|
on Network Economics |
| By: | Mikhail Anufriev; Kirill Borisov; Mikhail Pakhnin |
| Abstract: | We develop a model of social interaction in which agents hold private beliefs but communicate public statements. We distinguish between two networks: an audience network shaping statements through social pressure, and an influence network governing belief updating. This separation drives a wedge between public statements and underlying beliefs, so that agents may persistently communicate opinions they do not hold. We provide necessary and sufficient network conditions for the emergence of such hypocrisy. The model generates rich patterns of opinion propagation, including the amplification of messages that agents themselves do not believe, and sheds light on phenomena such as propaganda diffusion, the spiral of silence, and political correctness. |
| Keywords: | social learning, network theory, hypocrisy, political correctness, dissonance minimization, propaganda |
| JEL: | D83 D85 D91 Z13 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12700 |
| By: | Ulrich Hounyo; Jiahao Lin; Xiaojun Song |
| Abstract: | Dyadic regression models are commonly analyzed under the conventional dyadic dependence paradigm, in which two observations may be dependent only if the corresponding dyads share a node. This paper studies inference when this paradigm breaks down because nodes are ordered and nearby nodes are exposed to common latent shocks. In this setting, dyads with no common endpoint may still be dependent when their endpoints are close in the ordering. Although each additional covariance term may be weak, the number of nearby-node dyad pairs diverges with the sample size, so their aggregate contribution to the asymptotic variance can be non-negligible. We develop an inferential framework for dyadic arrays with ordered-node dependence. The first estimator is a dependent-node dyadic CRVE that retains covariance terms between dyads with nearby endpoints. The second is a row-column moving-block jackknife that deletes adjacent blocks of nodes together with all dyads touching those nodes. We establish the asymptotic validity of both procedures under weak dependence along the ordered node index. Monte Carlo evidence shows that accounting for ordered-node dependence can substantially improve size control, and that the jackknife version is comparatively stable in finite samples. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.28349 |
| By: | Müller, Tobias; McMiken, Shane |
| Abstract: | We study how sector-specific fiscal policy propagates in an economy with heterogeneous households and production networks. We develop a multisector New Keynesian model in which input-output linkages interact with differences in households’ marginal propensities to consume (MPCs). We show that fiscal multipliers depend on sectors’ positions in the production network, as network linkages reallocate income across households with heterogeneous consumption responses. We derive an intersectoral Keynesian cross and introduce an MPC-augmented network multiplier that jointly characterize the transmission of fiscal shocks. The interaction between heterogeneous consumption responses and production networks is non-additive: network linkages can either amplify or attenuate fiscal transmission depending on how income is redistributed across households. Fiscal policy is most effective when spending is directed toward labor-intensive, downstream sectors that employ a large share of high-MPC households. Using data from the Survey of Consumer Finances, we document substantial sectoral heterogeneity in household balance sheets and in the prevalence of hand-to-mouth households. Calibrating the model to the U.S. economy, we find sizable variation in sectoral fiscal multipliers and significant distributional effects of government spending. JEL Classification: D57, E21, E62, E32 |
| Keywords: | hand-to-mouth households, income distribution, input-output networks, marginal propensities to consume, sectoral fiscal multipliers |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263244 |
| By: | Diego Vallarino |
| Abstract: | This paper studies sovereign stress avalanches and network amplification in Latin American credit markets using monthly J.P. Morgan EMBI Global Diversified spreads for eleven sovereigns over 2007-2026. Country stress events are defined as positive log-spread innovations exceeding country-specific volatility thresholds, and regional avalanches count the number of stressed countries in each month. The empirical design combines finite-sample power-law diagnostics, threshold robustness checks, a country-level reshuffling placebo, and rolling correlation, partial-correlation, and minimum-spanning-tree networks. Avalanche sizes are heavy-tailed, with an estimated exponent of 1.77, while spread changes and inter-event times lie in a heavy-tail boundary regime. The placebo shows synchronization far above independent stress timing, with p-values below 0.001. Large avalanches coincide with denser and more spectrally amplifying raw-correlation networks, but not after partial-correlation filtering, indicating common-factor co-movement rather than conditional regional propagation. Network metrics describe contemporaneous stress regimes rather than early-warning signals. The results provide a finite-size criticality framework for monitoring sovereign fragility in emerging markets. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.12460 |
| By: | Tugrul Temel, Tugrul |
| Abstract: | This paper develops a co-evolutionary network model to analyze how micro-level interactions among heterogeneous organizations generate macro-level structural patterns and performance outcomes in innovation systems. Organizations possess knowledge stocks, absorptive and distributive capacities, and adaptively rewire their connections. We integrate six key mechanisms---capacity-constrained knowledge flows, endogenous capacity accumulation, resource-based collaboration costs, innovation as a growth-structure interaction, strategic repositioning, and adaptive network rewiring---into a formal simulation framework. The model is calibrated using Approximate Bayesian Computation to match stylized facts from the innovation literature and employed in a structured scenario analysis spanning alternative policy-relevant regimes. Results reveal systematic trade-offs with important policy implications. Expanding connectivity without parallel capacity development yields limited gains; isolated capacity building amplifies inequality. In contrast, coordinated interventions targeting both network structure and organizational capabilities produce the most robust and equitable growth. Comparative analysis across four distinct economic environments demonstrates that intervention effectiveness is highly contingent on underlying frictions. The findings underscore the need for innovation policy that is explicitly network-aware and systemic, emphasizing bundled, context-sensitive interventions rather than isolated levers. The model provides a computational laboratory for exploring such policy design principles. |
