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on Network Economics |
| By: | Christopher P. Chambers; Yusufcan Masatlioglu; Christopher Turansick |
| Abstract: | People are influenced by their peers when making decisions. In this paper, we study the linear-in-means model which is the standard empirical model of peer effects. As data on the underlying social network is often difficult to come by, we focus on data that only captures an agent's choices. Under exogenous agent participation variation, we study two questions. We first develop a revealed preference style test for the linear-in-means model. We then study the identification properties of the linear-in-means model. With sufficient participation variation, we show how an analyst is able to recover the underlying network structure and social influence parameters from choice data. Our identification result holds when we allow the social network to vary across contexts. To recover predictive power, we consider a refinement which allows us to extrapolate the underlying network structure across groups and provide a test of this version of the model. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.02609 |
| By: | Marsman, Maarten; Waldorp, Lourens; Sekulovski, Nikola (University of Amsterdam); Haslbeck, Jonas M B |
| Abstract: | Multivariate analysis of psychological variables using graphical models has become a standard analysis in the psychometric literature. Most cross-sectional measures are either binary or ordinal, and the methodology for inferring the structure of networks of binary and ordinal variables is developing rapidly. In practice, however, research questions often focus on whether and how networks differ between observed groups. While Bayes factor methods for inferring network structure are well established, a similar methodology for assessing group differences in networks of binary or ordinal variables is currently lacking. In this paper, we extend the Bayesian framework for the analysis of ordinal Markov random fields, a network model for binary and ordinal variables, and develop Bayes factor tests for assessing parameter differences in the networks of two independent groups. The proposed methods are implemented in the R package \texttt{bgms}, and we use numerical illustrations to show that the implemented methods work correctly and how well the methods work compared to existing methods in situations resembling empirical research. |
| Date: | 2024–12–20 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:f4pk9 |
| By: | Tseng, Yen-hsuan |
| Abstract: | Accurate network data are essential in fields such as economics, finance, sociology, epidemiology, and computer science. However, real-world constraints often prevent researchers from collect- ing a complete adjacency matrix, compelling them to rely on partial or aggregated information. One widespread example is Aggregated Relational Data (ARD), where respondents or institutions merely report the number of links they have to nodes possessing certain traits, rather than enu- merating all neighbors explicitly. This dissertation provides an in-depth examination of two major frameworks for reconstruct- ing networks from ARD: the Bayesian latent surface model and frequentist penalized regression ap- proaches. We supplement the original discussion with additional theoretical considerations on identifiability, consistency, and potential misreporting mechanisms. We also incorporate robust estimation techniques and references to privacy-preserving strategies such as differential privacy. By embedding nodes in a hyperspherical space, the Bayesian method captures geometric distance- based link formation, while the penalized regression approach casts unknown edges in a high- dimensional optimization problem, enabling scalability and the incorporation of covariates. Sim- ulations explore the effects of trait design, measurement error, and sample size. Real-world ap- plications illustrate the potential for partially observed networks in domains like financial risk, social recommendation systems, and epidemic contact tracing, complementing the original text with deeper investigations of large-scale inference challenges. Our aim is to show that even though ARD may be coarser than full adjacency data, it retains sub- stantial information about network structures, allowing reasonably accurate inference at scale. We conclude by discussing how adaptive trait selection, hybrid geometry-penalty methods, and privacy- aware data sharing can further advance this field. This enhanced treatment underscores the prac- tical relevance and theoretical rigor of ARD-based network inference. |
| Keywords: | Aggregated Relational Data (ARD) Network Inference Bayesian Latent Surface Model (BLSM) Penalized Regression Hyperspherical Embedding Differential Privacy Federated Learning Privacy-Preserving Networks Robust Estimation Misreporting in Networks High-Dimensional Optimization Sparse Networks Social Recommendation Systems Financial Interbank Networks Epidemic Contact Tracing |
| JEL: | C38 C55 C81 D85 |
| Date: | 2025–01–03 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123164 |
| By: | Demir, Gökay (IZA); Hertweck, Friederike (RWI – Leibniz Institute for Economic Research); Sandner, Malte (Technische Hochschule Nürnberg); Yükselen, Ipek (Institute for Employment Research (IAB), Nuremberg) |
| Abstract: | This paper analyzes the impact of college students' coworker networks formed during student jobs on their labor market outcomes after graduation. For our analysis, we use novel data that links students' administrative university records with their pre- and post-graduation employment registry data and their coworker networks. Our empirical strategy exploits variation in the timing and duration of student jobs, controlling for a variety of individual and network characteristics, as well as firm-by-occupation fixed effects, eliminating potential selection bias arising from non-random entry into student jobs and networks. The results show that students who work alongside higher-earning coworkers during their student jobs earn higher wages in their first post-graduation employment. Two key mechanisms appear to drive this effect: (1) sorting into higher-paying firms after graduation, facilitated by coworker referrals, and (2) enhanced field-specific human capital through exposure to skilled colleagues. However, the initial wage advantage from higher-earning coworker networks diminishes over time as students with worse networks catch up. Our findings contribute to the understanding of how early career networks shape labor market outcomes and facilitate a smoother transition from higher education to graduate employment. |
