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on Network Economics |
| By: | Xunkang Tian |
| Abstract: | This study introduces a novel approach for inferring social network structures using Aggregate Relational Data (ARD), addressing the challenge of limited detailed network data availability. By integrating ARD with variational approximation methods, we provide a computationally efficient and cost-effective solution for network analysis. Our methodology demonstrates the potential of ARD to offer insightful approximations of network dynamics, as evidenced by Monte Carlo Simulations. This paper not only showcases the utility of ARD in social network inference but also opens avenues for future research in enhancing estimation precision and exploring diverse network datasets. Through this work, we contribute to the field of network analysis by offering an alternative strategy for understanding complex social networks with constrained data. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01503 |
| By: | Fujita, Takaaki |
| Abstract: | Graph theory has been widely applied across diverse scientific domains [1, 2]. Hypergraphs extend classical graphs by allowing hyperedges to connect arbitrary subsets of vertices, while superhypergraphs further enrich this structure through iterated powersets that capture hierarchical and self-referential relationships [3, 4]. An actor network links heterogeneous actants—humans, artifacts, texts, and rules—through directed associations, emphasizing relational materiality and performative agency [5, 6]. An urban road network is a directed, weighted graph of intersections and road segments, modeling connectivity, capacities, and travel dynamics. In this paper, we extend actor networks and urban road networks by employing HyperGraphs and SuperHyperGraphs. These extensions are expected to provide clearer and more expressive representations of hierarchical structures in real-world actor networks and urban road networks. |
| Date: | 2025–09–12 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:57mvr_v1 |
| By: | Linh Nguyen; Marcel Boersma; Erman Acar |
| Abstract: | Fraudulent activity in the financial industry costs billions annually. Detecting fraud, therefore, is an essential yet technically challenging task that requires carefully analyzing large volumes of data. While machine learning (ML) approaches seem like a viable solution, applying them successfully is not so easy due to two main challenges: (1) the sparsely labeled data, which makes the training of such approaches challenging (with inherent labeling costs), and (2) lack of explainability for the flagged items posed by the opacity of ML models, that is often required by business regulations. This article proposes SAGE-FIN, a semi-supervised graph neural network (GNN) based approach with Granger causal explanations for Financial Interaction Networks. SAGE-FIN learns to flag fraudulent items based on weakly labeled (or unlabelled) data points. To adhere to regulatory requirements, the flagged items are explained by highlighting related items in the network using Granger causality. We empirically validate the favorable performance of SAGE-FIN on a real-world dataset, Bipartite Edge-And-Node Attributed financial network (Elliptic++), with Granger-causal explanations for the identified fraudulent items without any prior assumption on the network structure. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01980 |
| By: | Neave O'Clery; Ben Radcliffe-Brown; Thomas Spencer; Daniel Tarling-Hunter |
| Abstract: | Critical for policy-making and business operations, the study of global supply chains has been severely hampered by a lack of detailed data. Here we harness global firm-level transaction data covering 20m global firms, and 1 billion cross-border transactions, to infer key inputs for over 1200 products. Transforming this data to a directed network, we find that products are clustered into three large groups including textiles, chemicals and food, and machinery and metals. European industrial nations and China dominate critical intermediate products in the network such as metals, common components and tools, while industrial complexity is correlated with embeddedness in densely connected supply chains. To validate the network, we find structural similarities with two alternative product networks, one generated via LLM queries and the other derived by NAFTA to track product origins. We further detect linkages between products identified in manually mapped single sector supply chains, including electric vehicle batteries and semi-conductors. Finally, metrics derived from network structure capturing both forward and backward linkages are able to predict country-product diversification patterns with high accuracy. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.12315 |
| By: | de Vos, Wout (Tilburg University, School of Economics and Management); Grabisch, Michel; Rusinowska, Agnieszka |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:2db67b0b-ba8b-46e8-85af-10602d95e658 |
| By: | Chengqing Li; Junjie Zhou |
