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on Network Economics |
| By: | Pérez Velilla, Alejandro (University of California, Merced); Ready, Elspeth |
| Abstract: | Communities around the world rely on networks of resource sharing to buffer households against hardship. Yet, under such informal insurance schemes, some needy families can be systematically left out. By modeling sharing and reputational spread on a network, we show how generosity can be sustained when reputational benefits spread across a community’s communication network, but also how network density and position shape who receives transfers---well-connected households become priority receivers due to their ability to spread givers' reputation more effectively. Our analysis, combining mathematical modeling with food sharing data from an Inuit community, reveals that communication network sparsity incentivizes broader sharing, but also more selectivity. The correlation of communication network structure and resource endowment is critical for stabilizing needs-based transfers. If need status does not perfectly (negatively) correlate with social influence, marginal households in need may still be excluded from sharing. By linking individual incentives to community-wide patterns, our framework clarifies when indirect reciprocity can stabilize outcomes that show the signatures of need-based transfers. |
| Date: | 2025–12–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:6zfju_v1 |
| By: | Zihan Zhang; Lianyan Fu; Dehui Wang |
| Abstract: | Estimating causal effects from observational network data faces dual challenges of network interference and unmeasured confounding. To address this, we propose a general Difference-in-Differences framework that integrates double negative controls (DNC) and graph neural networks (GNNs). Based on the modified parallel trends assumption and DNC, semiparametric identification of direct and indirect causal effects is established. We then propose doubly robust estimators. Specifically, an approach combining GNNs with the generalized method of moments is developed to estimate the functions of high-dimensional covariates and network structure. Furthermore, we derive the estimator's asymptotic normality under the $\psi$-network dependence and approximate neighborhood interference. Simulations show the finite-sample performance of our estimators. Finally, we apply our method to analyze the impact of China's green credit policy on corporate green innovation. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.00603 |
| By: | Jingmin Huang; Yang Sun; Fanqi Xu; Wei Zhao |
| Abstract: | This paper investigates the decentralized provision of public goods in directed networks. We establish a correspondence between kernels in graph theory and specialized equilibria in which players either contribute a fixed threshold amount or free-ride entirely. Leveraging this relationship, we derive sufficient conditions for the existence and uniqueness of specialized equilibria in deterministic networks and prove that specialized equilibria exist almost surely in large random networks. We further demonstrate that enhancing network reciprocity weakly expands the set of specialized equilibria without destroying existing ones. Moreover, we propose an iterative elimination algorithm that simplifies the network while preserving equilibrium properties. Finally, we show that a Nash equilibrium is stable only if it is specialized, thereby providing dynamic justification for our focus on this equilibrium class. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.23193 |
| By: | John W.E. Cremin (Aix-Marseille Univ., CNRS, AMSE, Marseille, France) |
| Abstract: | Models of social learning conventionally assume that all actions are visible, whereas in reality, we can often choose whether or not to advertise our choices. Inthis paper, I study a model of sequential social learning in which social agents choose whether or not to let successors see their action, only wanting to do so if they are sufficiently confident in their choice (they are timid), and noise agents act randomly. I find that in sparse networks, this produces a form of unravelling to the effect that noise agents are overrepresented. This can damage learning to an arbitrary extent if social agents are sufficiently timid. In dense networks, however, no such unravelling occurs, and the combination of noise and timidity can facilitate complete learning even with bounded beliefs. |
| Keywords: | Sequential Social Learning, Endogenous Social Networks, Network Theory, Information Economics |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:aim:wpaimx:2526 |
| By: | Katz, Yarden Azoulay |
| Abstract: | Activists have shown how the nonprofit industrial complex (NPIC) co-opts grassroots social movements, and how the dependence on donors limits what nonprofits can say or do—especially concerning Palestine. Yet the NPIC is rarely analyzed as a whole system. This article analyzes the massive funding network of the NPIC, reconstructed from tax forms, and highlights Zionism’s place within it. I show how the NPIC’s funding web binds many organizations to Zionist nonprofits that directly fuel settler-colonialism in Palestine. The links between these organizations are facilitated by donor-advised funds, which form the “hubs” in the interconnected funding network. I explain the political implications of this finding using two “activist” nonprofits: the Black Lives Matter Global Foundation (BLMGN) and Jewish Voice for Peace (JVP). I argue that being a major player in the NPIC not only limits what an organization can do but also normalizes the broader funding networks driving colonial projects. |
| Date: | 2025–12–19 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:2vqwn_v1 |