| Keywords: | labor market entry, student jobs, coworker networks, wages |
| JEL: | I23 J24 J31 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17541 |
| By: | Stephan Peuhringer (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria); Matthias Aistleitner (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria); Lukas Cserjan (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria); Sophie Hieselmayr (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria); Jan Weber |
| Abstract: | The increasing concentration of income and wealth on the national and international level is a topic that has received increased attention both in social science research as well as public policy debates. While data availability is a well-known and often-lamented problem in wealth studies, especially the group of HNW-households remains largely unexplored. In this study, we aim to contribute to a deeper understanding of the impact of HNW-households and their networks on current wealth distributions. Based on an extensive data set of company ownerships of a sample of the 62 wealthiest Austrian HNW-households, we apply a social network analysis of two-mode networks (institutions and persons) to highlight networks of corporate ownership and (indirect) control. An overall finding is that numerous HNW-networks involve a multitude of legal entities, creating complex and opaque control structures that complicates the tracing of economic ownership. Besides this, our findings show several idiosyncrasies of the super-rich. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:ico:wpaper:157 |
| By: | Lee, Hane; Davison, Andrew; Ying, Zhiliang |
| Abstract: | Congressional literature suggests that the motivations behind roll call votes are complex, spanning the legislator's ideology, party strategies, and social influences. In terms of methodology, latent factor models have dominated roll call analysis, where the estimated "ideal points" are interpreted as the legislators' partisan-ideological positions, but these models do not account for partisan or social motivations behind the votes. On the other hand, some researchers have explored the social influence behind these votes using network models, but this approach often overlooks the role of ideology or parties. We address this gap by integrating the partisan-ideological and social approaches through a fused latent factor and social network model. This model decomposes the effects of partisan-ideology and social connections on roll call votes while giving priority to the former. Additionally, our method provides a direct measurement of social ties from roll call votes, rather than relying on proxies such as cosponsorship to first estimate the social effect and later make connections to political outcomes. We apply our model to the 101st Senate and find that the model successfully decomposes ideology and partisanship from social ties. The estimated social network captures notable friendships and geographical communities. We also demonstrate that cosponsorship and shared committee membership, commonly viewed as indicators of social connections, are either closely aligned with the legislator's revealed partisan-ideological preferences or have minimal legislative impact. |
| Date: | 2024–11–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:6euf3 |
| By: | Riccardo De Blasis; Luca Galati; Filippo Petroni |
| Abstract: | Understanding the dependencies among financial assets is critical for portfolio optimization. Traditional approaches based on correlation networks often fail to capture the nonlinear and directional relationships that exist in financial markets. In this study, we construct directed and weighted financial networks using the Mixture Transition Distribution (MTD) model, offering a richer representation of asset interdependencies. We apply local assortativity measures--metrics that evaluate how assets connect based on similarities or differences--to guide portfolio selection and allocation. Using data from the Dow Jones 30, Euro Stoxx 50, and FTSE 100 indices constituents, we show that portfolios optimized with network-based assortativity measures consistently outperform the classical mean-variance framework. Notably, modalities in which assets with differing characteristics connect enhance diversification and improve Sharpe ratios. The directed nature of MTD-based networks effectively captures complex relationships, yielding portfolios with superior risk-adjusted returns. Our findings highlight the utility of network-based methodologies in financial decision-making, demonstrating their ability to refine portfolio optimization strategies. This work thus underscores the potential of leveraging advanced financial networks to achieve enhanced performance, offering valuable insights for practitioners and setting a foundation for future research. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.04646 |
| By: | Federica Cappelli (Università degli studi di Ferrara) |
