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on Network Economics |
| By: | Wade Hann-Caruthers; Minghao Pan; Omer Tamuz |
| Abstract: | We consider a group of agents who can each take an irreversible costly action whose payoff depends on an unknown state. Agents learn about the state from private signals, as well as from past actions of their social network neighbors, which creates an incentive to postpone taking the action. We show that outcomes depend on network structure: on networks with a linear structure patient agents do not converge to the first-best action, while on regular directed tree networks they do. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.07061 |
| By: | Mengsi Gao |
| Abstract: | This paper investigates the identification and inference of treatment effects in randomized controlled trials with social interactions. Two key network features characterize the setting and introduce endogeneity: (1) latent variables may affect both network formation and outcomes, and (2) the intervention may alter network structure, mediating treatment effects. I make three contributions. First, I define parameters within a post-treatment network framework, distinguishing direct effects of treatment from indirect effects mediated through changes in network structure. I provide a causal interpretation of the coefficients in a linear outcome model. For estimation and inference, I focus on a specific form of peer effects, represented by the fraction of treated friends. Second, in the absence of endogeneity, I establish the consistency and asymptotic normality of ordinary least squares estimators. Third, if endogeneity is present, I propose addressing it through shift-share instrumental variables, demonstrating the consistency and asymptotic normality of instrumental variable estimators in relatively sparse networks. For denser networks, I propose a denoised estimator based on eigendecomposition to restore consistency. Finally, I revisit Prina (2015) as an empirical illustration, demonstrating that treatment can influence outcomes both directly and through network structure changes. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.02183 |
| By: | Luca Colombo (ESC [Rennes] - ESC Rennes School of Business); Paola Labrecciosa; Agnieszka Rusinowska (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We take a novel approach based on differential games to the study of criminal networks. We extend the static crime network game (Ballester et al., 2006, 2010) to a dynamic setting where criminal activities negatively impact the accumulation of total wealth in the economy. We derive a Markov Feedback Equilibrium and show that, unlike in the static crime network game, the vector of equilibrium crime rates is not necessarily proportional to the vector of Bonacich centralities. Next, we conduct a comparative dynamic analysis with respect to the network size, the network density, and the marginal expected punishment, finding results in contrast with those arising in the static crime network game. We also shed light on a novel issue in the network theory literature, i.e., the existence of a voracity effect. Finally, we study the problem of identifying the optimal target in the population of criminals when the planner's objective is to minimize aggregate crime at each point in time. Our analysis shows that the key player in the dynamic and the static setting may differ, and that the key player in the dynamic setting may change over time. |
| Keywords: | Differential games, Markov equilibrium, Criminal networks, Bonacich centrality, Key player |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04850675 |
| By: | Riccardo Milocco; Fabian Jansen; Diego Garlaschelli |
| Abstract: | Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nodes in an abstract geometric space, enabling various vector-based downstream tasks such as network modelling, data compression, link prediction, and community detection. Two apparently unrelated limitations affect these algorithms. On one hand, it is not clear what the basic operation defining vector spaces, i.e. the vector sum, corresponds to in terms of the original nodes in the network. On the other hand, while the same input network can be represented at multiple levels of resolution by coarse-graining the constituent nodes into arbitrary block-nodes, the relationship between node embeddings obtained at different hierarchical levels is not understood. Here, building on recent results in network renormalization theory, we address these two limitations at once and define a multiscale node embedding method that, upon arbitrary coarse-grainings, ensures statistical consistency of the embedding vector of a block-node with the sum of the embedding vectors of its constituent nodes. We illustrate the power of this approach on two economic networks that can be naturally represented at multiple resolution levels: namely, the international trade between (sets of) countries and the input-output flows among (sets of) industries in the Netherlands. We confirm the statistical consistency between networks retrieved from coarse-grained node vectors and networks retrieved from sums of fine-grained node vectors, a result that cannot be achieved by alternative methods. Several key network properties, including a large number of triangles, are successfully replicated already from embeddings of very low dimensionality, allowing for the generation of faithful replicas of the original networks at arbitrary resolution levels. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.04354 |
