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on Network Economics |
| By: | Zenou, Yves |
| Abstract: | This paper reviews the theoretical and empirical foundations of peer and network effects, aiming to bridge insights from both literatures. We first 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. The analysis then develops the microfoundations of peer effects through linear–quadratic network games, linking equilibrium behavior to network centrality and highlighting the role of key players. Using this framework, 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. |
| JEL: | A14 C57 D85 Z13 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20814 |
| By: | Zenou, Yves |
| Abstract: | This paper examines the behavior of Katz–Bonacich centrality and key-player intercentrality in linear–quadratic network games when spillovers are large. This question is both important and empirically relevant, as it greatly simplifies the empirical testing of peer-effect models. We show that Katz–Bonacich centrality converges (up to scale) to the Perron eigenvector, while intercentrality diverges and loses all discriminatory power across nodes. Consequently, designing key-player policies becomes problematic: key-player rankings collapse, and no agent is more important than another when spillovers are sufficiently large. |
| JEL: | D85 C72 D62 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20856 |
| By: | Eric Auerbach; Jonathan Auerbach; Sidonia McKenzie |
| Abstract: | Researchers often use the density of connections between groups of agents, such as communities, blocs, or markets, to characterize the structure of a social or economic network. In many cases, these groups are selected using the network data, making conventional fixed-group inference procedures potentially invalid. To address this issue, we develop two new confidence intervals that are universally valid post-selection in the sense that they guarantee simultaneous coverage asymptotically over all pairs of groups whose relative sizes do not vanish. Our first interval builds on a strategy of \cite{berk2013valid}. Our second interval is based on a Talagrand-type concentration inequality for empirical processes. Both intervals are simple to compute and scalable to large networks, but a key technical contribution of our paper is show that only the second interval achieves the best-possible width asymptotically up to a constant factor. Three empirical illustrations show that accounting for selection can matter in practice. Some evidence for homophily in a social network and a hub-and-spoke structure in a trade network survives our correction, while evidence for disjoint market segments in a worker transition network does not. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.00312 |
| By: | Olivier Bos; Stefano Bosi |
| Abstract: | We study how artificial intelligence (AI) interacts with social communication networks to shape the stability of collective knowledge. Agents exchange information through a network while receiving AI-generated content, and AI systems retrain on the aggregate social information they influence. This interaction generates two feedback forces: an AI contagion channel, through which distortions diffuse across the network, and an AI social distortion multiplier, through which retraining amplifies past errors. Despite the high dimensionality of the environment, we show that the long-run behavior of the system admits a two-dimensional representation whose spectral radius determines whether AI-mediated information systems are dynamically stable or unstable. We characterize a sharp regulatory frontier identifying the minimum filtering required for stability and show how network topology shapes systemic informational risk. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.15206 |
| By: | Duong Trinh; Santiago Montoya-Bland\'on |
| Abstract: | This paper introduces a new econometric framework for modeling social interactions with heterogeneous peer responses, addressing endogenous link formation. Our Selection-corrected Heterogeneous Spatial Autoregressive (SCHSAR) approach jointly models link formation and outcome determination. We incorporate a finite mixture structure to capture heterogeneity in peer effects and account for unobserved individual-specific factors driving both network formation and outcome equations, addressing network endogeneity for credible estimation of heterogeneous spillover effects. We propose a fully Bayesian data augmentation approach for estimation and inference, overcoming challenges posed to standard likelihood-based methods. A simulation study validates our approach. Our empirical application to an innovation network among U.S. firms reveals significant positive, yet heterogeneous, peer effects on corporate R&D investments, after accounting for endogenous network formation. The findings highlight varying firm behaviors in response to exogenous R&D policy shocks and and quantify firm-level direct and spillover effects, offering valuable insights for evidence-based and targeted policy design. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.24850 |
