nep-net New Economics Papers
on Network Economics
Issue of 2023‒09‒18
eleven papers chosen by
Alfonso Rosa García, Universidad de Murcia

  1. Degree Centrality, von Neumann-Morgenstern Expected Utility and Externalities in Networks By René Van Den Brink; Agnieszka Rusinowska
  2. Causal Interpretation of Linear Social Interaction Models with Endogenous Networks By Tadao Hoshino
  3. Understanding the DeFi Network Through the Lens of a Production-Network Model By Jonathan Chiu; Thorsten V. Koeppl; Hanna Yu; Shengxing Zhang
  4. Ripples into waves: trade networks, economic activity, and asset prices By Chang, Jeffery (Jinfan); Du, Huancheng; Lou, Dong; Polk, Christopher
  5. Network-based allocation of responsibility for GHG emissions By Rosa Van Den Ende; Antoine Mandel; Agnieszka Rusinowska
  6. anetworkapproachtointerbankcontagionriskinsouthafrica By Pierre Nkou Mananga; Shiqiang Lin; Hairui Zhang
  7. Allegations of Sexual Misconduct, Accused Scientists, and Their Research By Rainer Widmann; Michael E. Rose; Marina Chugunova
  8. Wisdom of the Crowds or Ignorance of the Masses? A data-driven guide to WSB By Valentina Semenova; Dragos Gorduza; William Wildi; Xiaowen Dong; Stefan Zohren
  9. Estimating the Spatial Amplification of Damage Caused by Degradation in the Amazon By Rafael Araujo; Juliano Assunção; Marina Hirota; José A. Scheinkman
  10. On sparsity, power-law, and clustering properties of graphex processes By Caron, François; Panero, Francesca; Rousseau, Judith
  11. The Role of Social Connections in the Racial Segregation of US Cities By Tanner Regan; Andreas Diemer; Cheng Keat Tang

  1. By: René Van Den Brink (Department of Economics and Tinbergen Institute, VU University, Amsterdam, The Netherlands); Agnieszka Rusinowska (Centre d'Economie de la Sorbonne, CNRS, Université Paris 1 Panthéon-Sorbonne, Paris School of Economics)
    Abstract: This paper aims to connect the social network literature on centrality measures with the economic literature on von Neumann-Morgenstern expected utility functions using cooperative game theory. The social network literature studies various concepts of network centrality, such as degree, betweenness, connectedness, and so on. This resulted in a great number of network centrality measures, each measuring centrality in a different way. In this paper, we aim to explore which centrality measures can be supported as von Neumann-Morgenstern expected utility functions, reflecting preferences over different network positions in different networks. Besides standard axioms on lotteries and preference relations, we consider neutrality to ordinary risk. We show that this leads to a class of centrality measures that is fully determined by the degrees (i.e. the numbers of neighbours) of the positions in a network. Although this allows for externalities, in the sense that the preferences of a position might depend on the way how other positions are connected, these externalities can be taken into account only by considering the degrees of the network positions. Besides bilateral networks, we extend our result to general cooperative TU-games to give a utility foundation of a class of TU-game solutions containing the Shapley value
    Keywords: weighted network; degree; centrality measure; externalities; neutrality to ordinary risk; expected utility function
    JEL: D85 D81 C02
    Date: 2023–08
  2. By: Tadao Hoshino
    Abstract: This study investigates the causal interpretation of linear social interaction models in the presence of endogeneity in network formation under a heterogeneous treatment effects framework. We consider an experimental setting in which individuals are randomly assigned to treatments while no interventions are made for the network structure. We show that running a linear regression ignoring network endogeneity is not problematic for estimating the average direct treatment effect. However, it leads to sample selection bias and negative-weights problem for the estimation of the average spillover effect. To overcome these problems, we propose using potential peer treatment as an instrumental variable (IV), which is automatically a valid IV for actual spillover exposure. Using this IV, we examine two IV-based estimands and demonstrate that they have a local average treatment-effect-type causal interpretation for the spillover effect.
    Date: 2023–08
  3. By: Jonathan Chiu (Bank of Canada); Thorsten V. Koeppl (Queen's University); Hanna Yu (Bank of Canada); Shengxing Zhang (Peking University HSBC Business School)
    Abstract: Decentralized Finance (DeFi) is composed of a variety of heterogeneous sectors that are interconnected through an input-output network of its tokens. We use a panel data set to empricially document the evolution of the DeFi network across its different sectors. We then employ a standard, theoretical production-network model to measure the value added and service outputs of different DeFi sectors which is fundamentally different from the commonly used metric of Total Value Locked (TVL). Our calibrated model is then used to study DeFi token prices and to predict the equilibrium effects of increasing network interconnectedness.
