nep-net New Economics Papers
on Network Economics
Issue of 2022‒12‒19
thirteen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. #IamLGBT: Social Networks and Coming Out By Jan Gromadzki; Przemysław Siemaszko
  2. Unconfoundedness with Network Interference By Michael P. Leung; Pantelis Loupos
  3. Sparse production networks By Andrew B. Bernard; Yuan Zi
  4. On the Empirical Association between Trade Network Complexity and Global Gross Domestic Product By Mayank Kejriwal; Yuesheng Luo
  5. False Information from Near and Far By Christophe Bravard; Jacques Durieu; Sudipta Sarangi; Stéphan Sémirat
  6. Model of spatial competition on discrete markets By Andrea Civilini; Vito Latora
  7. Social networks and resilience in emerging labor markets By Paola Tubaro
  8. Dollar-Yuan Battle in the World Trade Network By C\'elestin Coquid\'e; Jos\'e Lages; Dima L. Shepelyansky
  9. Stable and metastable contract networks By Danilov, Vladimir; Karzanov, Alexander
  10. A complex networks based analysis of jump risk in equity returns: An evidence using intraday movements from Pakistan stock market By Faheem Aslam; Yasir Tariq Mohmand; Saqib Aziz; Jamal Ouenniche
  11. Multimodal Transport Networks By Woan Foong Wong; Simon Fuchs
  12. Peer-to-peer solar and social rewards: evidence from a field experiment By Carattini, Stefano; Gillingham, Kenneth T.; Meng, Xiangyu; Yoeli, Erez
  13. Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks By Chong Mo; Song Li; Geoffrey K. F. Tso; Jiandong Zhou; Yiyan Qi; Mingjie Zhu

  1. By: Jan Gromadzki; Przemysław Siemaszko
    Abstract: In recent decades, the number of people disclosing their LGBTQ identity has increased substantially. We investigate the role of peer effects in coming out decisions using a model of a game social learning via networks. We use newly collected data from two waves of a spontaneous Twitter coming out campaign to test the prediction that observing peers coming out increases the probability of an individual disclosing their LGBTQ identity. We combine data on users' pre-campaign networks with the information on the exact time of costly coming out actions to construct a time-varying measure of the exposure to peers coming out as LGBTQ. A one standard deviation increase in the exposure increases the probability of coming out by almost 20%. We also exploit the non-overlapping network structure of users' peers groups as an exogenous source of variation, and we confirm the baseline results. We argue that the estimated effects are due to changes in beliefs about the costs of disclosure.
    Keywords: LGBTQ; social networks; peer effects; social media; cultural change
    JEL: J15 D85 D74 P16 Z13
    Date: 2022–10
  2. By: Michael P. Leung; Pantelis Loupos
    Abstract: This paper studies nonparametric estimation of treatment and spillover effects using observational data from a single large network. We consider a model of network interference that allows for peer influence in selection into treatment or outcomes but requires influence to decay with network distance. In this setting, the network and covariates of all units can be potential sources of confounding, in contrast to existing work that assumes confounding is limited to a known, low-dimensional function of these objects. To estimate the first-stage nuisance functions of the doubly robust estimator, we propose to use graph neural networks, which are designed to approximate functions of graph-structured inputs. Under our model of interference, we derive primitive conditions for a network analog of approximate sparsity, which provides justification for the use of shallow architectures.
    Date: 2022–11
  3. By: Andrew B. Bernard; Yuan Zi
    Abstract: Firm-to-firm connections in domestic and international production networks play a fundamental role in economic outcomes. Firm heterogeneity and the sparse nature of firm-to-firm connections implicitly discipline network structure. We find that a large group of well-established statistical relationships are not useful in improving our understanding of production networks. We propose an "elementary" model for production networks based on random matching and firm heterogeneity and characterize the families of statistics and data generating processes that may raise underidentification concerns in more complex models. The elementary model is a useful benchmark in developing "instructive" statistics and informing model construction and selection.
    Keywords: firm-to-firm networks, model selection, balls-and-bins, buyer-seller matching, underidentification
    Date: 2022–10–17
  4. By: Mayank Kejriwal; Yuesheng Luo
    Abstract: In recent decades, trade between nations has constituted an important component of global Gross Domestic Product (GDP), with official estimates showing that it likely accounted for a quarter of total global production. While evidence of association already exists in macro-economic data between trade volume and GDP growth, there is considerably less work on whether, at the level of individual granular sectors (such as vehicles or minerals), associations exist between the complexity of trading networks and global GDP. In this paper, we explore this question by using publicly available data from the Atlas of Economic Complexity project to rigorously construct global trade networks between nations across multiple sectors, and studying the correlation between network-theoretic measures computed on these networks (such as average clustering coefficient and density) and global GDP. We find that there is indeed significant association between trade networks' complexity and global GDP across almost every sector, and that network metrics also correlate with business cycle phenomena such as the Great Recession of 2007-2008. Our results show that trade volume alone cannot explain global GDP growth, and that network science may prove to be a valuable empirical avenue for studying complexity in macro-economic phenomena such as trade.
