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on Network Economics |
| By: | Kevin Dano; Bryan S. Graham; Yassine Sbai Sassi |
| Abstract: | In social and economic networks linked agents often share connections in common. There are two competing explanations for this phenomenon. First, agents may have a structural taste for transitive links - the returns to linking may be higher if two agents share a common connection. Second, agents may assortatively match on unobserved attributes, a process called homophily. We study parameter identifiability in a simple model of dynamic network formation with both effects. Agents form, maintain, and dissolve links over time to maximize utility. The return to linking may be higher if agents share connections in common. A pair-specific utility component allows for arbitrary homophily on time-invariant agent attributes. We derive conditions under which it is possible to detect the presence of a taste for transitivity in the presence of assortative matching on unobservables. We leave the joint distribution of the initial network and the pair-specific utility component, both high dimensional nuisance parameters, unrestricted. Our identification result is constructive, suggesting an analog estimator, whose finite and (single) large network properties we characterize. We show, via examples, the delicacy of information accumulation in the single (large) network setting. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.14049 |
| By: | Luca Brugnara (Bank of Italy); Gianmarco Cariola (Bank of Italy); Pasquale Recchia (Bank of Italy); Simone Zuccolalà (Bank of Italy) |
| Abstract: | This paper develops a firm-level input–output framework to measure participation in production networks using transaction-level data and applies it to the automotive industry. Using a novel dataset of firm-to-firm transactions, we construct a firm-specific measure of supply chain exposure based on the Ghosh inverse, which is a sufficient statistic for the upstream propagation of demand shocks within a standard production network model. We use this framework to trace both direct and indirect sales linkages across firms. We find that around 8, 000 Italian firms are significantly exposed to the EU automotive industry, with indirect linkages accounting for more than half of the supply chain. Exposure is highly heterogeneous across firms, sectors, and regions, and simulated demand shocks generate sizeable upstream propagation effects: a 1 per cent decline in final demand for cars would reduce value added among final producers by 63 million euros, with an additional 93 million propagating upstream through the network. The framework is scalable and can be extended to other downstream industries with clearly identifiable final producers, providing a flexible tool for analysing production networks and the upstream transmission of demand shocks. |
| Keywords: | production networks, firm-to-firm transaction data, shock propagation, automotive industry |
| JEL: | D57 L14 L62 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bdi:opques:qef_1041_26 |
| By: | Valerio Astuti; Daniele Piras |
| Abstract: | We present a comprehensive description of the network of Italian Business-to-Business commercial relationships, based on the universe of electronic invoices collected by the Italian Tax Office. The firm-to-firm detail of the data is exploited to describe the distribution of the numbers of buyers (customers) and sellers (suppliers) per firm, the centrality of each firm in the production network, and the average distance between firms. We characterize for the first time the firm-to-firm network describing the Italian production system, and present its geographic and sectoral breakdown. The analysis reveals heavy tailed distributions for the numbers of buyers and sellers per firm, implying a scale-free structure of the network. The distributions of centrality values display heavy tails as well, indicating a strong concentration of importance in a relatively small number of firms. We estimate the tail exponents for all these distributions, finding in all cases lower exponents for downstream (buyer-side) distributions than for the upstream (seller-side) ones. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.10227 |
| By: | Ana Isabel Castillo Pereda |
| Abstract: | This work presents an operator-based visual analytics pipeline for exploring synthetic systemic risk dynamics in financial networks. The framework is formulated as a composition of mathematical operators that sequentially transform synthetic financial observations into dynamic scientific visualizations. The pipeline consists of six operators: latent risk mapping, probabilistic score generation, financial network construction, distance-based contagion dynamics, visual encoding, and perspective projection. Together, these operators provide a modular computational structure linking nonlinear risk surfaces, time-dependent probabilistic states, network topology, and shock propagation. A reproducible implementation demonstrates the architecture through controlled synthetic experiments. Nonlinear latent risk representations are converted into probabilistic scores, embedded in a weighted financial network, and propagated via shortest-path contagion. The resulting states are then mapped into dynamic visual representations. The experiments illustrate how visualization can be treated as an explicit stage of the analytical process rather than a post-processing step. The proposed formulation does not aim to introduce new predictive models or contagion mechanisms. Instead, it offers a transparent and modular pipeline that connects generative risk modeling, network dynamics, and scientific visualization within a unified operator-based architecture. This approach supports reproducibility and facilitates the exploration and communication of complex systemic-risk processes in synthetic settings. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.22202 |
| By: | Valentina Michelangeli (Bank of Italy); Silvia Sacco (IVASS); Valentino Bado (Bank of Italy); Valeria De Chiara (Bank of Italy); Ginette Eramo (Bank of Italy); Francesco Ficarola (IVASS); Irene Mavilia (Bank of Italy); Claudia Miani (Bank of Italy); Ivan Quaglia (Bank of Italy); Giuseppe Reale (Bank of Italy); Francesco Sciarretta (IVASS); Riccardo Scimone (Bank of Italy); Laura Sigalotti (Bank of Italy) |
| Abstract: | We analyse the structure and evolution of the Italian financial system over the period 2019-2025 using a multilayer network framework that captures interconnections across financial institutions and instruments. The network has a bank-centred core, with insurance companies acting as intermediate connectors and investment funds forming a broad periphery. Although the overall number of institutions remains broadly stable, sectoral dynamics diverge, as consolidations among banks and insurers are offset by the expansion of the investment fund sector. While individual layers have distinct topological characteristics, cross-sector linkages account for a growing share of total connections, underscoring their increasing role in shock transmission. Despite its sparse structure, the network exhibits small-world properties, implying a rapid contagion potential. Consolidation episodes are associated with temporary but meaningful structural shifts, while the network's overall architecture remains remarkably stable over time. |