| By: | Klemp, Marc |
| Abstract: | This research shows that production networks make automation's wage effects state dependent: the speed of wage recovery differs sharply across economies, with differences particularly pronounced during recessions. I embed a standard ordered-task automation block in an input-output economy and decompose aggregate wage dynamics into a recovery push from improving task fundamentals and a general-equilibrium drag from recessionary reweighting of network pass-through. A key implication is convex amplification: the recession differential in automation's wage effect becomes more negative as network tightness rises. Using robot exposure (IFR), wages (EU KLEMS), and annual WIOD input-output tables for 24 EU countries (2000-2014), with an IV based on peer-country adoption, I find that a one-standard-deviation increase in network tightness raises the recession wage loss from a one-standard-deviation robot exposure increase by about 0.11 log points. The framework also delivers two diagnostics summarizing when task-level recovery survives network propagation. |
| JEL: | D57 E24 E32 J23 O33 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21172 |
| By: | Yi-Ning Weng; Hsuan-Wei Lee |
| Abstract: | Large and persistent differences in opioid prescribing across physicians and regions cannot be explained by patient characteristics or physician attributes alone. We developed a behavioral framework in which prescribing evolves through persistence, exposure to peers in professional networks, and heterogeneous responses to a common policy signal that varies with network centrality. Using nationwide Medicare Part D data from 2013 to 2020, covering more than two million physician-year observations, we tested three hypotheses implied by this framework. Physicians exposed to higher peer prescribing subsequently prescribe more; more central physicians reduce prescribing more following the introduction of the 2016 CDC guideline, with no evidence of differential pre-trends; and changes in peer prescribing are closely associated with changes in individual prescribing in the post-guideline period. By 2020, physicians at the 90th percentile of network centrality exhibited prescribing reductions 0.30 percentage points larger than those at the 10th percentile, with the gap widening steadily after the introduction of the CDC guideline. Together, these results indicate that opioid prescribing operates through professional networks, in which policy effects spread through connections and appear to be shaped by network position. This suggests that engaging highly connected physicians may help extend the reach of opioid stewardship programs. It also raises questions about how the burden and benefits of such targeting would be distributed across physicians and patients. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.22254 |
| By: | Brice Romuald Gueyap Kounga |
| Abstract: | This paper develops identification and estimation methods for a semiparametric dynamic logit model in which a binary outcome depends on observed covariates, the lagged outcome, and an unknown function of a latent social characteristic that also governs the formation of social ties. The unobserved characteristic is allowed to vary across agents and over time, and the network formation process is left completely unspecified. Identification combines three elements: conditional likelihood arguments that exploit the logistic structure, network-type matching that eliminates the unknown social influence function by comparing agents whose observed linking behavior reveals identical latent characteristics, and local temporal smoothing that handles the interaction between dynamics and time-varying unobserved heterogeneity. A kernel-weighted conditional maximum likelihood estimator is proposed, and its consistency and asymptotic normality are established at the $\sqrt{n}$ rate. Monte Carlo simulations show that the estimator substantially reduces the bias present in naive and control-function approaches across a range of network formation models and achieves close to nominal coverage at moderate sample sizes. The method is applied to longitudinal data on adolescent smoking and friendship networks from the Glasgow Teenage Friends and Lifestyle Study. An extension to ordered outcomes is developed using composite conditional maximum likelihood. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.16230 |
| By: | Shaowen Luo; Kwok Ping Tsang; Zichao Yang |