| By: | Kundan Mukhia; Imran Ansari; S R Luwang; Md Nurujjaman |
| Abstract: | This study investigates how financial market structure reorganizes during the COVID-19 crash using a conditional p-threshold mutual information (MI) based Minimum Spanning Tree (MST) framework. We analyze nonlinear dependencies among the largest stocks from four diverse QUAD countries: the US, Japan, Australia, and India. Crashes are identified using the Hellinger distance and Hilbert spectrum; a crash occurs when HD = mu\_H + 2*sigma\_H, segmenting data into pre-crash, crash, and post-crash periods. Conditional p-threshold MI filters out common market effects and applies permutation-based significance testing. Resulting validated dependencies are used to construct MST networks for comparison across periods. Networks become more integrated during the crash, with shorter path lengths, higher centrality, and lower algebraic connectivity, indicating fragility. Core-periphery structure declines, with increased periphery vulnerability, and disassortative mixing facilitates shock transmission. Post-crash networks show only partial recovery. Aftershock analysis using the Gutenberg-Richter law indicates higher relative frequency of large volatility events following the crash. Results are consistent across all markets, highlighting the conditional p-threshold MI framework for capturing nonlinear interdependencies and systemic vulnerability. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.00395 |
| By: | Mariluz Mate |
| Abstract: | Many empirical studies estimate causal effects in environments where economic units interact through spatial or network connections. In such settings, outcomes are jointly determined, and treatment induced shocks propagate across economically connected units. A growing literature highlights identification challenges in these models and questions the causal interpretation of estimated spillovers. This paper argues that the problem is more fundamental. Under interdependence, causal effects are not uniquely defined objects even when the interaction structure is correctly specified or consistently learned, and even under ideal identifying conditions. We develop a causal framework for firm-level economies in which interaction structures are unobserved but can be learned from predetermined characteristics. We show that learning the network, while necessary to model interdependence, is not sufficient for causal interpretation. Instead, causal conclusions hinge on explicit counterfactual assumptions governing how outcomes adjust following a treatment change. We formalize three economically meaningful counterfactual regimes partial equilibrium, local interaction, and network, consistent equilibrium, and show that standard spatial autoregressive estimates map into distinct causal effects depending on the counterfactual adopted. We derive identification conditions for each regime and demonstrate that equilibrium causal effects require substantially stronger assumptions than direct or local effects. A Monte Carlo simulation illustrates that equilibrium and partial-equilibrium effects differ mechanically even before estimation, and that network feedback can amplify bias when identifying assumptions fail. Taken together, our results clarify what existing spatial and network estimators can and cannot identify and provide practical guidance for empirical research in interdependent economic environments |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.00279 |
| By: | Haibo Wang |
| Abstract: | The growing economic influence of the BRICS nations requires risk models that capture complex, long-term dynamics. This paper introduces the Bank Risk Interlinkage with Dynamic Graph and Event Simulations (BRIDGES) framework, which analyzes systemic risk based on the level of information complexity (zero-order, first-order, and second-order). BRIDGES utilizes the Dynamic Time Warping (DTW) distance to construct a dynamic network for 551 BRICS banks based on their strategic similarity, using zero-order information such as annual balance sheet data from 2008 to 2024. It then employs first-order information, including trends in risk ratios, to detect shifts in banks' behavior. A Temporal Graph Neural Network (TGNN), as the core of BRIDGES, is deployed to learn network evolutions and detect second-order information, such as anomalous changes in the structural relationships of the bank network. To measure the impact of anomalous changes on network stability, BRIDGES performs Agent-Based Model (ABM) simulations to assess the banking system's resilience to internal financial failure and external geopolitical shocks at the individual country level and across BRICS nations. Simulation results show that the failure of the largest institutions causes more systemic damage than the failure of the financially vulnerable or dynamically anomalous ones, driven by powerful panic effects. Compared to this "too big to fail" scenario, a geopolitical shock with correlated country-wide propagation causes more destructive systemic damage, leading to a near-total systemic collapse. It suggests that the primary threats to BRICS financial stability are second-order panic and large-scale geopolitical shocks, which traditional risk analysis models might not detect. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.20515 |
| By: | Jesús Fernández-Villaverde (University of Pennsylvania); Tomohide Mineyama (International Monetary Fund); Dongho Song (Johns Hopkins University) |
