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on Network Economics |
| By: | Mahdi Kohan Sefidi |
| Abstract: | Production networks, dynamic systems of firms linked through input-output relationships, transmit microeconomic shocks into macroeconomic fluctuations. While prior studies often assume static networks, real-world economies feature continuous firm entry (node addition) and exit (node deletion). We develop a probabilistic model to analyze how these dynamics affect production volatility and network resilience. Integrating Leontief input-output frameworks with controllability theory. By quantifying fluctuations as expected values under probabilistic node dynamics, we identify trade-offs between adaptability and stability. Methodologically, we unify Kalman rank criteria and minimum input theory, offering policymakers insights to balance innovation-driven entry with safeguards against destabilizing exits. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.10154 |
| By: | Tomoo Kikuchi; Lien Pham |
| Abstract: | We develop a model of strategic competition between global currencies. Issuers choose their commitment levels for currency internationalization, while users -- interconnected in a trade network -- choose their usage of each currency for trade settlement. Our theoretical findings highlight not only the advantage of the status-quo issuer in maintaining dominance, but also the conditions under which an emerging issuer can attract users, potentially leading to a multi-currency payment system. The network centrality of users plays a key role in shaping both their currency choices and the strategic commitment levels of issuers. Our framework offers testable implications for the share of global currencies for trade settlement by linking the network structure, the strategy of issuers and the currency choice of users. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.22080 |
| By: | Yang Xu |
| Abstract: | This paper studies a linear production model in team networks with missing links. In the model, heterogeneous workers, represented as nodes, produce jointly and repeatedly within teams, represented as links. Links are omitted when their associated outcome variables fall below a threshold, resulting in partial observability of the network. To address this, I propose a Generalized Method of Moments estimator under normally distributed errors and develop a distribution-free test for detecting link truncation. Applied to academic publication data, the estimator reveals and corrects a substantial downward bias in the estimated scaling factor that aggregates individual fixed effects into team-specific fixed effects. This finding suggests that the collaboration premium may be systematically underestimated when missing links are not properly accounted for. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.08405 |
| By: | Jin-Wook Chang; Grace Chuan |
| Abstract: | The isolated effects of collateral reuse on financial stability are ambiguous and understudied. While greater collateral reuse can guarantee more payments with fewer assets, it can also increase the exposure to potential drops in collateral price. To analyze these tradeoffs, we develop a financial network model with endogenous asset pricing, multiple equilibria, and equilibrium selection. We find that more collateral reuse decreases the likelihood of the worst equilibrium (crisis), with varying effects depending on the network structure. Therefore, collateral reuse can unambiguously improve financial stability for a fixed degree of risk-taking behavior. However, with endogenous risk-taking, we show that a higher degree of collateral reuse can worsen financial stability through greater risk-taking. As a result, while crises may occur less frequently, their severity would increase, leading to a lower social surplus during crises. |
| Keywords: | Collateral; Collateral reuse; Financial network; Fire sale; Multiple equilibria; Equilibrium selection; Systemic risk |
| JEL: | D49 D53 G01 G21 G33 |
| Date: | 2025–05–20 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-35 |
| By: | Arun G. Chandrasekhar; Matthew O. Jackson |
| Abstract: | We provide an overview of methods for designing and implementing experiments (field, lab, hybrid, and natural) when there are networks of interactions between subjects. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.11313 |
| By: | Marc Homs-Dones; Robert S. MacKay; Bazil Sansom; Yijie Zhou |
| Abstract: | We introduce the Circular Directional Flow Decomposition (CDFD), a new framework for analyzing circularity in weighted directed networks. CDFD separates flow into two components: a circular (divergence-free) component and an acyclic component that carries all nett directional flow. This yields a normalized circularity index between 0 (fully acyclic) and 1 (for networks formed solely by the superposition of cycles), with the complement measuring directionality. This index captures the proportion of flow involved in cycles, and admits a range of interpretations - such as system closure, feedback, weighted strong connectivity, structural redundancy, or inefficiency. Although the decomposition is generally non-unique, we show that the set of all decompositions forms a well-structured geometric space with favourable topological properties. Within this space, we highlight two benchmark decompositions aligned with distinct analytical goals: the maximum circularity solution, which minimizes nett flow, and the Balanced Flow Forwarding (BFF) solution, a unique, locally computable decomposition that distributes circular flow across all feasible cycles in proportion to the original network structure. We demonstrate the interpretive value and computational tractability of both decompositions on synthetic and empirical networks. They outperform existing circularity metrics in detecting meaningful structural variation. The decomposition also enables structural analysis - such as mapping the distribution of cyclic flow - and supports practical applications that require explicit flow allocation or routing, including multilateral netting and efficient transport. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.12546 |