| Abstract: | Which regional exposure conclusions are identified when public data do not observe buyer-seller links across states? We study this question by treating the missing intermediate-input spatial kernel as an unknown coupling constrained by regional activity margins, support restrictions, and auxiliary shipment moments. For linear exposure statistics, the sharp identified set is computed by transportation linear programs. Applying the method to U.S. state-sector data, we find that shipment data are inconsistent with the spatial diffuseness implied by proportional regionalization in key goods sectors. However, they do not identify a unique regional production network or a precise ranking of state exposure to local shocks. Bilateral shipment restrictions tighten the bounds, but much of the remaining uncertainty comes from large service and mixed sectors that are weakly covered by goods-movement data. The results show which exposure conclusions are supported by public data and which are imposed by maintained regionalization assumptions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.17079 |
| By: | Ottaviano, Gianmarco |
| Abstract: | This paper studies how digital infrastructure is associated with the spatial structure of international trade in goods. We embed data availability into a structural gravity framework, conceptualizing it as an information friction that interacts with geographic distance and equilibrium market access. Using the topology of the global subsea cable network, we construct country-level measures of digital network position. We find that countries with greater digital network embeddedness, particularly on the exporter side, exhibit lower distance elasticities of trade. Other dimensions of digital connectivity are more closely associated with multilateral resistance, highlighting distinct channels through which digital infrastructure affects goods trade. |
| Keywords: | International trade |
| JEL: | R12 D85 F14 R11 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21276 |
| By: | Amir, Gideon; Arieli, Itai; Ashkenazi-Golan, Galit; Peretz, Ron |
| Abstract: | We study a model of opinion exchange in social networks where a state of the world is realized and every agent receives a zero-mean noisy signal of the realized state. Golub and Jackson (2010) have shown that under DeGroot (1974) dynamics agents reach a consensus that is close to the state of the world when the network is large. The DeGroot dynamics, however, is highly non-robust and the presence of a single “adversarial agent” that does not adhere to the updating rule can sway the public consensus to any other value. We introduce a variant of DeGroot dynamics that we call 1/ -DeGroot. 1/ -DeGroot dynamics approximates standard DeGroot dynamics to the nearest rational number with as its denominator and like the DeGroot dynamics it is Markovian and stationary. We show that in contrast to standard DeGroot dynamics, 1/ -DeGroot dynamics is highly robust both to the presence of adversarial agents and to certain types of misspecifications. |
| JEL: | C63 D83 D85 |
| Date: | 2025–01–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:126309 |
| By: | Van Coppenolle, Brenda; Vanden Eynde, Oliver |
| Abstract: | Networks can help political actors survive violence in revolutions. Connections could protect against reprisals and provide information. However, personal networks are not randomly assigned, making it difficult to causally trace their role. The French Constituent Assembly of 1789 randomly assigned legislators to smaller groups, which we use to assess outcomes in the ensuing revolution such as violent death and emigration. Violent death was contagious among the nobility in these networks. However, connections to two key leaders, Lafayette and Robespierre, were protective against violent death, encouraging emigration, regardless of ideological differences. We argue that nobles drew an informational advantage from their connections. |
| Keywords: | Historical political economy; French revolution; Networks |
| JEL: | D74 D72 N40 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21171 |
| By: | Johnen, Johannes; Shekhar, Shiva |
| Abstract: | This paper proposes a simple yet useful framework for evaluating vertical mergers in digital markets by distinguishing between product-specific and ecosystem-specific network effects. Vis-Ã -vis no network effects, product-specific network effects amplify foreclosure and steering incentives, as a rival’s growth directly undermines the platform’s product value. Conversely, ecosystem-specific effects dampen foreclosure incentives, since rivals contribute to the overall value of the platform ecosystem. We develop a formal model illustrating how this distinction shapes platform behavior and competitive outcomes. We apply this distinction to real-world examples to illustrate its potential usefulness. Our distinction implies that regulators may want to adopt a stricter standard with no presumption of efficiencies where product-specific effects dominate. In contrast, when ecosystem-specific effects prevail, merger evaluation should mirror traditional vertical merger analysis. Thus, offering a more nuanced approach to merger evaluation by presenting a practical screening tool to identify problematic vertical mergers in markets featuring network effects. |
