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on Network Economics |
| By: | Zenou, Yves (Monash University) |
| Abstract: | This paper reviews the theoretical and empirical foundations of peer and network effects, aiming to bridge insights from both literatures. We first analyze the microfoundations of peer effects through linear–quadratic network games, linking equilibrium behavior to network centrality and highlighting the role of key players. Then, we examine the main identification challenges in linear-in-means models—reflection, correlated effects, and sorting—and show how introducing explicit network structures can help address them. We also review reduced-form strategies based on within-school cohort composition, exposure to peers’ shocks, random assignment, and exogenous variation in network links. Finally, we discuss how structural models of network formation and individual effort choices can resolve endogeneity concerns. The paper concludes with recent advances on non-linear and multiplex interactions, where individuals respond to specific peers and operate across multiple, interdependent layers. |
| Keywords: | social interactions, identification, network games, centrality, multiplex networks, non-linearities |
| JEL: | A14 C57 D85 Z13 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18501 |
| By: | Wayne Yuan Gao; Yi Niu |
| Abstract: | This paper establishes (set) identification results in a dynamic dyadic network formation model with time-varying observed covariates, lagged local network statistics, and unobserved heterogeneity in the form of fixed effects. Our framework accommodates observed-covariate homophily, transitivity through common friends, second-order or indirect-friend effects, and more general local subgraph statistics within a single dynamic index model. The analysis combines two complementary ways of handling fixed effects: inequalities that integrate out time-invariant dyad heterogeneity by treating each dyad as a short panel, and signed-subgraph comparisons that difference out fixed effects algebraically through intertemporal variation within each dyad. We show that the semiparametric identifying restrictions can be sharpened using either or both of the following assumptions: (i) error distribution is serially independent with a known distribution, (ii) pairwise fixed effect takes the form of additive individual fixed effects. Combining (i) and (ii) under i.i.d. logit shocks, we obtain an exact conditional logit representation and provide sufficient conditions for point identification. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.07488 |
| By: | Ge Sun; Weisheng Zhang |
| Abstract: | Sampled network data are common in empirical research because collecting full network information is costly, but using sampled networks can lead to biased estimates. We propose a nonparametric imputation method for sampled networks and show that empirical analysis based on imputed networks yields consistent parameter estimates. Our approach imputes missing network links by combining a projection onto covariates with a local two-way fixed-effects regression, which avoids parametric assumptions, does not rely on low-rank restrictions, and flexibly accommodates both observed covariates and unobserved heterogeneity. We establish entrywise convergence rates for the imputed matrix and prove the consistency of GMM estimators based on the imputed network. We further derive the convergence rate of the corresponding estimator in the linear-in-means peer-effects model. Simulations show strong performance of our method both in terms of imputation accuracy and in downstream empirical analysis. We illustrate our method with an application to the microfinance network data of Banerjee et al. (2013). |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.03171 |
| By: | Diego Vallarino |
| Abstract: | We study identification and inference in nonlinear dynamic systems defined on unknown interaction networks. The system evolves through an unobserved dependence matrix governing cross-sectional shock propagation via a nonlinear operator. We show that the network structure is not generically identified, and that identification requires sufficient spectral heterogeneity. In particular, identification arises when the network induces non-exchangeable covariance patterns through heterogeneous amplification of eigenmodes. When the spectrum is concentrated, dependence becomes observationally equivalent to common shocks or scalar heterogeneity, leading to non-identification. We provide necessary and sufficient conditions for identification, characterize observational equivalence classes, and propose a semiparametric estimator with asymptotic theory. We also develop tests for network dependence whose power depends on spectral properties of the interaction matrix. The results apply to a broad class of economic models, including production networks, contagion models, and dynamic interaction systems. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.04961 |
| By: | Giulia Carallo; Roberto Casarin; Antonio Peruzzi |
| Abstract: | Count-weighted temporal networks often exhibit unequal dispersion in the edge weights, which cannot be fully explained by modelling observational heterogeneity through latent factors in the conditional mean. Therefore, we propose new dynamic network model classes exploiting the Generalized Poisson distribution to capture both under- and overdispersion. We consider three different dynamic specifications: latent factor dynamics, autoregressive dynamics, and latent position dynamics, and study some theoretical properties of the random networks, showing the impact of the dispersion parameter on the random network's connectivity. After discussing the parameter identification strategy, we present a Bayesian inference procedure along with a posterior sampling algorithm. A numerical illustration demonstrates the effectiveness of the designed algorithm and provides estimates of the misspecification bias when unequal dispersion is neglected. Our new models are then applied to two relevant dynamic datasets considered in previous studies: a set of bike-sharing dynamic networks and a set of dynamic media networks. Our results highlight the importance of explicitly modeling overdispersion for both an accurate in-sample fit and out-of-sample performance. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.05838 |
| By: | Mr. Andre O Santos |