    Keywords: Blockchain, Crypto, Decentralized Finance, Production Network
    JEL: G2 L14
    Date: 2023–07
  4. By: Chang, Jeffery (Jinfan); Du, Huancheng; Lou, Dong; Polk, Christopher
    Abstract: We exploit information in sovereign CDS spreads and the international trade network to provide causal evidence of the propagation of global economic shocks. We show that trade links are an important source of shock transmission using the natural experiments of the Japanese tsunami and the COVID-19 lockdown in China. We then confirm more general and gradual information flows along the trade network by showing extensive country-level credit/equity cross-sectional return predictability. News about country fundamentals flows primarily from importers to exporters, depends on both direct and indirect links in the trade network, and is magnified by the exporting country's financial vulnerability.
    Keywords: sovereign CDS; return predictability; trade networks; limited attention; information aggregation; 71;733;004; Paul Woolley Center
    JEL: G12 G15 F40
    Date: 2022–07–01
  5. By: Rosa Van Den Ende (Université Paris 1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne and Universität Bielefeld); Antoine Mandel (Université Paris 1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne, Paris School of Economics); Agnieszka Rusinowska (Centre d'Economie de la Sorbonne, CNRS, Université Paris 1 Panthéon-Sorbonne, Paris School of Economics)
    Abstract: We provide an axiomatic approach to the allocation of responsibility for GHG emissions in supply chains. Considering a set of axioms standardly used in networks and decision theory, and consistent with legal principles underlying responsibility, we show that responsibility measures shall be based on exponential discounting of upstream and downstream emissions. From a network theory perspective, the proposed responsibility measure corresponds to a convex combination of the Bonacich centralities for the upstream and downstream weighted adjacency matrices. Scope 1 emissions, consumption-based accounting and income-based accounting are obtained as particular cases of our approach, wich also gives a precise meaning to scope 3 emissions while avoiding double-counting. We apply our approach to the assessment of country-level responsibility for global GHG emissions and to sector-level responsibility in the USA. We examine how the responsibility of sectors/countries varies with the discounting of indirect emissions. We identify three groups of countries/sectors: producers of emissions whose responsibiliy decreases with the discounting factor, consumers of emissions whose responsibility increases with the discounting factor, and an intermediary group whose responsibility mostly depends on the network position and varies non-monotonically with the discounting factor. Overall, our axiomatic approach provides strong normative foundations for the definition of reporting requirements for indirect emissions and for the allocation of responsibility in claims for climate-related loss and damage
    Keywords: upstream and downstream emission responsibilities; supply chains and networks; responsibility measure; axiomatization; Bonacich centrality
    JEL: D85 Q5
    Date: 2023–08
  6. By: Pierre Nkou Mananga; Shiqiang Lin; Hairui Zhang
    Abstract: We investigate the resilience of the South African banking system using a dynamic agent-based model and the DebtRank algorithm. This approach enables us to identify each banks importance and vulnerability in the interbank network and is not limited to listed banks, as previous studies were. We find that larger banks are more systemically important, but a banks interbank-lending-to-equity multiple is significantly correlated with its vulnerability. Our research offers policymakers a direct and practical indicator for improved monitoring of financial stability.
    Date: 2023–09–06
  7. By: Rainer Widmann (MPI-IC); Michael E. Rose (MPI-IC); Marina Chugunova (MPI-IC)
    Abstract: Does the scientific community sanction sexual misconduct? Using a sample of scientists accused of sexual misconduct at US universities, we find that their prior work is cited less after allegations surface. The effect weakens with distance in the coauthorship network, indicating that researchers learn about allegations through their peers. Among the closest peers, male authors react more strongly, suggesting that they feel a greater need to disassociate themselves from the accused. In male-dominated fields, the effects on citations are more muted. Accused scientists are more likely to leave academic research, to move to non-university institutions, and to publish less.