| Keywords: | Network externalities; Platforms; Vertical integration; Foreclosure; Steering |
| JEL: | L22 L41 L51 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20899 |
| By: | Federico Daniel Forte |
| Abstract: | This Working Paper provides the first analysis of credit relationships between financial institutions and firms through the lens of hypergraphs. We applied empirically this approach to Credit Registry data from the Central Bank of Argentina, covering the period from Aug. 2023 to Dec. 2025 and focusing on commercial loans. This Working Paper provides the first analysis of credit relationships between financial institutions and firms through the lens of hypergraphs. We applied empirically this approach to Credit Registry data from the Central Bank of Argentina, covering the period from Aug. 2023 to Dec. 2025 and focusing on commercial loans. |
| Keywords: | Network Analysis, Análisis de redes, Credit, Crédito, Loans, Préstamos, Systemic Risk, Riesgo sistémico, Argentina, Argentina, Banks, Banca, Central Banks, Bancos Centrales, Financial Regulation, Regulación Financiera, Working Paper, Documento de Trabajo |
| JEL: | D85 G21 G28 C63 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:bbv:wpaper:2611 |
| By: | Gondauri, Davit |
| Abstract: | This study develops a regional benchmark and proof-of-concept Hodge-econometric framework for measuring the world economy as a connected topological macro-flow system rather than as a set of isolated output aggregates. Using official public macroeconomic anchors--regional GDP, world-output share, projected growth, transition GDP, and income depth--the study constructs a weighted regional network of ten global macro-regions, with the World used as a normalization anchor and the Caucasus retained as an explicit corridor-sensitive subregional node. The methodology is quantitative, non-experimental, computational, and validation-oriented: it combines gravity-style network construction, directed macro-flow pressure, discrete Hodge decomposition, spectral and higher-order Hodge-Laplacian diagnostics, robust econometric testing, machine-learning forecast validation, and persistent-homology confirmation. The central novelty is the transformation of standard macroeconomic indicators into a decomposable edge cochain, allowing regional hierarchy, local-cycle imbalance, harmonic systemic circulation, shock-transmission exposure, resilience, and tail risk to be measured within one coherent empirical architecture. The regional benchmark shows that the constructed graph is connected and cycle-rich, with 10 nodes, 27 edges, a density of 0.600, and a transitivity of 0.740. The Hodge energy decomposition separates macro-flow pressure into gradient hierarchy (49.69%), curl/local-cycle imbalance (36.22%), and harmonic/systemic circulation (14.09%), indicating that global-regional structure is not reducible to GDP size alone. Econometric, predictive, and topological tests are used as internal benchmark-validation diagnostics, not as final causal estimates for all countries or observed dyadic flows. The study therefore contributes a reproducible measurement architecture for international macroeconomic analysis and is best understood as a mathematically grounded prototype for future all-country, dyadic, multilayer, and longitudinal replication. |
| Keywords: | Hodge econometrics, discrete Hodge decomposition, global economy, regional macro-flow network, systemic circulation, curl imbalance, harmonic component, macroeconomic resilience, shock transmission, persistent homology, network econometrics, economic complexity |
| JEL: | C02 C31 C38 C45 C51 C53 F14 F15 F47 O47 R11 R15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341543 |
| By: | Diksha Gupta; Ritwick Mishra; Achla Marathe; Krista Danielle Yu; Anil Vullikanti |
| Abstract: | Global supply chains are highly interconnected, making them vulnerable to cascading disruptions induced by trade policy shocks. Understanding how such disruptions propagate through production networks, and how mitigation mechanisms such as trade reallocation and production adjustment can alleviate their impacts, remains a central challenge. In this work, we develop a linear programming formulation of an Input-Output (IO) system that captures cascading supply-chain disruptions together with trade reallocation and production expansion. Our formulation yields a system-level equilibrium characterization that enables the joint analysis of disruption propagation and mitigation within a unified framework. We propose an efficient algorithm for computing approximate equilibrium solutions by minimizing total unmet demand in large IO systems. We apply our approach to tariff-induced disruptions in the global oilseeds supply chain arising from the U.S.