| By: | Marie Laclau (HEC Paris - Ecole des Hautes Etudes Commerciales, CNRS - Centre National de la Recherche Scientifique); Ludovic Renou (QMUL - Queen Mary University of London); Xavier Venel (LUISS - Libera Università Internazionale degli Studi Sociali Guido Carli [Roma]) |
| Abstract: | We consider sender-receiver games, where the sender and the receiver are two distant nodes in a communication network. We show that if the network has two disjoint paths of communication between the sender and the receiver, then we can replicate all equilibrium outcomes not only of the direct communication game (i.e., when the sender and the receiver communicate directly with each other) but also of the mediated game (i.e., when the sender and the receiver communicate with the help of a mediator). |
| Keywords: | Cheap talk, direct, mediated, communication, protocol, network |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04836057 |
| By: | Arun G. Chandrasekhar; Vasu Chaudhary; Benjamin Golub; Matthew O. Jackson |
| Abstract: | Social and economic networks are often multiplexed, meaning that people are connected by different types of relationships -- such as borrowing goods and giving advice. We make three contributions to the study of multiplexing. First, we document empirical multiplexing patterns in Indian village data: relationships such as socializing, advising, helping, and lending are correlated but distinct, while commonly used proxies for networks based on ethnicity and geography are nearly uncorrelated with actual relationships. Second, we examine how these layers and their overlap affect information diffusion in a field experiment. The advice network is the best predictor of diffusion, but combining layers improves predictions further. Villages with greater overlap between layers (more multiplexing) experience less overall diffusion. This leads to our third contribution: developing a model and theoretical results about diffusion in multiplex networks. Multiplexing slows the spread of simple contagions, such as diseases or basic information, but can either impede or enhance the spread of complex contagions, such as new technologies, depending on their virality. Finally, we identify differences in multiplexing by gender and connectedness. These have implications for inequality in diffusion-mediated outcomes such as access to information and adherence to norms. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.11957 |
| By: | Tommaso Di Francesco; Daniel Torren Peraire |
| Abstract: | This paper investigates the interplay between information diffusion in social networks and its impact on financial markets with an Agent-Based Model (ABM). Agents receive and exchange information about an observable stochastic component of the dividend process of a risky asset \`a la Grossman and Stiglitz. A small proportion of the network has access to a private signal about the component, which can be clean (information) or distorted (misinformation). Other agents are uninformed and can receive information only from their peers. All agents are Bayesian, adjusting their beliefs according to the confidence they have in the source of information. We examine, by means of simulations, how information diffuses in the network and provide a framework to account for delayed absorption of shocks, that are not immediately priced as predicted by classical financial models. We investigate the effect of the network topology on the resulting asset price and evaluate under which condition misinformation diffusion can make the market more inefficient. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.16269 |
| By: | Chase Englund; Zach Modig |
| Abstract: | This paper uses data on bank connections with service providers to construct a representation of an operational network used to facilitate the sending of Fedwire transactions. Our data contains 227 connections between 215 banks (mostly community banks, but also some large banks) and four unique payment products used by the firms to send and receive Fedwire transactions. By constructing such an operational network between banks and payment providers, we can perform multiple analyses that are useful in operational resilience considerations. First, we use the mean daily Fedwire volume for each bank to create a dollar estimate of the "operational risk exposure" associated with each service platform based on its bank clients. Second, we examine how these bank payment risk exposure estimates compare with other, publicly available benchmarks, since payment data are usually confidential. Last, we use the network model to conduct analysis on network concentration, which provides an example of how such networks could be used in analyzing the likely impact of operational outages. Our results indicate that data on service provider connections such as that we analyze can provide important insights into the extent to which payment network resilience mitigates risk to the financial sector. Our results also indicate that several publicly available benchmarks can serve as substitutes (with certain caveats) for payments data in estimating payment risk exposure. |
