 20th century) and Computer & Communications. These measures are then used to propose a simple way of identifying GPTs with patent data. It is shown there exist a positive association between the rate of adoption of GPTs in sectors, measured in terms of the number of GPT patents, and their growth.

# 17.22 R&D Policy and Technological Trajectories of Regions: Evidence from the EU Framework Programmes

Wolf-Hendrik Uhlbach, Pierre-Alexandre Balland and Thomas Scherngell

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It is widely acknowledged that new technological specializations of regions are to a large extent driven by the recombination of existing knowledge and capabilities. Since this process is path-dependant and self-reinforcing, it can easily lead to technological lock-ins. A key issue is therefore to evaluate whether public policy can impact technological trajectories of regions and how it can be more effective. To address this issue, we analyze quantitatively and systematically the relation between R&D subsidies and new technological specializations of European regions from 1999 to 2010. R&D subsidies are identified by using the EU Framework Pro- grammes (FP) from the EUPRO database, and matched with patent documents from the OECD-REGPAT database. Using a fixed-effects linear probability model, our results indicate that FP participations have a positive but relatively small effect on the development of new specializations of regions, and that it can compensate for a lack of local related capabilities. We also find evidence that R&D subsidies have the highest impact if the level of relatedness with the new technology is neither too low (policy can not build a cathedral in the desert) nor too high (if all the capabilities are already present there is no need for policy).

# 15.15 The Evolution of Specialization in the EU15 Knowledge Space

Dieter F. Kogler and Jürgen Essletzbichler and David L. Rigby

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Significant attention has been directed to processes of knowledge production in a spatial context, but little consideration has been given to the type of technological knowledge produced within specific places. In this paper we use patent co-classification data from the European Patent Office (EPO) to measure the distance between all pairs of 629 International Patent Classification (IPC) categories. A multi-dimensional scaling algorithm allows us to visualize these distances in a map of the EU15 knowledge space. We trace the evolution of that space from 1981 to 2005. The patent class distance data are combined with counts of patents by IPC categories to measure the average relatedness (specialization) of knowledge produced within each NUTS2 region. We show that knowledge specialization has increased significantly across EU15 regions over time and we report those regions that have the most specialized and the least specialized knowledge bases. Changes in the average relatedness of regional knowledge cores are decomposed to reveal the contributions of technological entry, exit and selection processes over space and time. In a final section of the paper, technological diversification and abandonment at the NUTS2 level are modeled as a function of proximity to the knowledge core of the region and to knowledge spillovers from neighboring regions that are mediated by social and spatial distance.

# 13.16 Relatedness and Technological Change in Cities: The rise and fall of technological knowledge in U.S. metropolitan areas from 1981 to 2010

Ron Boschma, Pierre-Alexandre Balland, Dieter Franz Kogler

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This paper investigates by means of USPTO patent data whether technological relatedness was a crucial driving force behind technological change in 366 U.S. cities from 1981 to 2010. Based on a three-way fixed effects model, we find that the entry probability of a new technology in a city increases by 30 percent if the level of relatedness with existing technologies in the city increases by 10 percent, while the exit probability of an existing technology decreases by 8 percent.