-China trade war. Our results show that a localized 70% disruption to flows from the U.S. oilseeds sector to China leads to a 3.27% loss in global output, with China experiencing a disproportionate loss of 14.02%. As a counterfactual mitigation strategy, allowing a 20% reallocation from Brazil's oilseed sector to China significantly reduces global output losses to 1.36%, although pressure remains high on final-demand flows. We further investigate production expansion as an additional mitigation mechanism and show that it introduces tradeoffs between reducing global final-demand losses and protecting Brazil's domestic flows. Domestic reallocation disproportionately shifts losses toward smaller economies, while globally sourced expansion redistributes losses more broadly across the network. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.30685 |
| By: | Alfaro, Laura; Conconi, Paola; Kamal, Fariha; Kroff, Zachary |
| Abstract: | Traditional theories of firm boundaries predict trade between vertically related units of the same firm. Using novel data that combine a comprehensive mapping of U.S. multinationals’ production networks with their customs filings, we uncover a strong positive relationship between input-output linkages and trade between parents and their affiliates. We also find that intrafirm trade is prevalent, particularly between geographically proximate units: three-quarters of affiliates in North America trade with their U.S. parent. These results overturn prior findings based on survey data on intrafirm trade. Administrative intrafirm records enable correcting measurement errors in survey data, reconciling traditional theories with empirical evidence. |
| Keywords: | Multinational enterprises |
| JEL: | F14 F23 D23 L20 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21063 |
| By: | Vasco M. Carvalho; Matias Covarrubias; Galo Nuño |
| Abstract: | In dynamic multisector economies, the planner’s optimal capital allocation can serve to minimize the aggregate impact of shocks cascading through nonlinear production networks. We show analytically in a simplified model that, under complementarity and if risk aversion is not too low, (i) optimal capital allocation under uncertainty involves deliberately over-investing, relative to the deterministic optimum, in upstream sectors in order to mitigate severe economic downturns; (ii) this strategy can reduce the average level of consumption and give rise to a high welfare cost of business cycles. Deploying novel deep-learning techniques in a general environment, we show quantitatively that: (iii) the ergodic distribution of the simulated nonlinear economy features higher mean capital levels in key upstream sectors, lower mean levels of macroeconomic aggregates, realistic aggregate volatility, and a welfare cost of business cycles two orders of magnitude larger than in standard one-sector models. |
| Keywords: | deep learning, production network, nonlinear propagation |
| JEL: | E32 C63 C67 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12796 |
| By: | Borin, Alessandro; Conteduca, Francesco Paolo; Leone, Fabrizio; Mancini, Michele; Zoi, Patrick |
| Abstract: | This paper examines how international trade shocks transmit through domestic supply chains, shaping local economic vulnerabilities. Using detailed firm-to-firm domestic and foreign transaction data, we quantify the direct and indirect exposure of Italian labor markets to two major sources of external risk: imports from China and exports to the United States. We quantify the importance of firms’ domestic and foreign linkages for overall exposure and highlight the critical role of wholesalers and top trading firms within the domestic network in shaping tails risks. Pronounced local disparities in exposure reveal that aggregate trade statistics conceal substantial and uneven regional vulnerabilities. |
| JEL: | F14 R12 L14 F61 R15 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:20910 |
| By: | Olivier De Jonghe; Daniel Lewis |
| Abstract: | We propose a novel decomposition to identify relationship-specific effects or shocks in a bipartite network under covariance restrictions. We show existing two-way fixed effects decompositions are ill-suited to correlations consistent with realistic heterogeneity. Our strategy yields a simple consistent estimator. We estimate relationship-level credit demand and supply shocks across nine euro-area countries and three episodes. We find evidence demand and supply each have both firm and bank components, and within-firm/bank shock variation is of comparable scale to between-firm/bank variation. Regressions using firm fixed effects as demand controls are at odds with economic theory, while our method uncovers significant deleterious effects of the post-2022 monetary contraction on exposed firms. |
| Date: | 2026–07–16 |
| URL: | https://d.repec.org/n?u=RePEc:azt:cemmap:12/26 |
