Comparative analysis of technological fitness and coherence at different geographical scales.

Debates over the trade-offs between specialization and diversification have long intrigued scholars and policymakers. Specialization can amplify an economy by concentrating on core strengths, while diversification reduces vulnerability by distributing investments across multiple sectors. In this paper, we use patent data and the framework of Economic Complexity to investigate how the degree of technological specialization and diversification affects economic development at different scales: metropolitan areas, regions and countries.

The geography of technological innovation dynamics.

Cities and metropolitan areas are major drivers of creativity and innovation in all possible sectors: scientific, technological, social, artistic, etc. The critical concentration and proximity of diverse mindsets and opportunities, supported by efficient infrastructures, enable new technologies and ideas to emerge, thrive, and trigger further innovation. Though this pattern seems well established, geography's role in the emergence and diffusion of new technologies still needs to be clarified.

Prediction and visualization of Mergers and Acquisitions using Economic Complexity.

Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones.

Urban economic fitness and complexity from patent data.

Over the years, the growing availability of extensive datasets about registered patents allowed researchers to get a deeper insight into the drivers of technological innovation. In this work, we investigate how patents' technological contents characterise metropolitan areas' development and how innovation is related to GDP per capita. Exploiting worldwide data from 1980 to 2014, and through network-based techniques that only use information about patents, we identify coherent distinguished groups of metropolitan areas, either clustered in the same geographical area or similar in terms of their economic features.