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Essays on Innovation Networks and Global Cities

Edet, Samuel Asuquo (2022) Essays on Innovation Networks and Global Cities. Advisor: Riccaboni, Prof. Massimo. Coadvisor: Belderbos, Prof. Rene . pp. 275. [IMT PhD Thesis]

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In a competitive economy, technological innovation is a core element of economic growth and development, and its accumulation in a rapidly changing technological environment is key to adaptation. This thesis investigates how cities, particularly global cities can develop new technological capabilities to enter new technological fields and become competitive in new technological areas. First, a new geo-referenced patent database is developed to overcome some limitations of the existing international patent repository to make an international comparison of cities feasible. The new database provides broad coverage of cities in developed and emerging economies and allows us to tackle the following research questions: (i)how the co-patenting network of domestic and international linkages of cities has changed over time? (ii) which cities are more technologically complex? Can we predict the future evolution of cities’ technological complexity? (iii) What is the effect of inter-city linkages and technological relatedness on the likelihood of cities to enter new technological areas? (iv) which cities are the most central in coordinating complex international teams of inventors on a global scale? To geo-locate patents, an address retrieval algorithm has been applied to resolve the problem of missing addresses, exploiting the availability of address in patent family and similarity of inventors based on attributes such as working for the same applicant. A harmonized definition for functional urban areas is used to assign patents to cities on a global scale. The database provides a significant improvement from the raw PATSTAT dataset with an estimated confidence level of 84-88%. We analyze both unweighted (extensive) and patent-weighted (intensive) linkages between cities to address the first research question. The preliminary findings show an increase of international ties both intensively and extensively and the reliance of cities in developing economies on international ties to global cities. To address the second research question, we apply the generalized economic complexity (GENEPY) algorithm to measure the economic complexity of cities based on patent production in these cities. We use machine learning models (Random Forest, XGBoost, SVM, Neural network) to forecast the future technological complexities of cities. We show that the machine learning models (especially Random Forests) have higher predictive power than the benchmark model (time-independent conditional probabilities) as they account for higher-order and non-linear interdependencies between technologies. To address the third question, we applied a stratified semiparametric Cox proportional hazard model to examine the likelihood of cities entering new technologies. We show that inter-city linkages and technological relatedness significantly increase the likelihood of entry into new technological areas. Inter-city linkages are more critical for non-global cities than for global cities to enter new technological areas, whereas linkages to inventors located in cities with a large pool of inventors positively moderate the effect of inter-city linkages on entry. For the last research question, we use hypergraphs structure and propose a measure based on 3-hyperedges (three cities in multiple countries) in the collaboration networks constructed from scientific publications and patents to identify the most competitive global cities in the international network of inventors. To this end, we construct a null model using the hypergeometric ensembles of random graphs and find that five US cities play a leading role in transnational networks of researchers. San Francisco stands out as the most global city, but Shanghai is rapidly emerging as a global player.

Item Type: IMT PhD Thesis
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
PhD Course: Economics, Networks and Business Analytics
Identification Number: https://doi.org/10.13118/kknf-9a08
NBN Number: urn:nbn:it:imtlucca-28253
Date Deposited: 30 Apr 2022 11:19
URI: http://e-theses.imtlucca.it/id/eprint/348

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