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on Network Economics |
| By: | Paolo Pin |
| Abstract: | We develop a model in which country-specific tariffs shape trade flows, prices, and welfare in a global economy with one homogeneous good. Trade flows form a Directed Acyclic Graph (DAG), and tariffs influence not only market outcomes but also the structure of the global trade network. A numerical example illustrates how tariffs may eliminate targeted imports, divert trade flows toward third markets, expose domestic firms to intensified foreign competition abroad, reduce consumer welfare, and ultimately harm the country imposing the tariff. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.04816 |
| By: | Jiankun Chen (School of Economics, University of International Business and Economics, Beijing, China); Yanli Lin (University of Western Australia Business School, Perth, Australia); Yang Yang (Ma Yinchu School of Economics, Tianjin University, Tianjin, China) |
| Abstract: | This paper introduces a model featuring two hierarchically structured layers of spatial or social networks in a cross-sectional setting. Individuals interact within groups, while groups also interact with one another, generating network dependence at both the individual and group levels. The network structures can be flexibly specified using general measures of proximity. The model accommodates individual random effects with heteroskedasticity, as well as unobserved random group effects. Given the complex error structure, we consider a Generalized Method of Moments (GMM) approach for estimation. The linear moment conditions exploit exogenous variations in individual and group characteristics to identify the network parameters at both levels. To enhance identification when linear moments are weak, we also propose a new set of quadratic moments that are robust to heteroskedasticity. Building on the method of Lin and Lee (2010), we can consistently estimate the variance-covariance (VC) matrix of these heteroskedasticity-robust moments, enabling the construction of a GMM estimator with optimally weighted moments. The asymptotic properties of both a generic and the "optimal" GMM estimator are derived. Monte Carlo simulations demonstrate that the proposed estimators perform well in finite samples. The model is applicable to a variety of social and economic contexts where network effects at two distinct levels are of particular interest, with peer effects among students within the same class and spillovers between classes serving as a leading example. |
| Keywords: | Hierarchical networks, Spatial model, Social interaction, Random effect, GMM |
| JEL: | C31 C51 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:uwa:wpaper:25-03 |
| By: | Pasquale Accardo (University of Bath); Giuseppe De Feo (University of Liverpool); Giacomo De Luca (Università Vita-Salute San Raffaele; Free University of Bozen-Bolzano) |
| Abstract: | So far, the application of network analysis to crime has been limited to the relationships within criminal networks. We build a novel network dataset by encoding information coming from the archive of the Italian Anti-mafia Commission, describing relationships of collusion and exchange of favours between mafia members and the political, economic and social elites in Sicily, the homeland of the Sicilian mafia. We apply network analysis techniques to study the "topological" role of mafia bosses and show that they strategically position themselves in the social network as an interface between the criminal and the legitimate world. |
| Date: | 2024–04–04 |
| URL: | https://d.repec.org/n?u=RePEc:eid:wpaper:58184 |
| By: | Rosa Van Den Ende (Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, Universität Bielefeld); Dylan Laplace Mermoud (ENSTA, Institut Polytechnique de Paris, Conservatoire National des Arts et Métiers) |
| Abstract: | Responsibility in complex networks extends beyond direct actions: players should also bear responsibility for the indirect effects within their supply chains or network. We introduce a novel framework to allocate responsibility for indirect environmental, social, and economic impacts across a dynamic network. Unlike static approaches, our framework accounts for the evolving structure of supply chains, financial systems, and other interconnected systems, where relationships change over time. We use the time-dependent Laplacian matrix to capture how responsibility propagates through the network, revealing a diffusion process that aligns with key axioms of fairness: linearity, efficiency, symmetry, and the independent player property. We show that approximating the responsibility measure preserves these properties, supporting the use of our framework as a rigorous and practical method to allocate responsibility in real-world networks |
| Keywords: | Dynamic networks; Laplacian matrix; allocation of responsibility; diffusion; climate policy |
| JEL: | D85 Q5 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:mse:cesdoc:25008 |
| By: | Michael Koch; Antonella Nocco |
