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on Network Economics |
| By: | Valentina Macchiati; Emiliano Marchese; Piero Mazzarisi; Diego Garlaschelli; Tiziano Squartini |
| Abstract: | The level of systemic risk in economic and financial systems is strongly determined by the structure of the underlying networks of interdependent entities that can propagate shocks and stresses. Since changes in network structure imply changes in risk levels, it is important to identify structural transitions potentially leading to system-wide crises. Methods have been proposed to assess whether a real-world network is in a (quasi-)stationary state by checking the consistency of its structural evolution with appropriate maximum-entropy ensembles of graphs. While previous analyses of this kind have focused on dyadic and triadic motifs, hence disregarding higher-order structures, here we consider closed walks of any length. Specifically, we study the ensemble properties of the spectral radius of random graph models calibrated on real-world evolving networks. Our approach is shown to work remarkably well for directed networks, both binary and weighted. As illustrative examples, we consider the Electronic Market for Interbank Deposit (e-MID), the Dutch Interbank Network (DIN) and the International Trade Network (ITN) in their evolution across the 2008 crisis. By monitoring the deviation of the spectral radius from its ensemble expectation, we find that the ITN remains in a (quasi-)equilibrium state throughout the period considered, while both the DIN and e-MID exhibit a clear out-of-equilibrium behaviour. The spectral deviation therefore captures ongoing topological changes, extending over all length scales, to provide a compact proxy of the resilience of economic and financial networks. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.03349 |
| By: | Koen Jochmans (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | This paper concerns the analysis of network data when unobserved node-specific heterogeneity is present. We postulate a weighted version of the classic stochastic block model, where nodes belong to one of a finite number of latent communities and the placement of edges between them and any weight assigned to these depend on the communities to which the nodes belong. A simple rank condition is presented under which we establish that the number of latent communities, their distribution, and the conditional distribution of edges and weights given community membership are all nonparametrically identified from knowledge of the joint (marginal) distribution of edges and weights in graphs of a fixed size. The identification argument is constructive and we present a computationally-attractive nonparametric estimator based on it. Limit theory is derived under asymptotics where we observe a growing number of independent networks of a fixed size. The results of a series of numerical experiments are reported on. |
| Keywords: | Heterogeneity, Network, Random graph, Sorting, Stochastic block model |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04672521 |
| By: | Bertoni, Marco (University of Padova); Parkam, Saeideh (University of Naples Federico II) |
| Abstract: | We leverage the timing of pandemic-induced school closures to learn about the emergence of ordinal rank effects in education. Using administrative data from Italian middle schools for four cohorts of students, our study reveals that disrupting peer interactions during the first year of middle school - when students are still unfamiliar with one another - substantially diminishes the impact of ordinal rank on test scores. Instead, later interruptions to peer interactions do not significantly affect the strength of these interpersonal comparisons. |
| Keywords: | ability peer effect, ordinal ability rank, school closures, COVID-19 |
| JEL: | I21 I24 J24 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17222 |
| By: | Zhongjian Lin; Francis Vella |
| Abstract: | We address the estimation of endogenous treatment models with social interactions in both the treatment and outcome equations. We model the interactions between individuals in an internally consistent manner via a game theoretic approach based on discrete Bayesian games. This introduces a substantial computational burden in estimation which we address through a sequential version of the nested fixed point algorithm. We also provide some relevant treatment effects, and procedures for their estimation, which capture the impact on both the individual and the total sample. Our empirical application examines the impact of an individual's exercise frequency on her level of self-esteem. We find that an individual's exercise frequency is influenced by her expectation of her friends'. We also find that an individual's level of self-esteem is affected by her level of exercise and, at relatively lower levels of self-esteem, by the expectation of her friends' self-esteem. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.13971 |