Titolo della tesi: Interplay between technologies and development of metropolitan areas and firms
This thesis aims to employ principles and methodologies from physics to survey, comprehend, and model the patent-related activities of companies and cities to uncover and quantify their economic role and impact.
Technological innovation and patenting play pivotal roles in enhancing the economic prosperity of nations, regions, cities and firms. Extensive research has consistently demonstrated a positive correlation between technological innovation and economic growth. Simultaneously, increased patenting activity drives competitiveness in product exports and fosters scientific progress. Thus, technological advancement and patenting are powerful catalysts in pursuing global progress and economic growth. The focus of my research is on cities due to their significant role in various contexts, including production and dissemination, scientific knowledge, and cultural exchange.
Moreover, firms are the main drivers of innovation. Just think of the field of Artificial Intelligence (AI), for which in recent years there has been a dramatic increase in corporate spending on related research projects, or the role of companies during the COVID-19 pandemic in developing vaccines at a rate unsustainable for academic actors. Given companies’ prominent role in scientific and technological progress, understanding the predictability of corporate patent output is critical for several actors. Indeed, it can help managers identify effective innovation strategies and high-potential investment opportunities, and policymakers design effective policies that promote entrepreneurship and accelerate human progress.
This PhD thesis contributes to developing the field of Economic Complexity (EC), i.e. the study of economics through the means used by the physics of Complex Systems, by introducing new methods of analysis and models that can shed light on issues arising from the economic and development impact of companies and cities from their patent activity.
This thesis is structured as follows. First, I present a general discussion on EC to set the framework for this thesis and present general considerations on both macro and micro aspects of EC. The first consists of presenting and discussing the Economic Fitness and Complexity (EFC) algorithm, a tool that quantifies how competitive, for example, a country is in exporting a product or, in the case of this thesis, how competitive a city or company is in technological development. The second concerns Similarity and Relatedness calculations, since they are fundamental tools in my research. These measures quantify respectively the similarity between different technology activities, and the relationship between a city or firm and a specific technology activity. After this general introduction, in chapter 2, I present the database and data preparation, a process essential to the results obtained. Finally, to discuss the contribution of my research, I present the results, which can be found also in the following publications:
• Matteo Straccamore, Luciano Pietronero, and Andrea Zaccaria. Which will be your firm’s next technology? Comparison between machine learning and network-based algorithms. Journal of Physics: Complexity, 3(3):035002, 2022. [1];
• Lorenzo Arsini, Matteo Straccamore, and Andrea Zaccaria. Prediction and visualization of mergers and acquisitions using economic complexity. Plos one, 18(4):e0283217, 2023. [2];
• Matteo Straccamore, Matteo Bruno, Bernardo Monechi, and Vittorio Loreto. Urban economic fitness and complexity from patent data. Scientific Reports, 13(1):3655, 2023. [3];
• Matteo Straccamore, Vittorio Loreto, and Pietro Gravino. The geography of technological innovation dynamics. Scientific Reports, 13(1):21043, 2023. [4].
In the last two chapters, I present the main results of this work. Chapter 3 presents the findings related to firms, as published in [1] and [2]. In [1], we show how Relatedness and Similarity measures can predict the future technological output of companies by offering the superiority of Machine Learning (ML). Moreover, we introduce the Continuos Technology Space, a tool able to solve the interpretability problems of the ML by projecting the forecast results on a 2D plane. In [2], we exploit firms’ technology activity to find how Similarity and Relatedness measures can be used to forecast Mergers & Acquisitions between companies. All this is done by only using information about the patent activity of firms. Chapter 4 is devoted to cities. In [3], we highlight the importance of patent activity in determining economic welfare in cities. Also, we show how it is more important for cities to know the degree of coherence of their technology production (i.e., how close and similar the technologies produced by a city) than how many technologies are made. A city with a more coherent technology basket will have a better chance of economic growth.
Finally, in [4], we study state institutions’ importance in inter-city technology diffusion. To do this, we define a new measure of Relatedness that considers the belonging of two cities to the same country. In addition to giving better prediction results, the new measure is fully interpretable. Our evidence suggests that political geography has been highly important for the diffusion of innovation till around two decades ago, slowly declining afterwards in favour of a more global innovation ecosystem.
In conclusion, this thesis presents different findings in the field of technological development and innovation diffusion:
• Technological Output Prediction in Companies: Demonstrated that Machine Learning, enhanced by the Continuous Technology Space for interpretability, can effectively predict companies' future technological output using Relatedness and Similarity measures.
• Forecasting Mergers & Acquisitions: Showed that firms' patent activities can predict Mergers & Acquisitions, emphasizing the importance of technological Similarity and Relatedness in corporate consolidations.
• Economic Welfare in Cities: Highlighted the crucial role of patent activity and the coherence of technology production in driving a city's economic growth, rather than the quantity of technologies developed.
• Inter-City Technology Diffusion: Introduced a new Relatedness measure considering cities' political geography, revealing a shift from a politically influenced innovation diffusion to a more globalized approach in recent decades.
These findings collectively enhance our understanding of how technological innovation and strategic alignment influence economic and corporate dynamics.