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on Network Economics |
| By: | Yuchen Guo; Matthew O. Jackson; Ruixue Jia |
| Abstract: | Do social networks and peer influence shape major life decisions in highly polarized settings? We explore this question by examining how peers influenced the allegiances of West Point cadets during the American Civil War. Leveraging quasi-random variations in the proportion of cadets from Free States, we analyze how these differences affected decisions about which army to join. We find that a higher proportion of classmates from Free States significantly increased the likelihood that cadets from Slave States joined the Union Army, while almost all cadets from Free States joined the Union Army (if they decided to join the war). We further examine how cadets' decisions affected their military rank and career outcomes. Our findings highlight that peers still influence choices even when they are life-altering and occur during periods of extreme polarization. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.09419 |
| By: | Riccardo Milocco; Fabian Jansen; Diego Garlaschelli |
| Abstract: | In machine learning, graph embedding algorithms seek low-dimensional representations of the input network data, thereby allowing for downstream tasks on compressed encodings. Recently, within the framework of network renormalization, multi-scale embeddings that remain consistent under an arbitrary aggregation of nodes onto block-nodes, and consequently under an arbitrary change of resolution of the input network data, have been proposed. Here we investigate such multi-scale graph embeddings in the modified context where the input network is not entirely observable, due to data limitations or privacy constraints. This situation is typical for financial and economic networks, where connections between individual banks or firms are hidden due to confidentiality, and one has to probabilistically reconstruct the underlying network from aggregate information. We first consider state-of-the-art network reconstruction techniques based on the maximum-entropy principle, which is designed to operate optimally at a fixed resolution level. We then discuss the limitations of these methods when they are used as graph embeddings to yield predictions across different resolution levels. Finally, we propose their natural 'renormalizable' counterparts derived from the distinct principle of scale invariance, yielding consistent graph embeddings for multi-scale network reconstruction. We illustrate these methods on national economic input-output networks and on international trade networks, which can be naturally represented at multiple levels of industrial and geographic resolution, respectively. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20706 |
| By: | Nobuyuki Hanaki (Institute of Social and Economic Research, Osaka University); Giulia Iori (City, University of London); Pietro Vassallo (Bank of Italy) |
| Abstract: | In this paper we present the results of experiments and computational analyses of trading in decentralized markets with asymmetric information. We consider three trading configurations, namely the ring, the small-world, and the Erdös-Rényi random network, which allow us to introduce heterogeneity in nodes degree, centrality and clustering, while keeping the number of possible trading relationships fixed. We analyze how the prices of a traded risky asset and the profits of differently informed traders are affected by the distribution of the trading links, and by the location of the traders in the network. This allows us to infer key features in the dynamics of learning and information diffusion through the market. Experimental results show that learning is enhanced locally by clustering rather than degree, pointing to a learning dynamic driven by interdependent, successive trading events, rather than independent exposures to informed traders. By calibrating a behavioural agent-based model to the experimental data we are able to estimate the speed at which agents learn and to locate where information accumulates in the market. Interestingly, simulations indicate that proximity to the insiders leads to more information in regular networks but not so in random networks. |
| Keywords: | OTC markets; Asymmetric information; Learning; Information diffusion; Networks; Insider trading |
| JEL: | G1 C6 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:12 |
| By: | Simone Alfarano (Universitat Jaume I); Albert Banal-Estañol (Universitat Pompeu Fabra; Barcelona School of Economics; Barcelona School of Management; City, University of London); Eva Camacho (Universitat Jaume I); Giulia Iori (Ca’ Foscari University of Venice; City, University of London); Burcu Kapar (University of Wollongong); Rohit Rahi (London School of Economics) |
| Abstract: | We consider a setting in which privately informed agents are located in a network and trade a risky asset with other agents with whom they are directly connected. We compare the performance, both theoretically and experimentally, of a complete network (centralized market) to incomplete networks with differing levels of connectivity (decentralized markets). We show that decentralized markets can deliver higher informational efficiency, with prices closer to fundamentals, as well as higher welfare for mean-variance investors. |
| Keywords: | Networks, heuristic learning, informational efficiency, experimental asset markets |
| JEL: | C92 D82 G14 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:13 |
| By: | Andrea Benso |
| Abstract: | We consider a repeated game in which players, considered as nodes of a network, are connected. Each player observes her neighbors' moves only. Thus, monitoring is private and imperfect. Players can communicate with their neighbors at each stage; each player, for any subset of her neighbors, sends the same message to any player of that subset. Thus, communication is local and both public and private. Both communication and monitoring structures are given by the network. The solution concept is perfect Bayesian equilibrium. In this paper we show that a folk theorem holds if and only if the network is 2-connected for any number of players. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.10148 |
| By: | Pablo Acevedo; Elías Albagli; Gonzalo García-Trujillo; María Antonia Yung |
