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on Network Economics |
| By: | Vincent Boucher; Aristide Houndetoungan |
| Abstract: | We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer effects on students' academic achievement using the widely used Add Health database, and show that network data errors have a large downward bias on estimated peer effects. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08145 |
| By: | Toygar T. Kerman; Anastas P. Tenev; Konstantin Zabarnyi |
| Abstract: | We study a Bayesian persuasion setting in which a sender wants to persuade a critical mass of receivers by revealing partial information about the state to them. The homogeneous binary-action receivers are located on a communication network, and each observes the private messages sent to them and their immediate neighbors. We examine how the sender's expected utility varies with increased communication among receivers. We show that for general families of networks, extending the network can strictly benefit the sender. Thus, the sender's gain from persuasion is not monotonic in network density. Moreover, many network extensions can achieve the upper bound on the sender's expected utility among all networks, which corresponds to the payoff in an empty network. This is the case in networks reflecting a clear informational hierarchy (e.g., in global corporations), as well as in decentralized networks in which information originates from multiple sources (e.g., influencers in social media). Finally, we show that a slight modification to the structure of some of these networks precludes the possibility of such beneficial extensions. Overall, our results caution against presuming that more communication necessarily leads to better collective outcomes. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.09099 |
| By: | Nikhil Kumar |
| Abstract: | This paper presents a social learning model where the network structure is endogenously determined by signal precision and dimension choices. Agents not only choose the precision of their signals and what dimension of the state to learn about, but these decisions directly determine the underlying network structure on which social learning occurs. We show that under a fixed network structure, the optimal precision choice is sublinear in the agent's stationary influence in the network, and this individually optimal choice is worse than the socially optimal choice by a factor of $n^{1/3}$. Under a dynamic network structure, we specify the network by defining a kernel distance between agents, which then determines how much weight agents place on one another. Agents choose dimensions to learn about such that their choice minimizes the squared sum of influences of all agents: a network with equally distributed influence across agents is ideal. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.00249 |
| By: | Kensuke Sakamoto; Yuya Shimizu |
| Abstract: | OLS estimators are widely used in network experiments to estimate spillover effects via regressions on exposure mappings that summarize treatment and network structure. We study the causal interpretation and inference of such OLS estimators when both design-based uncertainty in treatment assignment and sampling-based uncertainty in network links are present. We show that correlations among elements of the exposure mapping can contaminate the OLS estimand, preventing it from aggregating heterogeneous spillover effects for clear causal interpretation. We derive the estimator's asymptotic distribution and propose a network-robust variance estimator. Simulations and an empirical application reveal sizable contamination bias and inflated spillover estimates. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.22989 |
| By: | Matthew O. Jackson; Agathe Pernoud |
| Abstract: | We examine optimal regulation of financial networks with debt interdependencies between financial firms. We first characterize when it is firms have an incentive to choose excessively risky portfolios and overly correlate their portfolios with those of their counterparties. We then characterize how optimal regulation depends on a firm's financial centrality and its available investment opportunities. In standard core-periphery networks, optimal regulation depends non-monotonically on the correlation of banks' investments, with maximal restrictions for intermediate levels of correlation. Moreover, it can be uniquely optimal to treat banks asymmetrically: restricting the investments of one core bank while allowing an otherwise identical core bank (in all aspects, including network centrality) to invest freely. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.16648 |
| By: | Arthur Lewbel; Xi Qu; Xun Tang |
| Abstract: | We propose an adjusted 2SLS estimator for social network models when reported binary network links are misclassified (some zeros reported as ones and vice versa) due, e.g., to survey respondents' recall errors, or lapses in data input. We show misclassification adds new sources of correlation between the regressors and errors, which makes all covariates endogenous and invalidates conventional estimators. We resolve these issues by constructing a novel estimator of misclassification rates and using those estimates to both adjust endogenous peer outcomes and construct new instruments for 2SLS estimation. A distinctive feature of our method is that it does not require structural modeling of link formation. Simulation results confirm our adjusted 2SLS estimator corrects the bias from a naive, unadjusted 2SLS estimator which ignores misclassification and uses conventional instruments. We apply our method to study peer effects in household decisions to participate in a microfinance program in Indian villages. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.07343 |