| Date: | 2025–01–03 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfn:2025-01-03 |
| By: | Dou, Liyu (School of Economics, Singapore Management University); KASTL, Jakub (Department of Economics, Princeton University, NBER and CEPR); LAZAREV, John (Stern Economics, New York University) |
| Abstract: | We develop a framework for quantifying delay propagation in airline networks that combines structural modeling and machine learning methods together to estimate causal objects of interest. Using a large comprehensive data set on actual delays and a model-selection algorithm (elastic net) we estimate a weighted directed graph of delay propagation for each major airline in the US and derive conditions under which the estimates of the propagation coefficients are causal. We use these estimates to decompose the airline performance into “luck” and “ability.” We find that luck may explain about 38% of the performance difference between Delta and American in our data. We further use these estimates to describe how network topology and other airline network characteristics (such as aircraft fleet heterogeneity) affect the expected delays. |
| Keywords: | Airline Networks; Shock Propagation; Elastic Net |
| JEL: | C50 L14 L93 |
| Date: | 2025–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:ris:smuesw:2024_014 |
| By: | Lutz Honvehlmann |
| Abstract: | Weighted reciprocity between two agents can be defined as the minimum of sending and receiving value in their bilateral relationship. In financial networks, such reciprocity characterizes the importance of individual banks as both liquidity absorber and provider, a feature typically attributed to large, intermediating dealer banks. In this paper we develop an exponential random graph model that can account for reciprocal links of each node simultaneously on the topological as well as on the weighted level. We provide an exact expression for the normalizing constant and thus a closed-form solution for the graph probability distribution. Applying this statistical null model to Italian interbank data, we find that before the great financial crisis (i) banks displayed significantly more weighted reciprocity compared to what the lower-order network features (size and volume distributions) would predict (ii) with a disappearance of this deviation once the early periods of the crisis set in, (iii) a trend which can be attributed in particular to smaller banks (dis)engaging in bilateral high-value trading relationships. Moreover, we show that neglecting reciprocal links and weights can lead to spurious findings of triadic relationships. As the hierarchical structure in the network is found to be compatible with its transitive but not with its intransitive triadic sub-graphs, the interbank market seems to be well-characterized by a hierarchical core-periphery structure enhanced by non-hierarchical reciprocal trading relationships. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.10329 |
| By: | Leonardo Niccol\`o Ialongo; Sylvain Bangma; Fabian Jansen; Diego Garlaschelli |
| Abstract: | The structure of the supply chain network has important implications for modelling economic systems, from growth trajectories to responses to shocks or natural disasters. However, reconstructing firm-to-firm networks from available information poses several practical and theoretical challenges: the lack of publicly available data, the complexity of meso-scale structures, and the high level of heterogeneity of firms. With this work we contribute to the literature on economic network reconstruction by proposing a novel methodology based on a recently developed multi-scale model. This approach has three main advantages over other methods: its parameters are defined to maintain statistical consistency at different scales of node aggregation, it can be applied in a multi-scale setting, and it is computationally more tractable for very large graphs. The consistency at different scales of aggregation, inherent to the model definition, is preserved for any hierarchy of coarse-grainings. The arbitrariness of the aggregation allows us to work across different scales, making it possible to estimate model parameters even when node information is inconsistent, such as when some nodes are firms while others are countries or regions. Finally, the model can be fitted at an aggregate scale with lower computational requirements, since the parameters are invariant to the grouping of nodes. We assess the advantages and limitations of this approach by testing it on two complementary datasets of Dutch firms constructed from inter-client transactions on the bank accounts of two major Dutch banking institutions. We show that the model reliably predicts important topological properties of the observed network in several scenarios of practical interest and is therefore a suitable candidate for reconstructing firm-to-firm networks at scale. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.16122 |
| By: | Jang, Haneul; Redhead, Daniel |