| Abstract: | This project uses unique Chilean administrative data to shed light on how production networks might play a key role in shaping the macroeconomic impacts of green transition policies. First, using customs and firm-to-firm transaction data that covers the universe of firms in Chile, we build the fossil fuel consumption and the direct CO2 emissions at the firm, sectoral, and aggregate levels. In line with the official national sources, the electricity generation sector is the most important contributor to aggregate CO2 emissions, followed by the manufacturing, transport, and mining sectors. Then, we study the role of input-output linkages in propagating CO2 emissions to the rest of the economy. To do so, we construct the production network and the carbon footprint at the firm level using firm-to-firm transaction data from the Chilean IRS, and we validate our results with the input-output tables approach used in the literature. The results show that the electricity generation sector is central in the network, with potentially important downstream spillover effects, while the mining sector is located in the outer part of the network with rich upstream connections. Also, we show that the copper mining industry is the most exposed one to a carbon tax scheme implemented on all the firms in the economy and also to one that only targets the electricity generation sector. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1047 |
| By: | Mateusz Wilinski; Juho Kanniainen |
| Abstract: | There are multiple explanations for stylized facts in high-frequency trading, including adaptive and informed agents, many of which have been studied through agent-based models. This paper investigates an alternative explanation by examining whether, and under what circumstances, interactions between traders placing limit order book messages can reproduce stylized facts, and what forms of interaction are required. While the agent-based modeling literature has introduced interconnected agents on networks, little attention has been paid to whether specific trading network topologies can generate stylized facts in limit order book markets. In our model, agents are strictly zero-intelligence, with no fundamental knowledge or chartist-like strategies, so that the role of network topology can be isolated. We find that scale-free connectivity between agents reproduces stylized facts observed in markets, whereas no-interaction does not. Our experiments show that regular lattices and Erdos-Renyi networks are not significantly different from the no-interaction baseline. Thus, we provide a completely new, potentially complementary, explanation for the emergence of stylized facts. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20672 |
| By: | Yin, Wei (School of Economics and Management, Southeast University, Nanjing, China); Wu, Fan (School of Economics and Management, Southeast University, Nanjing, China); Zhou, Peng (Cardiff Business School, Cardiff University, Cardiff, UK); Kirkulak-Uludag, Berna (Faculty of Business, Dokuz Eylul University, Ä°zmir, Turkiye) |
| Abstract: | The cryptocurrency market is characterized by rapid risk transmission, strong interconnectedness, and substantial downside risk, driven by technical similarities among major cryptocurrencies and herd behavior of investors. To analyze these dynamics, we construct a directed, weighted cryptocurrency risk spillover network consisting of 20 leading cryptocurrencies, using the DCC-GARCH-Copula-ΔCoVaR model. The market is segmented into six groups based on the interdependence of market values. The study evaluates the resilience of the network under a range of scenarios, including both random failures and intentional attacks, and validates the findings through a real-world case study of the 2022 Luna collapse. The results show that the overall resilience of the cryptocurrency risk network has improved as the market matures. Leading cryptocurrencies act as net risk receivers, enhancing the network's robustness. In contrast, active cryptocurrencies can accelerate the contagion of risks across the market. These findings suggest that effective risk management in the cryptocurrency market requires not only the stabilization of major cryptocurrencies but also the ongoing monitoring of smaller, high-activity cryptocurrencies. |
| Keywords: | Cryptocurrency, Risk spillover, Complex network, Resilience |
| JEL: | G11 G12 G15 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2025/18 |
| By: | Yasutaka Koike-Mori; Antonio Martner |
| Abstract: | We investigate the role of multi-product firms in shaping resource misallocation within production networks and its impact on aggregate total factor productivity (TFP) growth. Using administrative data on product transactions between all the for-mal Chilean firms, we provide evidence that demand shocks to one product affect the production of other products within the same firm, suggesting firms engage in joint production. We develop a framework to measure resource misallocation in production networks with joint production, deriving non-parametric sufficient statistics to quantify these effects. Applying the framework to Chile, we find that reallocation effects, considering joint production, explain 86% of observed aggregate TFP growth for the 2016-2022 period. Ignoring joint production leads to overestimating resource misallocation. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1022 |
| By: | Atika Aouri; Philipp Otto |
| Abstract: | We introduce a heterogeneous spatiotemporal GARCH model for geostatistical data or processes on networks, e.g., for modelling and predicting financial return volatility across firms in a latent spatial framework. The model combines classical GARCH(p, q) dynamics with spatially correlated innovations and spatially varying parameters, estimated using local likelihood methods. Spatial dependence is introduced through a geostatistical covariance structure on the innovation process, capturing contemporaneous cross-sectional correlation. This dependence propagates into the volatility dynamics via the recursive GARCH structure, allowing the model to reflect spatial spillovers and contagion effects in a parsimonious and interpretable way. In addition, this modelling framework allows for spatial volatility predictions at unobserved locations. In an empirical application, we demonstrate how the model can be applied to financial stock networks. Unlike other spatial GARCH models, our framework does not rely on a fixed adjacency matrix; instead, spatial proximity is defined in a proxy space constructed from balance sheet characteristics. Using daily log returns of 50 publicly listed firms over a one-year period, we evaluate the model's predictive performance in a cross-validation study. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.20101 |