| Keywords: | innovation systems; policy design; network analysis; graph-theoretic concepts; |
| JEL: | O31 O32 O38 |
| Date: | 2026–02–11 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128018 |
| By: | Celso Brunetti; Christoph Frei |
| Abstract: | We study the rise of nonbank financial intermediation and its implications for systemic risk. We develop a structural network model of banks and nonbank financial institutions (NBFIs) that decomposes intermediation into a capacity channel, driven by bank balance-sheet constraints, and a reliance channel, reflecting NBFI funding reliance. Using U.S. banking confidential supervisory data, we estimate key structural parameters and quantify both channels. We find that fluctuations in bank-NBFI intermediation are primarily explained by the reliance channel, with variation in NBFI fragility emerging as the dominant driver. We show that NBFI intermediation can amplify shocks through funding interconnectedness. |
| Keywords: | bank regulation; nonbank financial intermediation; systemic risk; financial networks; balance-sheet constraints; nonbank financial institution (NBFI) fragility; capacity and reliance channels; supervisory data |
| JEL: | G21 G23 G28 C51 D85 |
| Date: | 2026–05–11 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:103340 |
| By: | Lianyan Fu; Rui Wang; Zihan Zhang |
| Abstract: | This paper proposes a generalized Mundlak estimator based on graph neural networks (GME-GNN). The estimator is designed to mitigate bias arising from group-level heterogeneity and to accommodate within-group dependence among individuals. Traditional fixed-effects models handle group heterogeneity via group-specific intercepts, but require overly strict linear additivity and intra-group independence assumptions, and are confined to within-group comparisons. Rather than relying on intercepts, GME-GNN uses aggregated group-level balancing statistics to fully control between-group confounding, enabling valid cross-group comparisons and relaxing linearity constraints. It further employs graph neural network message-passing to adaptively learn nonlinear representations and capture intra-group interaction effects. Theoretical analysis shows that the estimator satisfies double robustness and is asymptotically normal. Simulation and empirical studies confirm its performance. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.29238 |
| By: | Beyer, Jürgen |
| Abstract: | This paper reinterprets the emergence of the digital winner-takes-all economy through Mahoney’s concept of the reactive sequence. Rather than treating digital concentration as the natural result of network effects, scale economies, or technological lock-in, it argues that these mechanisms became decisive only within a historically specific institutional trajectory. The early internet was constituted as a non-commercial sphere shaped by gift exchange, hacker ethics, open sharing, and community-oriented practices. As these norms diffused into mass usage, they were simplified into low willingness to pay and expectations of free access. Digital firms reacted by monetizing other market sides, especially advertisers, sellers, and platform-dependent businesses. This adaptation intensified network effects, weakened price differentiation, and privileged market leaders. Over time, observed concentration became generalized into a winner-takes-all expectation and institutionalized through venture-capital strategies, rapid scaling norms, and founder self-understandings. The essay extends this sequence to contemporary AI firms and their emerging wealth elites. |
| Date: | 2026–06–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:zaq7w_v2 |
| By: | Avishek Bhandari; Ipsita Parida |
| Abstract: | In this paper, an attempt is made to examine how the speed at which financial markets absorb information governs the way shocks travel between them. It may be noted that a market which digests news slowly will register an incoming shock more gradually, and hence over longer horizons, than a market which reacts quickly. Building on the Heterogeneous Agents Contagion versus Interdependence (HACI) framework, in which advanced economies adapt quickly and emerging economies slowly, we develop a spectral theory of contagion in which both the originating and the receiving market filter the shock. The central result is an intuitive one: the slower of the two markets determines the time horizon over which contagion is felt most strongly. From this we obtain three testable predictions, jointly the Scale-Ordered Contagion Hypothesis, namely that contagion involving slower markets peaks at longer horizons; that the horizon pattern is the same in both directions for any pair of markets; and that only the strength of contagion, and not its timing, differs by direction. We then turn the theory into an estimator that recovers each market's speed of adaptation from the data, and bring the predictions to G20 equity markets over the period 2006 to 2026. The results are supportive, though we report them honestly: the horizon-ordering prediction holds (p=0.042); the symmetry prediction, which is the sharpest of the three, holds for all twenty-eight market pairs (p>0.05); the strength-asymmetry prediction lies in the predicted direction but is not statistically significant; and the method identifies India and China cleanly as the slowest adapters, though the fastest markets cannot be told apart from daily data. The framework thus offers a simple and testable account of why short-horizon and long-horizon contagion differ systematically with the speeds of the markets involved. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.04113 |