| Abstract: | We examine price regulation for monopolists in networks with demand spillovers. The Pareto frontier of the profit-surplus set is characterized using a centrality-based price family. Under typical price regulation policies, regulated outcomes are generically Pareto inefficient at fixed spillover levels but become neutral as spillovers grow, with relative profit loss and surplus changes vanishing. Welfare impacts of banning price discrimination under strong spillovers depend solely on the correlation between intrinsic values and network summary statistics. In networks with two node types (e.g., coreperiphery or complete bipartite), intrinsic value averages across node types suffice for welfare comparisons. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.17301 |
| By: | Anna Mancini; Bal\'azs Lengyel; Riccardo Di Clemente; Giulio Cimini |
| Abstract: | Recent crises like the COVID-19 pandemic and geopolitical tensions have exposed vulnerabilities and caused disruptions of supply chains, leading to product shortages, increased costs, and economic instability. This has prompted increasing efforts to assess systemic risk, namely the effects of firm disruptions on entire economies. However, the ability of firms to react to crises by rewiring their supply links has been largely overlooked, limiting our understanding of production networks resilience. Here we study dynamics and determinants of firm-level systemic risk in the Hungarian production network from 2015 to 2022. We use as benchmark a heuristic maximum entropy null model that generates an ensemble of production networks at equilibrium, by preserving the total input (demand) and output (supply) of each firm at the sector level. We show that the fairly stable set of firms with highest systemic risk undergoes a structural change during COVID-19, as those enabling economic exchanges become key players in the economy -- a result which is not reproduced by the null model. Although the empirical systemic risk aligns well with the null value until the onset of the pandemic, it becomes significantly smaller afterwards as the adaptive behavior of firms leads to a more resilient economy. Furthermore, firms' international trade volume (being a subject of disruption) becomes a significant predictor of their systemic risk. However, international links cannot provide an unequivocal explanation for the observed trends, as imports and exports have opposing effects on local systemic risk through the supply and demand channels. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.21426 |
| By: | Manabu Nose (Keio University, Faculty of Economics); Yasuyuki Sawada (University of Tokyo, Faculty of Economics, Graduate School of Economics) |
| Abstract: | This paper examines the nonlinear effects of a large-scale highway construction project in the Greater Mekong Subregion, which connects the historically conflict-affected borderlands of northern Vietnam to the country’s industrial core. Employing a market access framework with geo-coded highway network and firm-level panel data, we estimate the causal impact of improved interregional connectivity, while accounting for spillovers via production input-output linkages. To address endogeneity issues arising from non-random route placements, we construct least-cost path spanning tree networks. Our instrumental variable estimates reveal that enhanced market access spurred manufacturing firm agglomeration and employment growth, particularly in peripheral rural areas. We further explore the underlying sources of polycentric development patterns, finding pronounced effects in second-tier cities characterized by less intense competition and better access to national road networks. Our findings are robust to controls for industrial zones, underscoring the pivotal role of the upgraded highway connectivity in transforming previously marginalized regions and supporting economy-wide industrialization over the past decade. |
| Keywords: | spatial structural transformation, market access, treatment spillover, agglomeration, core-periphery |
| JEL: | O14 O18 O22 O25 R12 R32 R58 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:keo:dpaper:dp2025-010 |
| By: | Alonso Alfaro-Ureña (Department of Economic Research, Central Bank of Costa Rica); Paolo Zacchia (Charles University, Czech Academy of Sciences, IZA) |
| Abstract: | We build a model of production network formation that enables econometric estimation of the determinants of supplier choice, like trade costs or matching frictions. The model informs an estimator obtained from a transformation of the multinomial logit likelihood function that conditions on two network statistics: the out-degree of sellers (a sufficient statistic for the seller marginal costs) and the in-degree of buyers (which is pinned down by technology and by “make-or-buy” decisions). In an empirical application about the network effects of a major Costa Rican highway, this estimator registers much smaller estimates than those of a (biased) naive multinomial logit. ***Resumen: Desarrollamos un modelo de formación de redes de producción que permite la estimación econométrica de los determinantes en la elección de proveedores, tales como los costos de comerciar o las fricciones de emparejamiento. El modelo guía un estimador obtenido a partir de una transformación de la función de verosimilitud del logit multinomial, que está condicionado por dos estadísticas de red: el grado de salida de los vendedores (una estadística suficiente para los costos marginales del vendedor) y el “grado de entrada” (in-degree) de los compradores (determinado por la tecnología y las decisiones de “hacer o comprar”). En una aplicación empírica sobre los efectos en red de una importante carretera en Costa Rica, este estimador arroja estimaciones considerablemente menores que las de un logit multinomial ingenuo (y sesgado). |