| Abstract: | The European Union's energy security is increasingly challenged by its heavy dependence on imported oil, which exposes the region to geopolitical risks and market vulnerabilities. This study explores the role of trade dynamics in exacerbating this dependency, leading to what we term trade lock-in. Additionally, we assess the effectiveness of environmental policies in reducing oil import dependence, investigating whether these policies foster a shift toward greener investments (divestment effect) or inadvertently drive increased oil extraction (green paradox effect). We use network analysis to represent the international oil trade network and use this information in an econometric framework covering the period from 1999 to 2019, accounting for the presence of cross-sectional dependence. We identify two main factors that lock energy systems into an oil-based path: technological (represented by the level of energy intensity) and trade (represented by the existence of privileged trade relations with major oil-exporting countries) lock-ins. Furthermore, we find evidence of the divestment effect for some specific environmental policy instruments, but the effect is not uniform across instruments characterised as either demand-pull or technology-push. Finally, we find that an efficient eco-innovation system can effectively reduce oil import dependence only in countries with a comparative advantage in exporting clean technologies. |
| Keywords: | oil dependence; network analysis; environmental policy; technological change; European Union |
| JEL: | F18 O32 Q32 Q37 Q48 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:srt:wpaper:0125 |
| By: | Marcel Fafchamps; Asadul Islam; Debayan Pakrashi; Denni Tommasi |
| Abstract: | We conduct a clustered randomized controlled trial across 180 villages in Uttar Pradesh, India, to promote the take-up of a savings commitment product newly introduced to our study population. A random subset of participants was targeted through our promotional campaign to test whether the product's diffusion among untargeted participants operates primarily through information sharing or through persuasion by incentivized target participants. If social learning is the main channel of diffusion, we would expect higher sign-up and take-up rates in information villages compared to persuasion villages. Conversely, if persuasion is the primary channel, sign-up and take-up rates should be higher in persuasion villages. Our findings consistently favor the persuasion channel, as sign-up and take-up rates were higher in the persuasion treatment, even without increased financial literacy or knowledge about the product. Information alone had a negligible impact on take-up, while the combined treatment achieved the highest sign-up and conversion rates, suggesting that information complements persuasion by enhancing its effectiveness. These results highlight the importance of incentivized persuasion in promoting product take-up and suggest that, in certain contexts, direct information-sharing may be less effective than previously assumed. |
| JEL: | D14 G21 O16 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33285 |
| By: | Lennart Ante; Aman Saggu |
| Abstract: | The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite pro-cessing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally in-fluence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.05299 |
| By: | Hrishidev Unni; Rubal Rathi; Sangita Dutta Gupta; Anirban Chakraborti |
| Abstract: | The Sustainable Development Goals (SDGs) offer a critical global framework for addressing challenges like poverty, inequality, climate change, etc. They encourage a holistic approach integrating economic growth, social inclusion, and environmental sustainability to create a better future. We aim to examine India's responsibility in achieving the SDGs by recognizing the contributions of its diverse states in the federal structure of governance. As the nodal agency in India, the NITI Aayog's existing SDG index, using various socioeconomic indicators to determine the performance across different goals, serves as a foundation for assessing each state's progress. Building on the seminal works of Hidalgo and Hausmann (2009) and Tachhella et al. (2012), which introduced the economic complexity/fitness index, Sciarra et al. (2020) proposed the SDGs-Generalized Economic Complexity (GENEPY) framework to quantify "complexity" by computing "ranks for states" and "scores for goals", treating them as part of a complex bipartite network. In this paper, we apply the SDGs-GENEPY, to evaluate the progress and evolution of Indian states and union territories over several years. This enables us to identify each state's capacity (and rank) in achieving the SDGs. We can interpret these complexity scores as "centrality measures" of a complex bipartite network of the states and the goals. This enhances our understanding of the complex relationship between state capabilities and the achievability of SDGs within the Indian context and enables data-driven policy-making. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2501.05314 |
| By: | Herrera Gómez Marcos; Elosegui Pedro; Michelena Gabriel |
| Abstract: | Multi-regional input-output (MRIO) matrices are an important tool for regional economic analysis, but compiling the data for them remains challenging, especially in developing countries like Argentina. There is no consistent, up-to-date, official national I-O table available for Argentina, and data at the provincial level is limited and fragmented across different sources. This paper develops a premier (limited information) multi-regional input-output matrix for Argentina 2019 making a dual contribution: (i) constructing the first MRIO table for Argentina using official and customized sources, and (ii) evaluating I-O multipliers, providing insights for future applications. The MRIO table includes 5 regions aggregating the 24 Argentinean provinces and 20 economic sectors. While only basic multipliers are presented, the table provides a foundation for more in-depth input-output modeling and analysis of production, consumption, and trade linkages between regions and sectors in Argentina. We found a high concentration in the provinces of the Pampeana region in gross output, value added and regional internal inputs, although less in external inputs, confirming the asymmetric structure of the country. In addition, the analysis of multipliers allows us to detect some relevant links in the peripheral regions reflecting the interaction of spatial location and sector specialization in a federal and heterogeneous open developing economy. |
| JEL: | C67 D57 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:aep:anales:4739 |