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on Network Economics |
| By: | Fan, Junwei (University of Glasgow, Adam Smith Business School); Montoya-Blandon, Santiago (University of Glasgow, Adam Smith Business School); Proto, Eugenio (University of Birmingham, CAGE) |
| Abstract: | We examine how social comparison within endogenously formed peer networks affects different measures of young adult mental health. Using over 2 million smartphone-based communication records from the NetHealth Study, we construct objective social networks among university students and estimate a structural model in which individuals choose their peers and mental health depends on the income of chosen peers. When networks are treated as exogenous, higher average peer income predicts better mental health; once network formation is modelled endogenously, the relationship reverses: exposure to higher-income peers significantly worsens depressive symptoms. Income gaps harm mental health in both directions, consistent with inequality aversion rather than relative deprivation. The reversal survives extending binary links to message-weighted networks, and counterfactual exercises show that policies reducing the volume of messages exchanged with peers improve mental health, increasingly so with the size of the reduction, though the effect of targeted reductions depends on which ties are attenuated. Ignoring endogenous peer selection can thus yield qualitatively misleading conclusions about peer effects on mental health. |
| Keywords: | social networks; relative status; mental health; network formation. JEL Classification: D63, D85, D91, I14, I31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:cge:wacage:824 |
| By: | Deepankar Basu |
| Abstract: | This paper introduces segregation monotonicity as a criterion for evaluating measures of inequality in social networks. A network inequality measure satisfies segregation monotonicity if, holding the distribution of income fixed, it weakly increases as the network becomes more segregated according to a specified transformation of network architecture. I investigate this property using a class of level-$k$ star networks that represent increasing social segregation in the following sense: relatively poorer individuals become increasingly isolated from one another and their social comparisons become increasingly concentrated among richer individuals. I show that a relative deprivation-based measure of inequality, which aggregates comparisons with richer network neighbors, satisfies segregation monotonicity. By contrast, total experience-based measures, which aggregate absolute income differences among all network neighbors, do not generally satisfy segregation monotonicity and can decline as segregation rises. I also establish several relationships between the total experience-based measures and the standard Gini coefficient. The results show that alternative network-based inequality measures can embody fundamentally different conceptions of socially relevant comparisons. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.17807 |
| By: | Shaowen Luo; Kwok Ping Tsang; Zichao Yang |
| Abstract: | A production network can remain largely unidentified even when the economic decision it supports is identified. We characterize sufficient measurements for exposure-based decisions and compute sharp maximum regret over networks consistent with released totals. Using earlier and later vintages of Japan's interregional input-output accounts, we select measurements from the 1995 table and evaluate the frozen design against the 2005 benchmark. At roughly half the statistics required for full disclosure, the resulting monitoring set loses only 0.07 percentage points of average exposure relative to the benchmark optimum, yet its sharp maximum regret across compatible networks is 4.83 points. In U.S. coal deliveries surrounding a 2005 Wyoming rail disruption, additional shipment measurements identify the optimal set of plants to monitor for inventory risk, even though four monitored plants' exposures to the affected coal supply remain unidentified. The results distinguish good benchmark performance from a decision guarantee and show that decisions can be identified before individual exposures. They suggest evaluating network data by the economic decisions they support. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.29905 |
| By: | Hanping Chen; Zeqi Wu |
| Abstract: | This paper investigates the construction of moment restrictions in dyadic network formation models with unobserved individual heterogeneity under nontransferable utility. Using observed links and covariates from five-node pentads, we construct moment restrictions that do not depend on individual fixed effects. For a broad class of covariate specifications, the construction is minimal in the sense that it uses the least possible number of nodes and dyads. Based on these moment restrictions, we propose the pentad-GMM estimator. We establish asymptotic normality of the pentad-GMM estimator in dense, sparse, and ultra-sparse network regimes, with regime-specific convergence rates and asymptotic variances. These results provide a basis for inference across all three regimes. To make our method computationally efficient, we develop an algorithm that reduces the computational cost of the estimator from na\"ive \(O(N^5)\) to \(O(N^3)\). We apply the proposed method to an academic-discussion network, and find academic homophily and a positive association between link formation and a potential partner's openness to different perspectives. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.12545 |
