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on Network Economics |
| By: | Johnsen, Julian V. (SNF, Bergen); Khoury, Laura (PSL Université Paris Dauphine) |
| Abstract: | Peer interactions play a key role in the criminal sector due to its secrecy and lack of formal institutions. A significant part of criminal peer exposure happens in prison, directly influenced by policymakers. This paper provides a broader understanding of how peer effects shape criminal behavior among prison inmates, focusing on co-inmate impacts on recidivism and criminal network formation. Using Norwegian register data on over 140, 000 prison spells, we causally identify peer effects through within-prison variation in peers over time. Our analysis reveals several new insights. First, exposure to more experienced co-inmates increases recidivism. Second, exposure to "top criminals" (i.e. those with extreme levels of criminal experience) plays a distinctive role in shaping these recidivism patterns. Third, inmates form lasting criminal networks, as proxied by post-incarceration co-offending. Fourth, homophily intensifies these peer effects. These findings contribute to the theoretical understanding of peer influences in criminal activities and offer practical insights for reducing recidivism through strategic inmate grouping and prison management policies. |
| Keywords: | prison inmates, incarceration, criminal behavior, criminal experi- ence, criminal networks, recidivism |
| JEL: | K14 K42 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17114 |
| By: | Giorgio Fabbri (GAEL - Laboratoire d'Economie Appliquée de Grenoble - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Silvia Faggian (Université de Venise Ca’ Foscari | Università Ca’ Foscari di Venezia); Giuseppe Freni (PARTHENOPE - Università degli Studi di Napoli “Parthenope” = University of Naples) |
| Abstract: | This study examines the dynamics of the exploitation of a natural resource distributed among and flowing between several nodes connected via a weighted, directed network. The network represents the locations and interactions of the resource nodes. A regulator decides to designate some of the nodes as natural reserves where no exploitation is allowed. The remaining nodes are assigned (one‐to‐one) to players, who exploit the resource at the node. It is demonstrated how the equilibrium exploitation and resource stocks depend on the productivity of the resource sites, the structure of the connections between the sites, and the number and preferences of the agents. The best locations to host nature reserves are identified per the model's parameters and correspond to the most central (in the sense of eigenvector centrality) nodes of a suitably redefined network that considers the nodes' productivity. |
| Keywords: | Harvesting, spatial models, differential games, nature reserves |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04612475 |
| By: | Yuqing Song |
| Abstract: | Collaboration is a common artistic practice. The art market, however, often focuses on individual artists. How does collaboration affect the market value of artworks? The present study explores the market for collaborative paintings and calligraphy with a focus on one of the most important artists of the 20th century, Zhang Daqian (1899-1983), the “Picasso of the East.” We reveal a collaboration network of 247 nodes and 782 edges, spreading across three stages of the artist’s career. We provide evidence that, on average, collaborative artworks fetch lower prices than single-authored ones. Interestingly, not all collaborators lower prices equally. Network analysis suggests an inversely U-shaped relationship between a collaborator’s centrality and art prices. The paper sheds light on the mechanisms driving value in the market for collaborative artworks. |
| Keywords: | Collaboration; Collaborative paintings; Celebrity branding; Zhang Daqian; Chinese art market |
| Date: | 2024–07–09 |
| URL: | https://d.repec.org/n?u=RePEc:sol:wpaper:2013/375963 |
| By: | Yu Cheng; Junjie Guo; Shiqing Long; You Wu; Mengfang Sun; Rong Zhang |
| Abstract: | The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. This intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model's understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.06529 |
| By: | Krishna Dasaratha; Santosh Venkatesh; Rakesh Vohra |
| Abstract: | Widespread default involves substantial deadweight costs which could be countered by injecting capital into failing firms. Injections have positive spillovers that can trigger a repayment cascade. But which firms should a regulator bailout so as to minimize the total injection of capital while ensuring solvency of all firms? While the problem is, in general, NP-hard, for a wide range of networks that arise from a stochastic block model, we show that the optimal bailout can be implemented by a simple policy that targets firms based on their characteristics and position in the network. Specific examples of the setting include core-periphery networks. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.12818 |
| By: | Alexandre Truc (Université Côte d'Azur, CNRS, GREDEG, France); Muriel Dal Pont Legrand (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | In the present paper, we investigate the diffusion of agent-based models (ABMs) in economics using a quantitative approach to better understand how the introduction of this tool in economics influenced the structure of the field as well as research programs in recent years. Our analysis shows that the proliferation of ABMs has resulted in the emergence of diverse research subfields rather than one unified research program. Most notably, we highlight how interdisciplinarity plays a pivotal role in understanding the diversity of ways in which agent-based models are integrated into economics. While in some cases ABMs are used by economists as an imported tool to address disciplinary-oriented questions in dedicated subfields journals, in other cases ABMs are a vehicle for more interdisciplinary transfers and interactions (e.g., interdisciplinary co-authorship) that are more challenging to the traditional frontiers of economics. |
| Keywords: | Agent-Based, Interdisciplinarity, Social Network Analysis |
| JEL: | B2 B21 B4 D9 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2024-19 |
| By: | Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); Matteo Foglia (Department of Economics and Finance, University of Bari “Aldo Moro†, Italy); Sayar Karmakar (Department of Statistics, University of Florida, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018 - January 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVID-19 pandemic and Russia-Ukraine conflict, and Bitcoin and Ethereum emerge are net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant risk-return relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the return-volatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management. |
| Keywords: | Multilayer networks, Spillover effects, return-volatility, cryptocurrencies, NFTs, DeFi, COVID-19, Russia-Ukraine conflict |
| JEL: | C32 G10 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202432 |