Interests

I work on issues related to technological change and innovation. Most recent research projects study the emergence and spatial concentration of complex technologies using historical data on patent activity and the distributional impact of the emergence and diffusion of disruptive technologies.

Just Published

Complex Economic Activities Concentrate in Large Cities

(with Pierre-Alexandre Balland, Cristian Jara-Figueroa, Mathieu Steijn, David Rigby, and César A. Hidalgo)

 

Why do some economic activities agglomerate more than others? And, why does the agglomeration of some economic activities continue to increase despite recent developments in communication and transportation technologies? In this paper, we present evidence that complex economic activities concentrate more in large cities. We find this to be true for technologies, scientific publications, industries, and occupations. Using historical patent data, we show that the urban concentration of complex economic activities has been continuously increasing since 1850. These findings suggest that the increasing urban concentration of jobs and innovation might be a consequence of the growing complexity of the economy.

Media Coverage: The Atlantic, Boston.com, New York TimesLiveMint

Mapping General Purpose Technologies with Patent Data

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.

Data: https://dataverse.harvard.edu/dataverse/GPT-Indicators

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