| Abstract: | This paper introduces a novel mechanism by emphasizing benefits for firms through participation in buyer networks among firms that source the same locally produced inputs. In a first step, we utilize register-based data from Denmark to generate a firm-specific buyer network variable which relies on firms’ industrial input structures and imports. Utilizing this proxy we provide evidence of cost savings from network participation, as larger buyer networks reduce firms’ input demand. Subsequently, we develop a trade model incorporating vertical linkages and introduce network effects that result in savings in intermediate costs. Our theory posits that the magnitude of these savings may be associated with the effectiveness of knowledge transmission among network participants. Consequently, firms operating in regions with efficient knowledge transmission networks may realize greater savings in intermediate input costs, leading to increased profits from local and export sales. In a last step, we provide empirical evidence supporting our theoretical predictions by demonstrating the positive impact of buyer networks based on relationship-specific products on domestic firm revenues. |
| Keywords: | new trade theory, vertical linkages, network effects. |
| JEL: | F12 F15 R12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_117815 |
| By: | Arieli, Itai; Ashkenazi-Golan, Galit; Peretz, Ron; Tsodikovich, Yevgeny |
| Abstract: | Agents in a network adopt an innovation if a certain fraction of their neighbors has already done so. We study the minimal contagious set size required for a successful innovation adoption by the entire population, and provide upper and lower bounds on it. Since detailed information about the network structure is often unavailable, we study bounds that depend only on the degree distribution of the network – a simple statistic of the network topology. Moreover, as our bounds are robust to small changes in the degree distribution, they also apply to large networks for which the degree distribution can only be approximated. Applying our bounds to growing networks shows that the minimal contagious set size is linear in the number of nodes. Consequently, for outside of knife-edge cases (such as the star-shaped network), contagion cannot be achieved without seeding a significant fraction of the population. This finding highlights the resilience of networks and demonstrates a high penetration cost in the corresponding markets. |
| Keywords: | innovation; diffusion; word-of-mouth; contagious; attachment |
| JEL: | O33 M30 |
| Date: | 2025–05–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127954 |
| By: | Alastair Langtry |
| Abstract: | This paper presents a model of network formation and public goods provision in local communities. Here, networks can sustain public good provision by spreading information about people's behaviour. I find a critical threshold in network connectedness at which public good provision drops sharply, even though agents are highly heterogeneous. Technology change can tear a community's social fabric by pushing high-skilled workers to withdraw from their local community. This can help explain rising resentment toward perceived ``elites'' -- their withdrawal actively harms those left behind. Moreover, well-meaning policies that upskill workers can make them worse off by reducing network connectedness. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.06872 |
| By: | Xian Wu |
| Abstract: | This paper proposes a new algorithm -- Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks. It combines the strength of the traditional simulated method of moments (SMM) and recent machine learning techniques -- Graph Neural Network (GNN). It outperforms existing reduced-form methods with network centrality measures in prediction accuracy. The method can be used on networks with any structure, allowing for heterogeneity among both traders and assets. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.07923 |
| By: | Lin William Cong; Eswar S. Prasad; Daniel Rabetti |
| Abstract: | Oracles are software components that enable data exchange between siloed blockchains and external environments, enhancing smart contract capabilities and platform interoperability. Oracles play key roles in decentralized finance and blockchain applications in centralized finance. We find that integration into decentralized oracle networks is positively associated with key measures of economic activity such as Total Value Locked, triggered by positive network effects in adoption and usage. Our study reveals symbiotic gains from enhanced interoperability and network effects across protocols on a given chain and among integrated chains. Oracle integration appears to improve risk-sharing and mitigates contagion, increasing resilience during turbulent periods in crypto markets. Overall, oracles emerge as a crucial component to enable informational and economic integration in decentralized finance ecosystems. |
| JEL: | F15 F36 G15 G29 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33639 |
| By: | Bobby W. Chung (University of South Florida); Roksana Ghanbariamin (Analysis Group) |
| Abstract: | This paper provides the first empirical evidence on hospital participation spillover of the National Kidney Registry (NKR), the largest kidney-exchange network in the United States. We use a unique dataset from the Scientific Registry of Transplant Recipients to define links between hospitals based on the presence of common surgeons. We find that a hospital with one more NKR connection in the last period is 1.2 to 1.5 times more likely than its no-NKR counterparts to join the NKR. The spillover concentrates among strong connections, measured by the type and the number of common surgeons. In light of the current fragmented kidney-exchange market, our finding sheds light on reducing information friction to promote new participation. |