| Abstract: | Cultural transmission across generations is key to cumulative cultural evolution. While several mechanisms—such as vertical, horizontal, and oblique transmission—have been studied for decades, how these mechanisms change across the life course, beyond childhood. Furthermore, it remains under-explored whether different mechanisms apply to distinct forms of learning processes: long-term learning—where individuals invest time and effort to acquire skills—and short-term learning—where individuals share information of immediate use. To investigate the network structure of these two types of knowledge transmission—long-term learning of foraging skills and short-term learning of food location information—we present social network data (1, 633 nominations) collected from all 132 inhabitants (aged 4 to 75) of a BaYaka community in the Republic of the Congo. Applying latent network models that estimate and adjust for measurement biases typical to self-reported data, we find that the demographic structure of a population—age distribution, sex, kinship, and marriage—shapes the dynamics of community-wide knowledge transmission. Foraging skills are transmitted within smaller, sparser networks with limited reciprocity, whereas food location information is exchanged more widely and reciprocally among peers. Both long-term and short-term knowledge transmission extend into adulthood, with adults learning from older adults, peers, and marital partners, and sharing knowledge with younger generations. Crucially, individuals tend to report more accurately about the partners with whom they shared knowledge than about those from whom they received knowledge. Our findings provide important empirical evidence on how community-wide cultural transmission is structured by demography and perception, and how these factors operate across different learning processes in a real-world foraging society. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130134 |
| By: | Valentin Flietner (PwC and Tinbergen Institute); Bernd Heidergott (Vrije Universiteit Amsterdam and Tinbergen Institute); Frank den Hollander (Leiden University); Ines Lindner (Vrije Universiteit Amsterdam and Tinbergen Institute); Azadeh Parvaneh (Leiden University); Holger Strulik (University of Göttingen) |
| Abstract: | In this paper, we advance the network theory of aging and mortality by developing a causal mathematical model for the mortality rate. First, we show that in large networks, where health deficits accumulate at nodes representing health indicators, the modeling of network evolution with Poisson processes is universal and can be derived from fundamental principles. Second, with the help of two simplifying approximations, which we refer to as mean-field assumption and homogeneity assumption, we provide an analytical derivation of Gompertz law under generic and biologically relevant conditions. We identify the parameters in Gompertz law as a function of the parameters driving the evolution of the network, and illustrate our computations with simulations and analytic approximations. |
| JEL: | I10 J10 |
| Date: | 2024–12–20 |
| URL: | https://d.repec.org/n?u=RePEc:tin:wpaper:20240079 |
| By: | Ahmet Duhan Yassa; Kamil Yýlmaz |
| Abstract: | This study investigates the propagation of foreign trade-related shocks through domestic supply chain networks. By combining firm-level customs data with domestic firm-to-firm transaction data, we demonstrate that importer and exporter firms occupy a central position within Türkiye’s domestic supply chain network. These firms interact with numerous other firms and account for a significant share of total domestic trade, facilitating the transmission of external shocks through the supply chain. Our empirical analysis reveals that Turkish firms’ indirect exposure to exchange rate fluctuations, imported input price changes, and foreign demand shocks—via their suppliers or customers—is at least as significant as their direct exposure in explaining variations in gross profitability. Given the close relationship between gross profitability and value added, our results suggest that foreign trade-related shocks can significantly impact gross domestic product (GDP), both directly and indirectly. |
| Keywords: | Production network, Supply chains, Foreign trade, GDP volatility, Centrality |
| JEL: | B17 D22 D24 L14 L61 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:wpaper:2503 |
| By: | Zhesheng Qiu; Yicheng Wang; Le Xu; Francesco Zanetti |
| Abstract: | This paper studies the design of monetary policy in small open economies with domestic and cross-border production networks and nominal rigidities. The monetary policy that closes the domestic output gap is nearly optimal and is implemented by stabilizing the aggregate inflation index those weights sectoral inflation according to the sector’s roles as a supplier of inputs and a net exporter of products within the international production networks. To close the output gap, monetary policy should assign large weights to inflation in sectors with small direct or indirect (i.e., via the downstream sectors) import shares and failing to account for the cross-border production networks overemphasizes inflation in sectors that export intensively directly and indirectly (i.e., via the downstream sectors). We validate our theoretical results using the World Input-Output Database and show that the monetary policy that closes the output gap outperforms alternative policies that abstract from the openness of the economy or the input-output linkages. |
