|
on Network Economics |
| By: | Papastaikoudis, I.; Prodromidis, P.; Watson, J.; Lestas, I. |
| Abstract: | We study a multicommodity trade and production model across multiple subeconomies, focusing on social welfare. Trade flows between subeconomies are represented by a multiplex network. This framework allows us to explore how production shocks in one subeconomy can impact others by examining the interconnected trade dimensions through a Leontief input-output model. The approach aligns with mesoeconomics, assuming cooperation among subeconomies to enhance welfare. We formulate the social welfare problem as a distributed optimization problem using the multiplex network Laplacian matrix and solve it with a primal-dual algorithm. The model functions as a decentralized general equilibrium system, encompassing the production process. |
| Keywords: | Decentralized General Equilibrium, Multiplex Networks, Distributed Optimization, Mesoeconomics, Social Welfare |
| JEL: | C61 C62 C67 D30 D50 D60 D85 R13 |
| Date: | 2025–02–01 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camjip:2503 |
| By: | Avner Seror |
| Abstract: | This paper introduces a network-based method to capture unobserved heterogeneity in consumer microdata. We develop a permutation-based approach that repeatedly samples subsets of choices from each agent and partitions agents into jointly rational types. Aggregating these partitions yields a network that characterizes the unobserved heterogeneity, as edges denote the fraction of times two agents belong to the same type across samples. To evaluate how observable characteristics align with the heterogeneity, we implement permutation tests that shuffle covariate labels across network nodes, thereby generating a null distribution of alignment. We further introduce various network-based measures of alignment that assess whether nodes sharing the same observable values are disproportionately linked or clustered, and introduce standardized effect sizes that measure how strongly each covariate "tilts" the entire network away from random assignment. These non-parametric effect sizes capture the global influence of observables on the heterogeneity structure. We apply the method to grocery expenditure data from the Stanford Basket Dataset. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.13721 |
| By: | Yen-hsuan Tseng |
| Abstract: | Accurate network data are essential in fields such as economics, sociology, and computer science. Aggregated Relational Data (ARD) provides a way to capture network structures using partial data. This article compares two main frameworks for recovering network links from ARD: Bayesian Latent Surface Modeling (BLSM) and Frequentist Penalized Regression (FPR). Using simulation studies and real-world applications, we evaluate their theoretical properties, computational efficiency, and practical utility in domains like financial risk assessment and epidemiology. Key findings emphasize the importance of trait design, privacy considerations, and hybrid modeling approaches to improve scalability and robustness. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.10675 |
| By: | Jochmans, Koen; Bonhomme, Stéphane; Weidner, Martin |
| Abstract: | A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading examples where this is the case are models for panel and network data that feature fixed effects. In this paper, we show how, in the conditional-likelihood setting, estimating equations can be constructed that are orthogonal to any chosen order. Combining these equations with sample splitting yields higher-order bias-corrected estimators of target parameters. In an empirical application we apply our method to a fixed-effect model of team production and obtain estimates of complementarity in production and impacts of counterfactual re-allocations. |
| Keywords: | Neyman-orthogonality; incidental parameter; higher-order bias correction; networks |
| JEL: | C13 C23 C55 |
| Date: | 2025–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:130199 |
| By: | Han Qiu; Hyun Song Shin; Leanne Si Ying Zhang |
| Abstract: | The latest firm-level network data reveal that global value chains have lengthened, although without the accompanying network densification that might indicate that supplier relationships are diversifying. Lengthening of supply chains is especially significant for supplier-customer linkages from China to the United States, where firms from other jurisdictions, notably in Asia, have interposed themselves in the supply chain. Nevertheless, these recent developments have not so far reversed the long-running trend toward greater regional integration of trade in recent decades, especially in Asia. |
| Date: | 2023–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:bis:bisblt:78 |
| By: | Wataru Tamura |
| Abstract: | This paper examines the optimal design of information sharing in organizations. Organizational performance depends on agents adapting to uncertain external environments while coordinating their actions, where coordination incentives and synergies are modeled as graphs (networks). The equilibrium strategies and the principal's objective function are summarized using Laplacian matrices of these graphs. I formulate a Bayesian persuasion problem to determine the optimal public signal and show that it comprises a set of statistics on local states, necessarily including their average, which serves as the organizational goal. When the principal benefits equally from the coordination of any two agents, the choice of disclosed statistics is based on the Laplacian eigenvectors and eigenvalues of the incentive graph. The algebraic connectivity (the second smallest Laplacian eigenvalue) determines the condition for full revelation, while the Laplacian spectral radius (the largest Laplacian eigenvalue) establishes the condition for minimum transparency, where only the average state is disclosed. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.12669 |