| By: | Abhinav Tomar; Lakshya Nagpal; Vikas Chauhan; S. R. Hassan |
| Abstract: | Interconnected financial systems are vulnerable to cascading failures arising from cross-holdings and nonlinear contagion, making the analysis and mitigation of systemic risk a challenging computational problem. In this work, we develop a unified optimization framework for financial network analysis based on Ising models and Quadratic Unconstrained Binary Optimization (QUBO). Starting from the Elliott Golub Jackson financial network model, we extend equilibrium valuation to incorporate threshold-induced failures, formulate the Maximum Cascade Failure Problem, and derive an equivalent QUBO representation. We then formulate the Optimal Bailout Allocation Problem as a controlled Ising model and transform the resulting bi-level optimization into a single joint QUBO that simultaneously determines equilibrium failures and optimal interventions under budget constraints. To characterize the influence of individual institutions, we introduce bailout susceptibility as a response-based measure of systemic importance and develop a susceptibility-driven greedy intervention strategy. Numerical simulations demonstrate equilibrium valuation, worst-case cascade identification, optimal bailout allocation, and susceptibility analysis on financial networks of varying sizes. Beyond optimization, the Ising representation provides a general statistical-mechanical framework for analyzing financial contagion, enabling the application of response theory, Monte Carlo methods, and other techniques developed for interacting spin systems. The proposed framework establishes a unified approach for systemic risk analysis that is compatible with classical annealing, quantum-inspired optimization, and emerging quantum annealing technologies |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.17415 |
| By: | Hamed Amini; Zhecheng Wu |
| Abstract: | We study equilibrium and risk evaluation in large sparse economic networks with heterogeneous responses, shocks, and bilateral exposures. Our approach approximates the distribution of equilibrium outcomes through local computations on a limiting rooted network. Under marked local weak convergence, we prove convergence in probability of the empirical equilibrium distribution in two settings: uniformly contractive responses and bounded monotone nonexpansive responses whose lower and upper root iterations coalesce. In the latter setting, the limit is independent of the measurable finite-network equilibrium selection, even when equilibria are not unique. Under bounded domain and continuity assumptions, this convergence extends to law-invariant risk measures, including conditional value-at-risk, with recursion-depth error bounds under additional regularity. Numerical experiments on production networks and payment clearing systems illustrate the accuracy and computational benefits of the local approximation. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.27107 |
| By: | Xinyu Song |
| Abstract: | We introduce a network realized GARCH-It\^o model in which volatility transmission is a dynamic relation among the latent daily integrated volatilities of multiple assets. An unknown directed and signed network is embedded in a continuous-time variance process and appears in the resulting exponential daily recursion. Intraday returns identify integrated volatility and hence the network that governs its propagation. For a fixed network template, feasible estimation based on realized volatility is first-order equivalent to estimation based on latent integrated volatility. For an unknown network, regularization selects the relevant structure, and, under oracle conditions, a fixed-rank local refit provides conditional inference for model-implied response-based connectedness. In an application to nine U.S. sector ETFs from 2007 to 2025, the model attains the lowest average out-of-sample QLIKE among the reported recursive forecasts, although its advantage over HAR is not statistically significant. In the full sample the weighted LASSO selects an empty sparse support, so inference concerns the rank-one projected factor component; this conditional analysis identifies Energy as a net volatility transmitter. Aggregate connectedness is more stable across volatility measures than individual sparse channels. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.29515 |
| By: | Esteve Almirall; Steve Willmott; Ulises Cort\'es |