| By: | Rafael Acuna; Aldie Alejandro; Robert Leung |
| Abstract: | Dynasties have long dominated Philippine politics. Despite the theoretical consensus that dynastic rule erodes democratic accountability, there is limited empirical evidence establishing dynasties' true impact on development. A key challenge has been developing robust metrics for characterizing dynasties that facilitate meaningful comparisons across geographies and election cycles. Using election data from 2004 to 2022, we leverage methods from graph theory to develop four indicators to investigate dynastic evolution: Political Herfindahl-Hirschman Index (HHI), measuring dynastic power concentration; Centrality Gini Coefficient (CGC), reflecting inequalities of influence between clan members; Connected Component Density (CCD), representing the degree of inter-clan connection; and Average Community Connectivity (ACC), quantifying intra-clan cohesion. Our analysis reveals three key findings. Firstly, dynasties have grown stronger and more interconnected, occupying an increasing share of elected positions. Dominant clans have also remained tightly knit, but with great power imbalances between members. Secondly, we examine variations in party-hopping between dynastic and non-dynastic candidates. Across every election cycle, party-hopping rates are significantly higher (p |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.21280 |
| By: | Demir, Banu; Javorcik, Beata; Piyush Paritosh Panigrahi |
| Abstract: | This paper explores how improved internet infrastructure impacts supply chains and economic activity, focusing on Turkiye. Using the expansion of fiber-optic networks and firm-to-firm transaction data, the paper finds that better connectivity shifts input sourcing to well-connected regions and diversifies supplier networks. Estimates from a spatial equilibrium model with endogenous network formation and rational inattention show that high-speed internet reduced information acquisition and communication costs. Enhanced connectivity increased real income by 2.2 percent in the median province. The findings underscore the importance of digital infrastructure investments in fostering economic growth by improving supply chain efficiency and broadening firms' access to suppliers. |
| Date: | 2025–05–13 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:11122 |
| By: | Shota FUJISHIMA; Takara SAKAI; Yuki TAKAYAMA |
| Abstract: | This study estimates the structural parameters of a travel time function, which relates traffic volume to travel time, within the context of a traffic assignment model in which travelers strategically select routes to minimize their travel costs, influenced by congestion. The proposed model is formulated as a potential game, enabling the estimation of parameters using the maximum likelihood method based on a stochastic evolutionary process. The impact of congestion pricing on welfare is evaluated using the estimated parameters. Preliminary analysis using the Sioux Falls network shows that congestion pricing enhances overall welfare, even when accounting for estimation errors. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:25055 |
| By: | Mattia Marzi; Francesca Giuffrida; Diego Garlaschelli; Tiziano Squartini |
| Abstract: | The study of probabilistic models for the analysis of complex networks represents a flourishing research field. Among the former, Exponential Random Graphs (ERGs) have gained increasing attention over the years. So far, only linear ERGs have been extensively employed to gain insight into the structural organisation of real-world complex networks. None, however, is capable of accounting for the variance of the empirical degree distribution. To this aim, non-linear ERGs must be considered. After showing that the usual mean-field approximation forces the degree-corrected version of the two-star model to degenerate, we define a fitness-induced variant of it. Such a `softened' model is capable of reproducing the sample variance, while retaining the explanatory power of its linear counterpart, within a purely canonical framework. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.10373 |
| By: | Liexin Cheng; Xue Cheng; Shuaiqiang Liu |
| Abstract: | This paper demonstrates that a broad class of problems in quantitative finance, including those previously addressed using deep neural networks, can be efficiently solved using single-layer neural networks without iterative gradient-based training, namely extreme learning machine (ELM). ELM utilizes a single-layer network with randomly initialized hidden nodes and analytically computed output weights obtained via convex optimization, enabling rapid training and inference. Both supervised and unsupervised learning tasks are explored. In supervised learning, ELM is employed to learn parametric option pricing functions, predict intraday stock returns, and complete implied volatility surfaces. Compared with deep neural networks, Gaussian process regression, and logistic regression, ELM achieves higher computational speed, comparable accuracy, and superior generalization. In unsupervised learning, ELM numerically solves Black-Scholes-type PDEs, and outperforms Physics-Informed Neural Networks in training speed without losing precision. The approximation and generalization abilities of ELM are briefly discussed. The findings establish ELM as a practical and efficient tool for various tasks in quantitative finance. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.09551 |