| Keywords: | Kidney-exchange networks, National Kidney Registry (NKR), Spillover effects |
| JEL: | I11 L14 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:usf:wpaper:2025-02 |
| By: | DeMets, Sydney; Spiro, Emma (University of Washington) |
| Abstract: | Social networks structure the flow of political information that is critical for civic participation and individual decision making, simultaneously opening and constraining the diffusion of ideas and information. Understanding the current information landscape is pressing given the current salience of false and misleading information. Given the growing prominence of podcasts within the information ecosystem, and the high levels of trust that podcasters enjoy from listeners, it is critical to better understand the role this medium plays in political communication. In this paper, we construct a bipartite network of podcasts and their invited guests. We then generate a network of paths that guests take as they move from one podcast to the next using entailment analysis, and evaluate if guests are typically invited to speak on less prominent shows first, before moving on to more prominent shows. This dynamic has several parallels to Centola’s power of the periphery hypothesis, complimented by the idea that guests may visit progressively more prominent podcasts as they themselves become more visible. We also find that shows aiming to feature a politically diverse set of guests on their own shows play an outsize role in brokering the movement of guests between liberal and conservative shows, although this cross-boundary brokerage has equivocal outcomes. |
| Date: | 2025–03–13 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:t7y2c_v1 |
| By: | Michael Coopman; Austin Jacobs; Henry Pascoe; J. E. Pascoe |
| Abstract: | The structure bilateral trading costs is one of the key features of international trade. Drawing upon the freeness-of-trade matrix, which allows the modeling of N-state trade costs, we develop a ``geometry of inconvenience'' to better understand how they impact equilbrium outcomes. The freeness-of-trade matrix was introduced in a model by Mossay and Tabuchi, where they essentially proved that if a freeness-of-trade matrix is positive definite, then the corresponding model admits a unique equilibrium. Drawing upon the spectral theory of metrics, we prove the model admits nonunique, perverse, equilibria. We use this result to provide a family of policy relevant bipartite examples, with substantive applications to economic sanctions. More generally, we show how the network structure of the freeness of trade is central to understanding the impacts of policy interventions. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.07700 |
| By: | Sai Krishna Kamepalli; Serena Ng; Francisco Ruge-Murcia |
| Abstract: | Skewness is a prevalent feature of macroeconomic time series and may arise exogenously because shocks are asymmetrically distributed, or endogenously, as shocks propagate through production networks. Previous theoretical work often studies these two possibilities in isolation. We nest all possible sources of skewness in a model where output has a network, a common, and an idiosyncratic component. In this model, skewness can arise not only from the three components, but also from coskewness due to the higher order covariation between components. An analysis of output growth in 43 U.S. sectors shows that coskewness is a key source of asymmetry in the data and constitutes a connectivity channel not previously explored. To help interpret our results, we construct and estimate a micro-founded multi-sector general equilibrium model and show that it can generate skewness and coskewness consistent with the data. |
| JEL: | C3 C5 E03 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33701 |
| By: | Ṣebnem Kalemli-Özcan; Can Soylu; Muhammed A. Yildirim |
| Abstract: | We develop a novel framework to study the interaction between monetary policy and trade. Our New Keynesian open economy model incorporates international production networks, sectoral heterogeneity in price rigidities, and trade distortions. We decompose the general equilibrium response to trade shocks into distinct channels that account for demand shifts, policy effects, exchange rate adjustments, expectations, price stickiness, and input–output linkages. Tariffs act simultaneously as demand and supply shocks, leading to endogenous fragmentation through changes in trade and production network linkages. We show that the net impact of tariffs on domestic inflation, output, employment, and the dollar depends on the endogenous monetary policy response in both the tariff-imposing and tariff-exposed countries, within a global general equilibrium framework. Our quantitative exercise replicates the observed effects of the 2018 tariffs on the U.S. economy and predicts a 1.6 pp decline in U.S. output, a 0.8 pp rise in inflation, and a 4.8% appreciation of the dollar in response to a retaliatory trade war linked to tariffs announced on “Liberation Day.” Tariff threats, even in the absence of actual implementation, are self-defeating—leading to a 4.1% appreciation of the dollar, 0.6% deflation, and a 0.7 pp decline in output, as agents re-optimize in anticipation of future distortions. Dollar appreciates less or even can depreciate under retaliation, tariff threats, and increased global uncertainty. |
| JEL: | E0 F40 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33686 |