| By: | Bijan Aghdasi; Abhijit Tagade |
| Abstract: | Do markets price knowledge spillovers? We show that patent grants influence the stock returns of firms that are connected through technological knowledge dependencies. Using directed patent citations among publicly listed companies in the United States, we construct a granular measure of each firm's exposure to new patents granted to its technologically upstream firms. Patents granted to these upstream companies significantly boost its abnormal stock returns during the week of the grant. We find that these financial spillovers are predominantly localized within a firm's immediate technological connections. Additionally, we provide a novel empirical decomposition of financial spillovers generated from patent grants, by distinguishing those spillovers emerging from sources of technological knowledge, from those emerging from product market rivals (negative effect) and suppliers (positive effect). Our findings are robust to alternative specifications and placebo tests, and they suggest that technological knowledge spillovers create important market-priced ties between firms that are not fully captured by traditional product market relationships. |
| Keywords: | innovation, networks, spillovers, patents, stock returns, supply chains |
| Date: | 2025–08–13 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2117 |
| By: | Kota Murayama |
| Abstract: | This paper studies how payoff heterogeneity affects the value of information in beauty contest games. I show that public information is detrimental to welfare if and only if agents' Katz-Bonacich centralities exhibit specific forms of heterogeneity, stemming from the network of coordination motives. A key insight is that agents may value the commonality of information so differently that some are harmed by their neighbors knowing what others know. Leveraging this insight, I also show that when the commonality of information is endogenously determined through information sharing, the equilibrium degree of information sharing can be inefficiently low, even without sharing costs. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.17660 |
| By: | Francesco Slataper; Luis Menéndez; Daniel Montolio; Hannes Mueller |
| Abstract: | This article exploits data from a political conflict between language groups to show how political events can rapidly redefine how these groups interact on social media. Leveraging on a unique dataset of 26 million retweets by 120 000 Catalan- and Spanish- speaking Twitter users, we estimate individual exposure to tweets with a network-based model. We then compare two shocks in the same region and year: the Barcelona terror attack and the Catalan independence referendum of 2017. The referendum, and related police violence, triggered a sharp, symmetric jump in retweeting across language groups. The terror attack, by contrast, did not lead to a similar realignment. |
| Keywords: | echo-chambers, ethno-linguistic conflict, polarization, political conflict, retweet behavior, social media, social networks |
| JEL: | D74 C55 C45 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bge:wpaper:1505 |
| By: | Alexandra Rottenkolber; Ola Ali; Gergely Mónus; Jiaxuan Li; Jisu Kim (Max Planck Institute for Demographic Research, Rostock, Germany); Daniela Perrotta (Max Planck Institute for Demographic Research, Rostock, Germany); Aliakbar Akbaritabar (Max Planck Institute for Demographic Research, Rostock, Germany) |
| Abstract: | Mobility of researchers is a key driver of knowledge diffusion, innovation, and international collaboration. While prior research highlights the role of networks in shaping migration flows, the extent to which personal and institutional ties influence the direction of scientific mobility remains unclear. This study leverages large-scale digital trace data from Scopus, capturing complete mobility trajectories, co-authorship networks, and collaboration histories of 172, 000 authors. Using multinomial logistic regressions and discrete choice modelling, we systematically assess the effects of first- and second-order co-authorship ties and institutional linkages on scholars’ mobility outcomes, focusing on their first career move. Our findings demonstrate that not only first-, but also second-order co-authorship ties — connections to a scholar’s collaborators’ collaborators — are a strong predictor for the direction of a move. Scholars with extensive individual professional networks, as well as those migrating abroad, are more likely to move along individual ties. In contrast, those from prestigious institutions, as well as those moving nationally, tend to follow institutional routes more often. Discrete choice models further confirm that both individual and institutional ties increase the probability of moving to specific research institutions, with individual connections being more influential than institutional ones. This research provides empirical evidence for the role that individual and institutional connections play in shaping high-skilled labour mobility. Furthermore, it has important implications for migration theory and policy, emphasising the need to support national and international collaborative networks, both individual and institutional, to foster scientific exchange. |