| Abstract: | Why does a superior technology sometimes spread and sometimes stall, even when its expected returns are much higher? We develop an agent-based model in which firms adopt a digital technology by imitating successful neighbours through a fast-and-frugal heuristic. We compare frequency-based imitation, where firms follow the local majority, with performance-conditioned imitation, where they follow better-performing neighbours. We define a cascade as high final adoption reached through rapid, accelerating takeoff rather than slow accumulation. In the calibrated regime, four results emerge. First, dynamic cascades arise under performance-conditioned imitation, but not under an exogenous adoption hazard or frequency-based imitation. Second, on degree-controlled networks, adoption tips once the network provides sufficient reach, at a small-world rewiring threshold near 0.41, with a 95 percent bootstrap confidence interval of 0.39 to 0.43. This reflects short paths, low clustering, and high-reach nodes rather than one graph statistic. Third, at a fixed mean threshold, concentrating receptiveness in the lower tail can generate cascades that homogeneous thresholds cannot. Fourth, cascade probability depends strongly on the positions of initial adopters and low-threshold pioneers. At human-scale observation degrees, the mechanism is genuine complex contagion: adoption requires reinforcement from multiple successful exemplars, survives a requirement of at least two successful digital neighbours when seeded by a clustered critical mass, and weakens beyond Dunbar-scale neighbourhoods. The model identifies where interventions may have leverage rather than forecasting industries. Broad cultural change is not required for system-wide adoption: a small minority of receptive firms, positioned where the network can transmit success, can be enough. In short, a few misfits can change the world. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.30269 |
| By: | Onur Polat (Hacettepe University, Informatics Institute 06800 Beytepe, Ankara, Turkiye); Hardik A. Marfatia (Department of Economics, Northeastern Illinois University, 5500 N. St. Louis Ave, Chicago 60625, USA); Christophe Andre (Economics Department, Organisation for Economic Co-operation and Development (OECD), 75775 Paris, Cedex 16, France); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This paper examines time-varying connectedness and volatility spillovers between housing markets and macroeconomic policy conditions, with particular emphasis on the role of housing deregulation. Using daily data from September 2007 to May 2026, we estimate a 15-node TVP-VAR network comprising housing volatility series for ten major U.S. metropolitan areas alongside five policy and financial indicators: the housing deregulation index, economic policy uncertainty (EPU), a spliced monetary policy proxy (Effective Federal Funds Rate/Krippner Shadow Short Rate), the Aruoba-Diebold-Scotti (ADS) business conditions index, and the 5-year breakeven inflation rate (T5YIE). Volatility inputs are filtered using a multivariate GJR-GARCH model augmented with time-varying skewness and kurtosis (GJRSK), and parameters are estimated within a Bayesian prior (BayesPrior) framework. The Total Connectedness Index reveals a counter-cyclical network topology, peaking during the 2008 financial crisis, the 2020 pandemic, and the 2024 monetary pivot. Pairwise decompositions reveal pronounced heterogeneity across cities: supply-inelastic coastal market, New York (peak spillover 45.5%), San Diego (48.8%), and San Francisco (39.8%), absorb the largest regulatory shocks, while deregulation also transmits persistently to monetary policy conditions (Shadow Short Rate positive in 87.7% of observations), real activity, and inflation expectations. These findings carry direct implications for macroprudential policy design and institutional portfolio risk management in the U.S. housing sector. |
| Keywords: | Housing Volatility Networks, Housing Deregulation; TVP-VAR, Net Pairwise Spillovers, GJRSK Volatility, BayesPrior |
| JEL: | C32 G10 R31 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202630 |
| By: | Benjamin O. Harrison; David T. Jacho-Chavez |
| Abstract: | This paper develops Wald inference for least-squares estimation of linear regression models on dyadic data, accommodating configurations where multiple observations share the same pair of units (e.g., directed flows, multilayer networks, and dyadic panels). We establish that the dyadic-robust Wald statistic is asymptotically $\chi^2_q$ for an arbitrary nonrandom sequence of full-rank restrictions, under a single condition on the accumulation of dependence. Throughout, no convergence rate is assumed or estimated, permitting the condition number of the score's variance matrix to diverge. We further propose a delete-one-unit jackknife alternative that is positive semidefinite by construction. This jackknife statistic attains the same asymptotic limit under one additional condition on dyad multiplicity and remains asymptotically conservative when that condition fails. A supplement contains all proofs, Monte Carlo experiments featuring estimated coefficients that converge at heterogeneous rates, and an empirical gravity application to bilateral trade. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.16968 |