| By: | Aguilar, Pablo; Darracq Pariès, Matthieu; Dieppe, Alistair; Domínguez-Díaz, Rubén; Gallegos, José-Elías; Quintana, Javier; Eugenelo, Antonio |
| Abstract: | We study the short-run macroeconomic transmission of a US–China tariff war in an open economy multi-sector New Keynesian model with input–output linkages, sectoral nominal rigidities, and heterogeneous currency invoicing. A reciprocal 10 percentage-point tariff increase generates asymmetric incidence: the tariff-imposing country bears more of the inflationary burden, while the targeted country experiences the larger output contraction. Production networks amplify this contraction by propagating the shock beyond the directly tariffed bilateral margin. Currency invoicing further shapes transmission. Under heterogeneous invoicing, dollar-priced border prices weaken the expenditure-switching role of exchange rates, deepening the contraction in China relative to producer-currency pricing and altering third-country spillovers. The EA response is small in the aggregate, but only because positive trade-diversion margins are offset by weaker demand from China and multilateral adjustments. We then exploit the model’s sectoral structure by imposing tariffs on one Chinese sector at a time. Sectoral incidence is highly concentrated, but aggregate effects cannot be inferred from the directly tariffed sector alone: domestic propagation offsets own-sector gains in the US, reinforces own-sector losses in China, and leaves the EA as a net object shaped by opposing trade margins. The results show that tariff incidence depends jointly on where the tariff lands, how the shock propagates through production networks, and how invoicing governs border-price adjustment. A framework that combines these margins delivers a materially different assessment from one built on bilateral trade shares alone. JEL Classification: E31, E32, E52, F13, F41, F42 |
| Keywords: | dominant currency pricing, DSGE, multicountry, networks, tariffs, trade |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263254 |
| By: | Degui Li; Yuying Sun; Boyao Wu |
| Abstract: | In this paper, we introduce a flexible time-varying multi-layer network vector autoregression (VAR) model framework for large-scale time series, allowing agents in dynamic systems to interact through multiple channels and incorporating multiple adjacency matrices to capture network spillover effects. We propose a penalized model averaging method to determine a time-varying optimal combination of multi-layer network VAR candidate models whose number may be divergent. Under some regularity conditions, the asymptotic properties such as asymptotic optimality and convergence rates of the proposed time-varying weight estimation are derived in the contexts of both the in-sample fitting and out-of-sample prediction. In addition, we extend the conformal prediction method to construct prediction bands for locally stationary time series. Monte-Carlo simulation studies and an empirical application to forecast CPI inflation by combining multiple network information are given to illustrate reliable finite-sample estimation and predictive performance of the developed methodology. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.25292 |
| By: | Flora Bellone (Université Côte d'Azur, CNRS, GREDEG, France); Edwin Fourrier-Nicolaï (Université Côte d'Azur, CNRS, GREDEG, France); Simone Vannuccini (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | We study how imported input price shocks affect both the intensity and direction of innovation. Using comprehensive French firm-level data combining accounting records, ownership structures, customs transactions and patents over the period 2014-2023, we construct firm-level exposure to input price shocks based on structural breaks in product-level import unit values from non-EU countries, aggregated using a shift-share design. Innovation intensity is measured using priority patent applications, while the direction of innovation is characterized by mapping patents to products and embedding them in a production network to distinguish innovations directly related to affected inputs from those connected through upstream, downstream, or technologically adjacent linkages. We find that input price shocks primarily affect the direction rather than the level of innovation. Exposed firms reallocate innovative activity toward connected technological domains, consistent with network-based directed technological change. This reallocation is strongest among firms at the technological frontier, while smaller and less productive firms adjust more through overall innovation intensity. We provide evidence for specific industries, showing that the shock-innovation impact-response is heterogeneous. We interpret our results as firms' resorting to what we label defensive innovation. Our findings can inform policy making and firm strategy in a context of increasing trade fragmentation and geopolitical risk. |