| JEL: | J1 Z0 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:dem:wpaper:wp-2025-028 |
| By: | Alessandro Ferrari; Lorenzo Pesaresi |
| Abstract: | Despite growing policy interest, the determinants of supply chain resilience are still not well understood. We propose a new theory of supply chain formation with compatibility frictions: only compatible inputs can be used in final good production. Intermediate producers choose the degree of specialization of their goods, trading off higher productivity against a lower share of compatible final producers. We model supply chains as complex production processes in which multiple complementary inputs must be sourced for final production to take place. Specialization choices, production complexity, and search frictions jointly determine supply chain resilience. Relative to the efficient allocation, the equilibrium is characterized by over-specialization due to a novel network externality arising from the interplay between frictional markets, endogenous specialization, and complex production. Over-specialization makes supply chains more productive in normal times but less resilient to disruptions than socially desirable. We show how a targeted transaction subsidy can decentralize efficient resilience in supply chains, and examine the implications of setting compatibility standards. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08981 |
| By: | Adam Abdel Kader Touré; Martin Trépanier; Thierry Warin |
| Abstract: | This study investigates the evolving dynamics of economic connectedness within the Great Lakes–St. Lawrence (GLSL) region, focusing on the manufacturing sector across eight U.S. states and two Canadian provinces. Leveraging monthly manufacturing employment growth rates from January 1990 to December 2024, the analysis employs a Vector Autoregressive (VAR) model combined with Elastic Net regularization to capture the interdependencies and directional spillovers among these highly integrated regional economies. Through forecast error variance decomposition, the approach identifies the contributions of shocks originating in any given state or province to fluctuations in the others, thereby quantifying both the magnitude of influence (“Connectedness To”) and the degree of exposure (“Connectedness From”). The results reveal a complex yet discernible network of industrial linkages, with states such as Ohio and Indiana emerging as consistent net transmitters of shocks and provinces like Quebec displaying relatively lower susceptibility to external disturbances. A rolling window estimation confirms that these patterns vary over time, frequently intensifying during episodes of macroeconomic stress, such as the 2008–2009 financial crisis and the onset of the COVID-19 pandemic. The findings highlight the significance of coordinated policy interventions aimed at stabilizing key nodes in the network and underscore the importance of diversification and risk management strategies for entities that exhibit heightened exposure. Cette étude examine l'évolution de la dynamique des liens économiques dans la région des Grands Lacs et du Saint-Laurent (GLSL), en se concentrant sur le secteur manufacturier dans huit États américains et deux provinces canadiennes. S'appuyant sur les taux de croissance mensuels de l'emploi dans le secteur manufacturier de janvier 1990 à décembre 2024, l'analyse utilise un modèle vectoriel autorégressif (VAR) combiné à une régularisation Elastic Net afin de saisir les interdépendances et les retombées directionnelles entre ces économies régionales hautement intégrées. La décomposition de la variance des erreurs de prévision permet d’évaluer l’influence (« Connectedness To ») et l’exposition (« Connectedness From ») de chaque juridiction aux chocs régionaux. Les résultats révèlent un réseau complexe mais discernable de liens industriels, avec des États tels que l'Ohio et l'Indiana qui apparaissent comme des transmetteurs nets constants de chocs et des provinces comme le Québec qui affichent une sensibilité relativement faible aux perturbations externes. Une estimation par fenêtre glissante confirme que ces tendances varient dans le temps, s'intensifiant fréquemment lors d'épisodes de tension macroéconomique, tels que la crise financière de 2008-2009 et le début de la pandémie de COVID-19. Les résultats soulignent l'importance des interventions politiques coordonnées visant à stabiliser les nœuds clés du réseau et mettent en évidence l'importance des stratégies de diversification et de gestion des risques pour les entités fortement exposées |
| Keywords: | Connectedness, Economic Integration, Labor, Great Lakes Saint Lawrence, Connectivité, Intégration économique, Secteur manufacturier, Marché du travail, Grands Lacs et Saint-Laurent |
| Date: | 2025–09–02 |
| URL: | https://d.repec.org/n?u=RePEc:cir:cirwor:2025s-25 |