| Keywords: | banks, insurers, investment funds, interconnections, network analysis |
| JEL: | G21 G22 G23 G18 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bdi:opques:qef_1049_26 |
| By: | Antonio Mosca; Piero Mazzarisi |
| Abstract: | Likelihood-based network models are often fitted under links' independence and low-order constraints, while empirical networks frequently exhibit systematic higher-order structures such as triangles and wedges, characterizing the observed clustering patterns. Real-world core-periphery networks such as the interbank market or the air transportation system represent key examples, with cores displaying complex and nonlinear features. We formalize Penalized Likelihood with Structural Discrepancies (PLSD) as an inference-level correction that trades likelihood fit for agreement with targeted motifs. PLSD augments the negative log-likelihood with a penalty on standardized wedge and triangle discrepancies, yielding a controlled distortion of the likelihood surface that can be interpreted through a linear response analysis. A calibration approach for the unique tuning hyperparameter is introduced to turn off the penalization term when the model is correctly specified, thereby recovering maximum-likelihood estimation in that case. We then introduce a mixed-constraint maximum-entropy core-periphery Exponential Random Graph Model (ERGM), derive unconditional semi-closed motif formulas under Pareto-distributed core fitness, and interpret sparse-regime scaling through a big-jump mechanism for heavy-tailed distributions. We finally corroborate the PLSD inference methodology both with Monte Carlo simulations of a toy stochastic block model and by applying the novel core-periphery ERGM to weekly eMID interbank networks (2009-2015) and monthly US air traffic networks (1991-2000). We show that PLSD describes cores that are not only dense but also higher-order-rich and captures the observed heterogeneous degree distributions and the overexpression of wedges and triangles, at a link-level cost proportional to the model misspecification. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.15796 |
| By: | Kim, Yong Jin |
| Abstract: | The international system is moving away from the relatively coherent post-Cold War order toward layered and contested multipolarity. Military power is diffusing; economic interdependence is increasingly securitized; alliance commitments are exposed to domestic bargaining; and states are embedded in overlapping security, technology, financial, and industrial networks. This paper develops a general framework for alliance choice and strategic autonomy under these conditions. A stylized model with linear preferences and linear technologies treats the United States and China as competing network centers: the sponsor chooses whether and how strongly to support its network, while smaller states compare alternative networks according to security, geography, productivity and supply-chain effects, transfers, political-assimilation costs, and indigenous capability. The model distinguishes decentralized allied defense from a network-planner benchmark and derives an alliance transfer multiplier theorem for alliance transfers. Under a frictionless one-for-one reallocation from direct U.S. military spending to an ally transfer, if the induced present-value increase in allied military effort exceeds one per unit transferred, the alliance multiplier raises U.S.-alliance military capability and, through the modeled hegemonic-power channel, U.S. welfare; allied welfare also rises under the maintained assumptions when the multiplier exceeds one. Although the local theorem holds membership fixed, entry can amplify the effect through network size, productivity, and further induced military effort. Conversely, transfer cuts near participation thresholds can trigger nonlinear alliance weakening. The formal logic is linked to a four-index comparative diagnostic applied across fourteen states. Strategic autonomy is defined not as equidistant neutrality but as the capacity to preserve meaningful choice under pressure. South Korea is retained as a brief hard-case illustration in Appendix A. |
| Keywords: | multipolarity, hegemonic stability, strategic autonomy, alliance networks, U.S.-China competition, network externalities |
| JEL: | F50 F52 F53 H56 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:343529 |
| By: | Brad Cannon; David Hirshleifer; Joshua Thornton |
| Abstract: | Using Facebook friendship data, we study how three aspects of social capital shape innovative activity. We find that the most important aspect of social capital in explaining innovation is Economic Connectedness (EC)--the share of high-income friendships. One standard deviation greater EC is associated with 97% more patents per capita among patenting ZIP Codes and 45% more breakthrough patents per capita among ZIP Codes with breakthrough patents. Reverse-causality tests, a within-inventor relocation design, and a quasi-experiment using fracking-driven economic shocks to non-local friends support a causal interpretation. Mechanism tests provide evidence consistent with a financing channel. |
| JEL: | D14 D25 D83 D85 D9 G32 G41 G51 O16 O3 O33 O35 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35732 |
| By: | Alex Chan |
| Abstract: | Spectrum repacking auctions can restore feasibility along two margins: retiring a station via buyout or relaxing a pairwise engineering restriction. I represent stations as vertices in a network and interference restrictions as edges between them, and so station buyouts delete vertices and engineering waivers relax edges. This paper shows that buying out any one station can reduce the number of channels needed, even though relaxing any single interference restriction cannot. The smallest feasible number of channels is k . For every k≥4, I show that there is a network in which removing any station saves one channel, while relaxing any single pairwise restriction does not. This main result follows directly from Dirac’s half-century-old conjecture on vertex-critical graphs, whose chromatic number falls after any vertex is removed. By constructing the missing counterexample at k=4 and combining it with earlier results for k≥5, I show that at every k≥4, some k-vertex-critical graph has no critical edge, resolving Dirac's conjecture. Market designers auditing feasibility link-by-link systematically can mistake combinatorial complementarity for remedy failure, overlooking station buyouts that unlock the clearing target. |
| JEL: | C6 C61 D44 D47 L96 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35757 |