| Keywords: | input trade shocks; directed innovation; trade fragmentation; patents |
| JEL: | F14 O31 O33 F18 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2026-17 |
| By: | E. Marrocu; R. Paci; L. Serafini |
| Abstract: | This paper investigates the determinants of interfirm agreement formation in the context of the twin digital and green transition. We focus on strategic alliances and joint ventures involving at least one Italian firm, using SDC Platinum data on agreements announced between 2000 and 2025. Digital and green agreements are identified through a keyword-based classification of deal synopses. The empirical analysis is conducted at the dyadic level by comparing realised agreements with potential firm pairs within the framework of rare event logit models, focusing on the role of geographical, technological and relational proximity. The results show that technological proximity is the strongest predictor of agreement formation. Firms operating in connected industrial domains are substantially more likely to collaborate, suggesting that compatible knowledge bases and absorptive capacity are central to partner selection. Geographical proximity also matters, mainly through coordination and interaction costs rather than administrative co-location. The comparison between digital and green agreements shows that both domains require technological compatibility, but they rely on different forms of proximity and complementarity. Digital agreements are especially sensitive to broad network-based technological proximity, consistent with the modular and cross-sectoral nature of digital technologies. Green agreements combine compatible but differentiated capabilities with a stronger spatial and implementation-related component, reflecting their connection to infrastructures, regulation, and local coordination conditions. Prior relational proximity increases the probability of agreement formation in the full sample, while network effects are more exploratory in the digital and green subsamples. The paper contributes to the literature on alliances, proximity, and transition-oriented innovation by showing that twin-transition collaboration is shaped by multiple and partially distinct proximity mechanisms. |
| Keywords: | twin transition, strategic alliances, joint ventures, proximities, networks, rare events |
| JEL: | C25 L14 O31 O33 R12 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202610 |
| By: | Jie Jian; Aaron Schein |
| Abstract: | We study sparse semi-continuous tensor data with excess zeros, heavy right tails, and slice-specific dispersion. Such features arise naturally in monetary-valued multi-way data, such as international trade, where most exporter--importer--product--year cells are zero while positive values are continuous and highly variable. To model these data, we propose a Bayesian hierarchical tensor factorization model that places a low-rank CP structure on a latent Poisson rate tensor and couples it with a conditional Gamma model for positive outcomes, with rate parameters that can vary across slices within a mode. The model therefore separates the occurrence and magnitude of positive observations while borrowing strength across all tensor dimensions through a shared low-rank latent structure. To scale posterior inference to large arrays, we develop a hybrid variational--Monte Carlo algorithm that combines efficient coordinate ascent updates with a partially collapsed augmented-data sampler. Applied to approximately 60 million trade flows, the method surfaces multiway dependence across exporters, importers, products, and years that is difficult to recover from gravity-type or pairwise network analyses, which do not jointly model the product and temporal dimensions. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.17267 |
| By: | Joan Vilá; Marcelo Bérgolo; Guillermo Cruces; Marcelo Bergolo |
| Abstract: | Standard welfare evaluations of means-tested transfers typically treat eligibility enforcement as affecting only households directly exposed to the rule. We show that this direct-only approach is incomplete when enforcement events transmit information through workplace networks, inducing other beneficiaries to adjust formal employment and reported earnings. We study Uruguay’s AFAM-PE conditional cash transfer, in which household income eligibility is verified monthly using administrative records of formal earnings and households exceeding the threshold may lose benefits, while most beneficiaries are largely unaware of the income rule. Linking program records, social security employment histories, and firm identifiers, we build workplace networks defined six months before each peer’s exposure to enforcement and identify spillovers using a fuzzy regression discontinuity design that exploits quasi-random variation in whether a peer crosses the income threshold. Coworkers of peers just above the cutoff reduce registered labor earnings by 20%, and the corresponding fuzzy-RD