| Keywords: | Production network, Supplier choice, Conditional logit, Infrastructures, infraestructura, logit condicional, elección de proveedores, redes de producción |
| JEL: | C25 L11 R12 R15 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:apk:doctra:2502 |
| By: | Okan Akarsu |
| Abstract: | In this paper, I explore the spillover effects of frontier firms on other firms in Türkiye, using a detailed administrative dataset with firm-level data on balance sheets, inter-firm transactions, and employment. I review key production function estimators, evaluate their assumptions and performance using a large dataset of Turkish firms, and apply estimated productivity to identify frontier firms and assess their influence on laggard firms' performance. Additionally, I contribute to the empirical literature by exploring the spillover and network effects of frontier firms on laggard firms, as well as examining the productivity convergence of laggard firms to frontier firms. The analysis reveals three key findings: (i) Frontier firms generate positive spillover effects within sectors, which enhance sales, employment, exports, and asset growth among laggard firms; (ii) detailed firm-to-firm invoice data reveals that a higher share of frontier firms in a firm’s network significantly boosts investment, net sales, and productivity growth; and (iii) laggard firms show faster productivity growth, with substantial variation across firm types and industries. |
| Keywords: | Spillover effect, Frontier firm, Total factor productivity, Production function estimation, Semiparametric estimator, Laggard firm dynamics |
| JEL: | C13 C14 C23 D24 D40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:wpaper:2511 |
| By: | Ali Kakhbod; Peiyao Li |
| Abstract: | We present NoLBERT, a lightweight, timestamped foundational language model for empirical research in social sciences, particularly in economics and finance. By pre-training exclusively on 1976-1995 text, NoLBERT avoids both lookback and lookahead biases that can undermine econometric inference. It exceeds domain-specific baselines on NLP benchmarks while maintaining temporal consistency. Applied to patent texts, NoLBERT enables the construction of firm-level innovation networks and shows that gains in innovation centrality predict higher long-run profit growth. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.01110 |
| By: | Semieniuk, Gregor; Weber, Isabella M.; Weaver, Iain S.; Wasner, Evan; Braun, Benjamin; Holden, Philip B.; Salas, Pablo; Mercure, Jean-Francois; Edwards, Neil R. |
| Abstract: | The 2022 oil and gas crisis resulted in record fossil-fuel profits globally that rehabilitated the oil and gas industry, obstructed the energy transition and contributed to inflation, but their magnitude and beneficiaries have been insufficiently understood. Here we show the size of profits across countries and their distribution across socio-economic groups within the United States, using company income statements, comprehensive ownership data and a network model for propagating profits via shareholdings. We estimate that globally, net income in publicly listed oil and gas companies alone reached US$916 billion in 2022, with the United States the biggest beneficiary with claims on US$301 billion, more than U.S. investments of US$267 billion in the low carbon economy that year. In a network of U.S. shareholdings with 252, 433 nodes including privately held U.S. companies, 50 % of profits went to the wealthiest 1 % of individuals, predominantly through direct shareholdings and private company ownership. In contrast the bottom 50 % only received 1 %. The incremental U.S. fossil-fuel profits in 2022 relative to 2021 were enough to increase the disposable income of the wealthiest Americans by several percent and compensate a substantial part of their purchasing power loss from inflation that year, thereby exacerbating inflation inequality. These profits also reinforced existing racial and ethnic inequalities and inequalities between groups with different educational attainments. We discuss how an excess profit tax could be used to both lower inequality and accelerate the energy transition as increasing geopolitical tensions and climate impacts threaten continued volatility in oil and gas markets. |
| JEL: | R14 J01 N0 |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129170 |