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on Network Economics |
| By: | Ryota Ishikawa (Graduate School of Economics, Waseda University) |
| Abstract: | Bramoull´e et al. (2009) provided identification conditions for linear social interaction models through network structures. Despite the importance of their results, the authors omitted detailed mathematical discussions. Moreover, they consider cases where many identical networks are observed simultaneously within the same dataset. In reality, multiple networks with different structures, such as classrooms or villages, are repeatedly observed within the same dataset. The purpose of this paper is to fill in the mathematical gaps in their arguments and to establish identification conditions for networks with different structures. In addition, we find the smallest network size as a necessary condition for identifying social effects. We also discuss the identification conditions of network models with a fixed network effect. |
| Keywords: | identification, network model, social interactions, network size |
| JEL: | C31 D85 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:wap:wpaper:2509 |
| By: | Alviarez, Vanessa; Blyde, Juan S. |
| Abstract: | Trade flows are the result of the interaction between buyers and sellers. Understanding how these firm-to-firm relationships form, survive, and evolve over time helps identify how countries can improve their export outcomes. Using a very granular dataset of United States import transactions from all countries around the world, we present a battery of static and dynamic metrics of firm-to-firm relationships. We show how Latin American exporters compare with exporters from other regions across all the metrics. We show that trade costs negatively affect the formation and duration of firm-to-firm networks. Regional differences in these costs partially explain Latin Americas performance. The study discusses a series of measures to reduce trade-related cots in the region that, in light of the results, are likely to improve the trade networks of Latin American countries. |
| Keywords: | Trade networks;Supply Chains;exports |
| JEL: | F12 F14 L22 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14092 |
| By: | Nina Boyarchenko; Hyuntae Choi; Leonardo Elias |
| Abstract: | The modern financial system is complex, with funding flowing not just from the financial sector to the real sector but within the financial sector through an intricate network of financial claims. While much of our work focuses on understanding the end result of these flows—credit provided to the real sector—we explore in this post how accounting for interlinkages across the financial sector changes our perception of who finances credit to the real sector. |
| Keywords: | real sector lending; financial networks; nonbank financial institutions (NBFIs); intermediated credit |
| JEL: | G21 G32 |
| Date: | 2025–05–12 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:99952 |
| By: | Dräger, Lena; Gründler, Klaus; Potrafke, Niklas |
| Abstract: | Social interactions affect individual behavior in a variety of ways, but their effects on expectation formation are less well understood. We design a large-scale global survey experiment among renowned experts working in 135 countries to study whether peer effects impact expectations about the macroeconomy. The global setting allows us to exploit rich cross-national variation in macroeconomic fundamentals. Our experiment uncovers sizable effects of peers and shows that peer information also shifts monetary policy recommendations of experts. The results have important implications for the design of policies and models of information acquisition. |
| Keywords: | Inflation expectations; belief formation; peer effects; survey experiment; economic experts |
| JEL: | E31 E71 D84 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:han:dpaper:dp-739 |
| By: | Lawrence, Alice |
| Abstract: | In today's highly competitive and volatile business environment, designing a supply chain network that strikes an optimal balance between cost-efficiency and resilience is crucial for sustaining long-term performance. This paper explores the intricate challenge of balancing these two key objectives in supply chain network design. Cost reduction has traditionally been a central goal, driving decisions around supplier selection, transportation networks, and inventory management. However, increasing global disruptions—ranging from natural disasters to geopolitical tensions—highlight the need for robust supply chains that can adapt to unforeseen shocks. By integrating cost minimization with resilience strategies, organizations can develop networks that are not only efficient but also agile and adaptable in the face of change. Through a combination of theoretical frameworks and practical case studies, this research proposes a multi-criteria decision-making model that incorporates both cost and resilience factors. We examine various strategies, such as diversification, inventory buffering, and flexible transportation routes, while also considering trade-offs that may arise between financial performance and operational flexibility. The study concludes that while achieving an absolute balance is challenging, a dynamic, context-sensitive approach to supply chain design can enable firms to navigate the complexities of modern global markets with greater agility and confidence. Keywords: Supply chain network design, cost efficiency, resilience, multi-criteria decision-making, global disruptions, supply chain agility, operational flexibility, inventory management, transportation strategy, strategic trade-offs. |
| Date: | 2024–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:wuk6m_v1 |
| By: | Hiroshi Kumanomido; Yutaro Takayasu; Suguru Otani |
| Abstract: | We construct a novel elite-level dataset on prewar Japanese elites using the Personnel Inquiry Records (PIR), a historical biographical source covering 1903 to 1939. The PIR contains rich information on family structure, education, occupation, and spatial mobility, capturing elite formation during Japan's transition from a feudal to modern society. By reconstructing intergenerational links and family networks, we document the persistence and transformation of elite status. The dataset provides a foundational resource for quantitative historical research on elite reproduction, social stratification, and institutional development. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.18788 |
| By: | Laura Spillner (Universität Bremen [Deutschland] = University of Bremen [Germany] = Université de Brême [Allemagne]); Carlo Romano Marcello Alessandro Santagiustina (médialab - médialab (Sciences Po) - Sciences Po - Sciences Po, University of Ca’ Foscari [Venice, Italy], Venice International University); Thomas Mildner (Universität Bremen [Deutschland] = University of Bremen [Germany] = Université de Brême [Allemagne]); Robert Porzel (Universität Bremen [Deutschland] = University of Bremen [Germany] = Université de Brême [Allemagne]) |
| Abstract: | In this work, we apply a hybrid-AI framework to analyse online discourse about inequality on Twitter. Our approach integrates knowledge from natural language understanding, knowledge graph-based semantics, and network analysis to identify narratives around inequality and analyse their changes during the COVID pandemic. We present a two-step approach: A bird's-eye perspective on the overall discourse network around inequality maps how the entities, concepts, and events of the narratives are connected by linking these to existing knowledge graphs and can be filtered on tweet metadata such as time. Two connected entities can then be explored in more detail in a fine-grained analysis of how the entities and their relationship are characterised by social media users, highlighting the diffusion of different perspectives on a given topic related to inequality, such as gender, education, and poverty. |
| Abstract: | Dans cette étude, nous mettons en œuvre un cadre hybride reposant sur l'intelligence artificielle afin d'analyser les discours en ligne relatifs aux inégalités à travers Twitter. Notre approche intègre des connaissances issues de la compréhension du langage naturel, de la sémantique fondée sur des graphes de connaissances, ainsi que de l'analyse de réseaux, afin d'identifier les récits portant sur les inégalités et d'examiner leur évolution durant la pandémie de COVID. Nous présentons une démarche en deux étapes : une première vue d'ensemble du réseau discursif autour des inégalités, qui cartographie les connexions entre entités, concepts et événements en les reliant à des graphes de connaissances existants, avec la possibilité d'appliquer des filtres basés sur les métadonnées des tweets, telles que la temporalité. Ensuite, deux entités connectées peuvent être explorées plus en détail grâce à une analyse fine de la manière dont elles, ainsi que leur relation, sont caractérisées par les utilisateurs des réseaux sociaux. Cette exploration permet de mettre en lumière la diffusion de différentes perspectives sur un sujet donné lié aux inégalités, tels que le genre, l'éducation ou la pauvreté. |
| Keywords: | Social media, Social networking sites, Network analysis, Natural language understanding, Knowledge graphs, Inequality, Inequality perceptions, Socio-economic inequalities, Socio-economic inequality, Socio-economics |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05031924 |