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.

Essays on Economic Networks

LUZZATI, DAVIDE SAMUELE
2026

Abstract

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.
12-gen-2026
Italiano
applied econometrics
climate change
network reconstruction
production networks
FAGIOLO, GIORGIO
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/377087
Il codice NBN di questa tesi è URN:NBN:IT:SSSUP-377087