| By: | Ziang Huang; Huashan Chen |
| Abstract: | Economic complexity - a group of dimensionality-reduction methods that apply network science to trade data - represented a paradigm shift in development economics towards materializing the once-intangible concept of capabilities as inferrable and quantifiable. Measures such as the Economic Complexity Index (ECI) and the Product Space have proven their worth as robust estimators of an economy's subsequent growth; less obvious, however, is how they have come to be so. Despite ECI drawing its micro-foundations from a combinatorial model of capabilities, where a set of homogeneous capabilities combine to form products and the economies which can produce them, such a model is consistent with neither the fact that distinct product classes draw on distinct capabilities, nor the interrelations between different products in the Product Space which so much of economic complexity is based upon. In this paper, we extend the combinatorial model of economic complexity through two innovations: an underlying network which governs the relatedness between capabilities, and a production function which trades the original binary specialization function for a fine-grained, product-level output function. Using country-product trade data across 216 countries, 5000 products and two decades, we show that this model is able to accurately replicate both the characteristic topology of the Product Space and the complexity distribution of countries' export baskets. In particular, the model bridges the gap between the ECI and capabilities by transforming measures of economic complexity into direct measures of the capabilities held by an economy - a transformation shown to both improve the informativeness of the Economic Complexity Index in predicting economic growth and enable an interpretation of economic complexity as a proxy for productive structure in the form of capability substitutability. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.21616 |
| By: | Xin Dong; Jose Ventura; Vikash V. Gayah |
| Abstract: | Ride-hailing platforms (e.g., Uber, Lyft) have transformed urban mobility by enabling ride-sharing, which holds considerable promise for reducing both travel costs and total vehicle miles traveled (VMT). However, the fragmentation of these platforms impedes system-wide efficiency by restricting ride-matching to intra-platform requests. Cross-platform collaboration could unlock substantial efficiency gains, but its realization hinges on fair and sustainable profit allocation mechanisms that can align the incentives of competing platforms. This study introduces a graph-theoretic framework that embeds profit-aware constraints into network optimization, facilitating equitable and efficient cross-platform ride-sharing. Within this framework, we evaluate three allocation schemes -- equal-profit-based, market-share-based, and Shapley-value-based -- through large-scale simulations. Results show that the Shapley-value-based mechanism consistently outperforms the alternatives across six key metrics. Notably, system efficiency and rider service quality improve with increasing demand, reflecting clear economies of scale. The observed economies of scale, along with their diminishing returns, can be understood with the structural evolution of rider-request graphs, where super-linear edge growth expands feasible matches and sub-linear degree scaling limits per-rider connectivity. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.19192 |
| By: | Sahil Loomba; Dean Eckles |
| Abstract: | In settings where units' outcomes are affected by others' treatments, there has been a proliferation of ways to quantify effects of treatments on outcomes. Here we describe how many proposed estimands can be represented as involving one of two ways of averaging over units and treatment assignments. The more common representation often results in quantities that are irrelevant, or at least insufficient, for optimal choice of policies governing treatment assignment. The other representation often yields quantities that lack an interpretation as summaries of unit-level effects, but that we argue may still be relevant to policy choice. Among various estimands, the expected average outcome -- or its contrast between two different policies -- can be represented both ways and, we argue, merits further attention. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.14391 |
| By: | Michael Ewens; Xavier Giroud |
| Abstract: | We introduce a novel measure of corporate hierarchies for over 2, 500 U.S. public firms. This measure is obtained from online resumes of 16 million employees and a network estimation technique that allows us to identify hierarchical layers. Equipped with this measure, we document several facts about corporate hierarchies. Firms have on average ten hierarchical layers and a pyramidal organizational structure. More hierarchical firms have a more educated workforce, higher internal promotion rates, and longer employee tenure. Their operating performance is higher, but they face higher administrative costs. They are more active acquirers and produce more patents, but not higher-quality patents. They exhibit lower stock return volatility and more stable cash flows. We also examine how companies adjust their hierarchies in response to demand and knowledge shocks. We find that biotech companies increased their number of layers following the Covid-19 pandemic, while companies flattened their hierarchies following the adoption of artificial intelligence (AI) technologies. These findings are consistent with the theoretical predictions of existing models of corporate hierarchies. |
| JEL: | D21 D23 G3 J24 L22 M12 M51 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34162 |