| Keywords: | production networks, small open economy, monetary policy |
| JEL: | C67 E52 F41 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2025-03 |
| By: | Bora Kim |
| Abstract: | In estimating spillover effects under network interference, practitioners often use linear regression with either the number or fraction of treated neighbors as regressors. An often overlooked fact is that the latter is undefined for units without neighbors (``isolated nodes"). The common practice is to impute this fraction as zero for isolated nodes. This paper shows that such practice introduces bias through theoretical derivations and simulations. Causal interpretations of the commonly used spillover regression coefficients are also provided. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.05919 |
| By: | Marlène Koffi; Roland Pongou; Leonard Wantchekon |
| Abstract: | This paper investigates the existence of racial disparities in the dissemination of ideas using the paper citation network in economics. Exploiting a comprehensive dataset of over 330, 000 publications from 1950 to 2021, combined with manually collected data from the CVs of thousands of economists, we document that papers authored by non-White scholars (Black, Hispanic, or Asian) receive 5.1% to 9.6% fewer citations than those authored by White scholars. The citation gap remains or even amplifies with increasing author seniority and conventional quality indicators and is especially pronounced for Black authors. Moreover, papers authored by non-White scholars are less likely to serve as citation bridges and are less often cited by highly cited papers as measured by the centrality indexes, limiting both their direct and indirect influence. Our analysis indicates that this disparity is not attributable to differences in research quality, author ability, or visibility. Rather, it is largely driven by homophily in citation patterns and racial clusters in networks, where scholars tend to cite authors from their racial group. These findings can be rationalized by a simple theoretical model where citation costs and peer-review preferences influence citation behavior. Then, we provide suggestive evidence that reducing information friction—thereby lowering the cost of citing—could reduce the racial citation gap by up to 50%. Finally, using natural language processing, we highlight the complementarity across racial groups in research and discuss potential losses from racial barriers to idea diffusion. |
| JEL: | A14 I23 J15 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33150 |
| By: | St\'ephane Bonhomme; Koen Jochmans; Martin Weidner |
| Abstract: | A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading examples where this is the case are models for panel and network data that feature fixed effects. In this paper, we show how, in the conditional-likelihood setting, estimating equations can be constructed that are orthogonal to any chosen order. Combining these equations with sample splitting yields higher-order bias-corrected estimators of target parameters. In an empirical application we apply our method to a fixed-effect model of team production and obtain estimates of complementarity in production and impacts of counterfactual re-allocations. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.10304 |
| By: | Jianhua Yao; Yuxin Dong; Jiajing Wang; Bingxing Wang; Hongye Zheng; Honglin Qin |
| Abstract: | This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph and the extraction of temporal information from historical price sequences. It also highlights the design of a graph convolution operation and a temporal attention aggregator to model the macro market state. The integration of these features results in a comprehensive stock prediction model that addresses the challenges of utilizing stock relationship data and modeling hierarchical attributes in the stock market. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.06862 |
| By: | Minseog Oh; Donggyu Kim |
| Abstract: | In financial applications, we often observe both global and local factors that are modeled by a multi-level factor model. When detecting unknown local group memberships under such a model, employing a covariance matrix as an adjacency matrix for local group memberships is inadequate due to the predominant effect of global factors. Thus, to detect a local group structure more effectively, this study introduces an inverse covariance matrix-based financial adjacency matrix (IFAM) that utilizes negative values of the inverse covariance matrix. We show that IFAM ensures that the edge density between different groups vanishes, while that within the same group remains non-vanishing. This reduces falsely detected connections and helps identify local group membership accurately. To estimate IFAM under the multi-level factor model, we introduce a factor-adjusted GLASSO estimator to address the prevalent global factor effect in the inverse covariance matrix. An empirical study using returns from international stocks across 20 financial markets demonstrates that incorporating IFAM effectively detects latent local groups, which helps improve the minimum variance portfolio allocation performance. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2412.05664 |