| Abstract: | The objective of this paper is to assess the impact of single-name and widespread markdowns in syndicated leveraged loans on systemic risk in the euro area. Systemic risk implications of markdowns are simulated with network valuation models (as in Bardoscia et al. (2016)) and extended to accommodate recapitalization of systemically important banks. Key results indicate that, while the impact of single-name and widespread markdowns in syndicated leveraged loans on banks’ equity is not significant under a strong confidence in the banking system, their impact could be devastating if confidence is low. This could be mitigated by timely and calibrated recapitalization of systemically important banks. |
| Keywords: | Contagion; syndicated loans; euro area banks; liability networks; recapitalization. |
| Date: | 2026–03–27 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/061 |
| By: | Alessio Abeltino; Tiziano Bacaloni; Andrea Bernardini; Francesco Giancaterini; Andrea Pannone |
| Abstract: | Understanding how corporate control concentrates in modern ownership systems is crucial in an economy increasingly shaped by cross-border mergers and acquisitions. Rather than expanding productive capacity, these operations reorganize ownership and control over existing firms through complex transnational structures involving financial intermediaries, holding companies, and investment vehicles. As a result, corporate control may become highly concentrated even when formal ownership appears fragmented. This paper examines how foreign direct investments-related capital centralization reshapes firm-level governance by tracing how control converges on individual companies through multi-layered ownership networks. Focusing on two strategically relevant Italian firms, we show that control is rarely exercised solely by ultimate owners, but instead arises from the interaction of a small set of financially interconnected intermediaries operating along transnational ownership chains. The results show how small equity stakes translate into substantial governance power, highlighting the role of financial intermediation and raising implications for strategic autonomy and economic sovereignty in key sectors. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.02875 |
| By: | David de la Croix (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES)); Rossana Scebba (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES)); Chiara Zanardello (Institute for Advanced Study in Toulouse (IAST). Toulouse School of Economics, Universite Toulouse Capitole) |
| Abstract: | While good ideas can emerge anywhere, it takes a community to develop and disseminate them. In premodern Europe (1084-1793), there were approximately 200 universities and 150 academies of sciences, home to thousands of scholars from the Middle Ages to the First Industrial Revolution. By inferring co-presence from institutional affiliations, we simulate how ideas would spread from a scholar to another across the European academic network. We find that the implied exposure patterns align with observed urban developments: examples include botanic gardens, astronomical observatories, and Protestantism. Scholars’ mobility and multiple affiliations sustain the diffusion, and counterfactual simulations underscore the bridging role played by scientific academies. We also show that the spread of ideas through the affiliation network was locally fragile but globally robust, pointing towards academia as being a connective infrastructure underlying early European development. |
| Keywords: | Temporal Network, Structural Estimation, Scientific Revolution, European Academia, Epidemiological model |
| JEL: | N33 O33 I23 |
| Date: | 2026–03–30 |
| URL: | https://d.repec.org/n?u=RePEc:ctl:louvir:2026008 |
| By: | Marion Hoffman (IAST - Institute for Advanced Study in Toulouse); Tyler Thrash (Unknown); Christoph Hölscher (Unknown); Mubbasir Kapadia (Unknown); Victor R. Schinazi (Unknown) |
| Abstract: | Understanding crowd behavior is critical for designing buildings and public spaces with efficient circulation. However, the interplay of social and spatial contexts makes this endeavor challenging. This paper examines scenarios in which crowds perform a search task with time constraints, akin to individuals shopping or officers searching a crime area. We formulate and test two sets of hypotheses defined at the crowd and individual levels using desktop VR experiments. We conducted four experimental sessions that employed different social incentives (collaborative versus competitive) with a total of 140 participants, using a mixed factorial design where each individual participated in 12 trials. We found that competitive incentives produced higher levels of crowd aggregation than collaborative incentives. In addition, individuals were more likely to be influenced by others' behaviors in the collaborative compared to the competitive condition. Notably, these social signals were conveyed among participants without any verbal communication. We also developed a novel graph theoretic measure, "search attractiveness, " that accurately predicts space occupation during a search task. This paper highlights the roles of social and spatial contexts in understanding occupation and aggregation. |
| Keywords: | Crowd dynamics, Spatial layout, Virtual reality experiments, Graph theory, Space syntax |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05551130 |
| By: | Buhai, Ioan-Sebastian (Stockholm University - SOFI, UC Chile - Instituto de Economia, University of Minho - NIPE) |
| Abstract: | Extreme economic outcomes are not shaped by tails alone. They are also shaped by unequal access to opportunities. This paper develops a theory of heterogeneous extremes by taking the distribution of opportunity access as the object of study. In a mixed Poisson search setting, normalized maxima admit a Laplace mixture representation that yields order comparisons and a clean benchmark against the homogeneous economy. The main contribution is geometric: a canonical coupling turns differences in heterogeneity into optimal transport bounds for the whole induced law of extremes, the full schedule of top quantiles, and structured counterfactual paths between economies. The paper also derives a second order expansion that separates classical extreme value approximation error from heterogeneity effects. As a complementary normative exercise, it studies an entropy regularized design problem for reallocating opportunities under a mean constraint. A stylized labor market network application interprets heterogeneity as unequal access to job opportunities and shows how the framework can be used for tail counterfactuals and robustness analysis of top wage distributions. |
| Keywords: | extreme value theory, heterogeneous search, optimal transport, Wasserstein distance, entropic design, labor market networks |
| JEL: | C46 C61 D83 D85 J31 J64 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18511 |