Tesi etd-01302025-114942
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Tipo di tesi
Dottorato
Autore
LUZZATI, DAVIDE SAMUELE
URN
etd-01302025-114942
Titolo
Essays on Economic Networks
Settore scientifico disciplinare
SECS-P/01
Corso di studi
Istituto di Economia - PHD IN ECONOMICS
Relatori
relatore Prof. FAGIOLO, GIORGIO
Parole chiave
- applied econometrics
- climate change
- network reconstruction
- production networks
Data inizio appello
17/07/2025;
Disponibilità
parziale
Riassunto analitico
This thesis investigates the relationships between the network structure of an economy and its outcomes and performance, exploring this issue across different levels of aggregation, ranging from countries to firms. It is organized into three core chapters, each addressing a distinct level of analysis.
After reviewing existing methodologies assessing the economic impacts of extreme weather events (Chapter 2), we introduce a novel approach to construct realistic synthetic firm-to-firm networks using partially aggregated data such as input-output tables and firm size distributions (Chapter 3). We show how building synthetic networks significantly improves the accuracy of simulation exercises, as compared to a sector-level analysis. To illustrate the applicability of our approach, we simulate the economic impact of a natural disaster and compare the results on empirically observed firm-to-firm networks. In Chapter 4 we shift the focus from firms to sectors, analyzing the long-term evolution of global production networks from 1965 to 2014. We employ a variety of network metrics including standard centrality measures, Global Value Chain (GVC) positioning, and intermediate intensity of production, to capture different dimensions of the production process. After providing a descriptive overview of the overall trends, we explore how these statistically associate to different performance indicators, such as productivity and value-added growth. Our findings reveal that both national and international linkages are crucial for growth, albeit with distinct impacts on the different outcome variables. We also discuss how the sector-level results aggregate to the country level. In Chapter 5 we finally move from sectors to countries examining their interconnections across three network layers: trade, migration, and finance. By constructing a multiplex network, we demonstrate how centrality measures influence country income in heterogeneous ways across the layers.
After reviewing existing methodologies assessing the economic impacts of extreme weather events (Chapter 2), we introduce a novel approach to construct realistic synthetic firm-to-firm networks using partially aggregated data such as input-output tables and firm size distributions (Chapter 3). We show how building synthetic networks significantly improves the accuracy of simulation exercises, as compared to a sector-level analysis. To illustrate the applicability of our approach, we simulate the economic impact of a natural disaster and compare the results on empirically observed firm-to-firm networks. In Chapter 4 we shift the focus from firms to sectors, analyzing the long-term evolution of global production networks from 1965 to 2014. We employ a variety of network metrics including standard centrality measures, Global Value Chain (GVC) positioning, and intermediate intensity of production, to capture different dimensions of the production process. After providing a descriptive overview of the overall trends, we explore how these statistically associate to different performance indicators, such as productivity and value-added growth. Our findings reveal that both national and international linkages are crucial for growth, albeit with distinct impacts on the different outcome variables. We also discuss how the sector-level results aggregate to the country level. In Chapter 5 we finally move from sectors to countries examining their interconnections across three network layers: trade, migration, and finance. By constructing a multiplex network, we demonstrate how centrality measures influence country income in heterogeneous ways across the layers.
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