    Keywords: sexual misconduct; scientific community; scientific impact;
    JEL: J16 M14 I23 K4
    Date: 2023–08–21
  8. By: Valentina Semenova; Dragos Gorduza; William Wildi; Xiaowen Dong; Stefan Zohren
    Abstract: A trite yet fundamental question in economics is: What causes large asset price fluctuations? A tenfold rise in the price of GameStop equity, between the 22nd and 28th of January 2021, demonstrated that herding behaviour among retail investors is an important contributing factor. This paper presents a data-driven guide to the forum that started the hype -- WallStreetBets (WSB). Our initial experiments decompose the forum using a large language topic model and network tools. The topic model describes the evolution of the forum over time and shows the persistence of certain topics (such as the market / S\&P500 discussion), and the sporadic interest in others, such as COVID or crude oil. Network analysis allows us to decompose the landscape of retail investors into clusters based on their posting and discussion habits; several large, correlated asset discussion clusters emerge, surrounded by smaller, niche ones. A second set of experiments assesses the impact that WSB discussions have had on the market. We show that forum activity has a Granger-causal relationship with the returns of several assets, some of which are now commonly classified as `meme stocks', while others have gone under the radar. The paper extracts a set of short-term trade signals from posts and long-term (monthly and weekly) trade signals from forum dynamics, and considers their predictive power at different time horizons. In addition to the analysis, the paper presents the dataset, as well as an interactive dashboard, in order to promote further research.
    Date: 2023–08
  9. By: Rafael Araujo; Juliano Assunção; Marina Hirota; José A. Scheinkman
    Abstract: The Amazon rainforests have been undergoing unprecedented levels of human-induced disturbances. In addition to local impacts, such changes are likely to cascade following the eastern-western atmospheric flow generated by trade winds. We propose a model of spatial and temporal interactions created by this flow to estimate the spread of local disturbances to downwind locations along atmospheric trajectories. The spatial component captures cascading effects propagated by neighboring regions while the temporal component captures persistence. All these network effects can be described by a single matrix, acting as a spatial multiplier that amplifies local disturbances. This matrix can be used to easily map where the damage of an initial forest disturbance is amplified and propagated to. We identify regions that are likely to cause the largest impact throughout the basin, and those that are the most vulnerable to shocks caused by remote deforestation. On average, the presence of cascading effects mediated by winds doubles the impact of an initial damage. However, there is heterogeneity in this impact. While damage in some regions does not propagate, in others amplification may reach 250%.
    JEL: C23 Q54 Q57
    Date: 2023–08
  10. By: Caron, François; Panero, Francesca; Rousseau, Judith
    Abstract: This paper investigates properties of the class of graphs based on exchangeable point processes. We provide asymptotic expressions for the number of edges, number of nodes, and degree distributions, identifying four regimes: (i) a dense regime, (ii) a sparse, almost dense regime, (iii) a sparse regime with power-law behaviour, and (iv) an almost extremely sparse regime. We show that, under mild assumptions, both the global and local clustering coefficients converge to constants which may or may not be the same. We also derive a central limit theorem for subgraph counts and for the number of nodes. Finally, we propose a class of models within this framework where one can separately control the latent structure and the global sparsity/power-law properties of the graph.
    Keywords: community structure; generalised graphon; Networks; Poisson processes; power law; sparsity; subgraph counts; transitivity; EPSRC and MRC Centre for Doctoral Training in Statistical Science (grant code EP/L016710/1; European Union’s Horizon 2020 research and innovation programme (grant agreement no. 834175
    JEL: C1
    Date: 2023–06–16
  11. By: Tanner Regan (George Washington University); Andreas Diemer (Stockholm University); Cheng Keat Tang (Nanyang Technological University)
    Abstract: We study the extent of segregation in the social space of urban America. We measure segregation as the (lack of) actual personal connections between groups as opposed to conventional measures based on own neighbourhood composition. We distinguish social segregation from geographical definitions of segregation, and build and compare city-level indices of each. Conditional on residential segregation, cities with more institutions that foster social cohesion (churches and community associations) are less socially segregated. Looking at within-city variation across neighbourhoods, growing up more socially exposed to non-white neighbourhoods is related to various adulthood outcomes (jailed, income rank, married, and non-migrant) for black individuals. Social exposure to non-white neighbourhoods is always related to worsening adulthood outcomes in neighbourhoods that are majority non-white. Our results suggest that social connections, beyond residential location or other spatial relationships, are important for understanding the effective segregation of race in America.
    Keywords: Segregation; Social Networks; US cities
    JEL: R23 J15
    Date: 2023–05

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