estimate implies a decline of about one-third of baseline earnings in response to a peer’s suspension. These effects emerge within two months and persist for at least 12 months. The response operates almost entirely on the extensive margin of formal employment, and most of the decline reflects transitions to informal work rather than exit from the labor force. Spillovers are concentrated in small firms and among similar coworkers, are nearly three times larger when the suspended peer actively contacted the program’s support service, and are absent among non-beneficiary coworkers. This pattern is consistent with information transmission as the main channel. Incorporating these indirect responses lowers the estimated MVPF of the program as implemented with enforcement from 0.53 to 0.36. Because the same information channel operates wherever means-tested programs are enforced through records of formal earnings, evaluations that count only directly exposed households systematically understate the cost of enforcement. |
| Keywords: | social assistance, income-testing, enforcement spillovers |
| JEL: | H53 H75 I38 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12805 |
| By: | Alvarez, Fernando; Argente, David; Lippi, Francesco; Méndez, Esteban; Van Patten, Diana |
| Abstract: | We develop a dynamic model of technology adoption featuring strategic complementarities: the benefits of the technology increase with the number of adopters. We show that complementarities give rise to gradual adoption, multiple equilibria, multiple steady states, and suboptimal allocations. We study the planner’s problem and its implementation through adoption subsidies. We apply the theory to SINPE Movil, a peer-to-peer payment app developed by the Central Bank of Costa Rica. Using transaction-level data and user-specific networks that we construct from administrative records, we causally estimate sizable complementarities. In our calibrated model, the optimal subsidy pushes the economy to universal adoption. |
| JEL: | E4 E5 O1 O2 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:21211 |
| By: | Mattia Stival (Ca’ Foscari University of Venice); Stefano F. Tonellato (Ca’ Foscari University of Venice) |
| Abstract: | We study graph-indexed time series in which the vertices of a connected graph are partitioned into spatially contiguous clusters and each cluster carries a Gaussian or generalized linear dynamic state-space regression. The observation layer covers continuous Gaussian responses and binomial, Poisson, and negative-binomial GLM responses, while the clusterlevel state is a time-varying regression vector shared by all vertices in the same connected region. The partition prior is represented through cuts of spanning trees, which enforces connected clusters while allowing irregular shapes. We establish support properties of the prior, exact-target invariance for reversible-jump and tempered kernels, posterior concentration for dynamic predictors under non-i.i.d. Gaussian/GLM likelihoods, and partition-selection consistency under a separation condition. We then develop a posterior sampler based on split, merge, cut-swap, tree-refresh, boundary-reassignment, state-hyperparameter, and observation-parameter moves, together with proxy-guided proposals, locally balanced options, and standard parallel tempering. We also describe pointwise and simultaneous functional credible bands for dynamic coefficients. A controlled negative-binomial simulation illustrates the data-generating mechanism, recovery of the connected partition, the role of parallel tempering in improving exploration, and posterior estimation of main-effect and interaction trajectories. |
| Keywords: | Bayesian asymptotics; Bayesian computation; connected graph partitions; generalized linear dynamic models; graph-indexed time series; posterior concentration; posterior credible bands; parallel tempering; reversible-jump MCMC; simulation study; spanning trees; state-space models |
| JEL: | C11 C13 C15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2026:21 |
| By: | Wenli Du; Andrea Zaccaria |
| Abstract: | Several network-based measures have been proposed to assess the economic complexity of countries. These measures have provided important insights into national economic development, and they are now widely applied at the subnational level as well. Here, we show that such applications lead to inconsistent results, in the sense that the estimated complexity of the same product appears to depend on methodological details such as the geographical scale of analysis. Building on these findings, we propose a measure of territorial economic complexity based on an exogenous and extensive computation. We show that these methodological choices yield estimates that are more consistent and more strongly aligned with standard economic indicators, such as GDP per capita and employment. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.26966 |