| Abstract: | We study how uneven gains from globalization can endogenously generate protectionism as a political equilibrium. Using U.S. data, we document that regions more exposed to import competition display stronger opposition to globalization, especially among households with little financial wealth, and that firms in trade-exposed sectors sharply increase lobbying expenditures. To interpret these patterns, we develop and quantify a general equilibrium Ricardian model with heterogeneous households, input–output linkages, and endogenous trade policy shaped by voting and lobbying. Distributional shocks reallocate political support among voters, while lobbying propagates through production networks, generating strategic complementarities that sustain protectionism. Calibrated to U.S.–China sectoral data from 1991–2019, the model accounts for rising inequality, declining support for globalization, and key aggregate trends in consumption and trade. |
| Keywords: | Globalization, heterogeneous households, multi-sector, production network, Ricardian trade, voting, political lobbying |
| JEL: | D57 D58 D63 D72 F1 F2 F4 F6 |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:26-001 |
| By: | Paul X. McCarthy; Xian Gong; Marian-Andrei Rizoiu; Paolo Boldi |
| Abstract: | The global aid system functions as a complex and evolving ecosystem; yet widespread understanding of its structure remains largely limited to aggregate volume flows. Here we map the network topology of global aid using a dataset of unprecedented scale: over 10 million transaction records connecting 2, 456 publishing organisations across 230 countries between 1967 and 2025. We apply bipartite projection and dimensionality reduction to reveal the geometry of the system and unveil hidden patterns. This exposes distinct functional clusters that are otherwise sparsely connected. We find that while governments and multilateral agencies provide the primary resources, a small set of knowledge brokers provide the critical connectivity. Universities and research foundations specifically act as essential bridges between disparate islands of implementers and funders. We identify a core solar system of 25 central actors who drive this connectivity including unanticipated brokers like J-PAL and the Hewlett Foundation. These findings demonstrate that influence in the aid ecosystem flows through structural connectivity as much as financial volume. Our results provide a new framework for donors to identify strategic partners that accelerate coordination and evidence diffusion across the global network. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.17243 |
| By: | Richard V. Burkhauser; Ji Ma |
| Abstract: | How does academic research inform presidential economic policy? This paper investigates the sources of evidence in the Economic Report of the President from 2010 to 2025. We construct a novel dataset of 4, 140 unique references cited across the Obama, Trump, and Biden administrations to map the evidence base used by the Council of Economic Advisers. Our analysis shows that peer-reviewed articles, comprising 66.62% of all these references, are heavily concentrated in top-tier economics journals. While the specific articles cited change with policy priorities, the hierarchy of journals remains moderately stable across years and administrations. A co-author network analysis reveals a scholarly landscape of distinct intellectual camps. Crucially, a small number of high-centrality scholars act as brokers, connecting these disparate research communities. Together, our findings illuminate the social structure of evidence-based policymaking, demonstrating how journal hierarchies and scholarly networks shape the flow of economic knowledge to the White House. |
| JEL: | A11 H11 I38 J08 N01 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34597 |
| By: | Hu, Yan (Department of Economics, Copenhagen Business School); Maurer, Stephan (University of Edinburgh,) |
| Abstract: | Do minorities benefit from social networks? In this paper, we study this ques-tion using the historical example of China’s first modern bureaucratic organization, the Chinese Maritime Customs Service. Drawing on newly digitized personnel records from 1876-1911, we first show that the Chinese clerks employed by the service were predomi-nantly Cantonese. Using the plausibly exogenous transfers of clerks across stations, we then estimate that a non-Cantonese (minority) clerk benefited significantly from meeting at least one colleague from his same province and dialect. Such connections led to faster promotion and a 5.6% salary increase, with even stronger effects when meeting a clerk who was either senior or of high quality. |
| Keywords: | Chinese Maritime Customs Service; Social connections; Wages; Promotion; Minorities |
| JEL: | J15 J31 J45 N35 N75 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:cbsnow:2025_014 |
| By: | John W.E. Cremin (Aix-Marseille Univ., CNRS, AMSE, Marseille, France) |
| Abstract: | Modern society is increasingly polarized, even on purely factual questions, despite greater access to information than ever. In a model of sequential sociallearning, I study the impact ofmotivated reasoningon information aggregation. This is a belief formation process in which agents trade-off accuracy against ideological convenience. I find that even Bayesian agents only learn in very highly connected networks, where agents have arbitrarily large neighborhoods asymptotically. This is driven by the fact that motivated agents sometimes reject information that can be inferred from their neighbors’ actions when it refutes their desired beliefs. Observing any finite neighborhood, there is always some probability that all of an agent’s neighbors will have disregarded information thus. Moreover, I establish thatconsensus, where all agents eventually choose the same action, is only possible with relatively uninformative private signals and low levels of motivated reasoning. |
| Keywords: | Social Learning, Motivated Reasoning, Networks, polarization |
| JEL: | D72 D83 D85 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:aim:wpaimx:2527 |