    Date: 2022–11
  5. By: Christophe Bravard (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); Jacques Durieu (CREG - Centre de recherche en économie de Grenoble - UPMF - Université Pierre Mendès France - Grenoble 2, 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); Sudipta Sarangi (DIW Berlin - Deutsches Institut für Wirtschaftsforschung, Virginia Tech [Blacksburg]); Stéphan Sémirat (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)
    Abstract: We study message credibility in social networks with biased and unbiased agents. Biased agents prefer a specific outcome while unbiased agents prefer the true state of the world. Each agent who receives a message knows the identity (but not type) of the message creator and only the identity and types of their immediate neighbors. We characterize the perfect Bayesian equilibria of this game and demonstrate filtering by the network: the posterior beliefs of agents depend on the distance a message travels. Unbiased agents, who receive a message from a biased agent, are more likely to assign a higher credibility and transmit it further when they are further away from the source. For a given network, we compute the probability that it will always support the communication of messages by unbiased agents. Finally, we establish that under certain parameters, this probability increases when agents are uncertain about their network location.
    Keywords: Influential Players,Filter,Network
    Date: 2022
  6. By: Andrea Civilini; Vito Latora
    Abstract: We propose a dynamical model of price formation on a spatial market where sellers and buyers are placed on the nodes of a graph, and the distribution of the buyers depends on the positions and prices of the sellers. We find that, depending on the positions of the sellers and on the level of information available, the price dynamics of our model can either converge to fixed prices, or produce cycles of different amplitudes and periods. We show how to measure the strength of competition in a spatial network by extracting the exponent of the scaling of the prices with the size of the system. As an application, we characterize the different level of competition in street networks of real cities across the globe. Finally, using the model dynamics we can define a novel measure of node centrality, which quantifies the relevance of a node in a competitive market.
    Date: 2022–11
  7. By: Paola Tubaro (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique, ENSAE - Ecole Nationale de la Statistique et de l'Analyse Economique - Ecole Nationale de la Statistique et de l'Analyse Economique, IP Paris - Institut Polytechnique de Paris)
    Abstract: The recent emergence of digital platforms as labor market intermediaries disrupts collective work practices, fostering fragmentation and individualized subcontracting. In these environments where isolation dominates, how do social networks operate, and how do they support social resilience? And how can we, as researchers, apprehend them? To address these questions, this chapter reviews insights from socioeconomic studies of networks, discusses their applicability to platforms, compares and contrasts them to existing evidence on platform work. The analysis confirms that overall, technologyenabled platform intermediation restrains sociability and limits interactions, but specific cases where networking has been possible highlight the fundamental advantages it may have for workers, and suggest directions for future research and policy action.
    Keywords: Labor markets,digital platforms,decent work,economic networks,formal/informal networks,multi-level networks
    Date: 2022–11
  8. By: C\'elestin Coquid\'e; Jos\'e Lages; Dima L. Shepelyansky
    Abstract: From the Bretton Woods agreement in 1944 till the present day, the US dollar has been the dominant currency in the world trade. However, the rise of the Chinese economy led recently to the emergence of trade transactions in Chinese yuan. Here, we analyze mathematically how the structure of the international trade flows would favor a country to trade whether in US dollar or in Chinese yuan. The computation of the trade currency preference is based on the world trade network built from the 2010-2020 UN Comtrade data. The preference of a country to trade in US dollar or Chinese yuan is determined by two multiplicative factors: the relative weight of trade volume exchanged by the country with its direct trade partners, and the relative weight of its trade partners in the global international trade. The performed analysis, based on Ising spin interactions on the world trade network, shows that, from 2010 to present, a transition took place, and the majority of the world countries would have now a preference to trade in Chinese yuan if one only consider the world trade network structure.
    Date: 2022–11
  9. By: Danilov, Vladimir; Karzanov, Alexander
    Abstract: We consider a hypergraph (I, C), with possible multiple (hyper)edges and loops, in which the vertices i ∈ I are interpreted as agents, and the edges c ∈ C as contracts that can be concluded between agents. The preferences of each agent i concerning the contracts where i takes part are given by use of a choice function fi possessing the so-called path independent property. In this general setup we introduce the notion of stable network of contracts. The paper contains two main results. The first one is that a general problem on stable systems of contracts for (I, C, f) is reduced to a set of special ones in which preferences of agents are described by use of so-called weak orders, or utility functions. However, for a special case of this sort, the stability may not exist. Trying to overcome this trouble when dealing with such special cases, we introduce a weaker notion of metastability for systems of contracts. Our second result is that a metastable system always exists.
    Keywords: Plott choice functions, Aizerman-Malishevski theorem, stable marriage, roommate problem, Scarf lemma
    JEL: C71 C78 D74
    Date: 2022–11–29
  10. By: Faheem Aslam (COMSATS University Islamabad); Yasir Tariq Mohmand (COMSATS University Islamabad); Saqib Aziz (ESC [Rennes] - ESC Rennes School of Business); Jamal Ouenniche (University of Edinburgh)
    Abstract: We employ a multi-stage methodology combining complex network analytics and financial risk modelling to unveil the correlation structures amongst the price jump risks of companies forming the KSE-100 index in Pakistan. We identify the most influential companies in terms of jump risk, and identify communities — clusters of companies with similar price movement characteristics or with highly correlated price jumps. We find that equities in Pakistan stock market experience jumps in different time periods that are correlated to varying degrees within and across industries resulting in 19 different communities, four of which are strongly connected. While Oil & Gas, Cement and Banking sectors exhibit a significant representation of firms in communities, the automobile industry, however, seems to play an important role in risk propagation. These results provide an interesting insight to investors and other stakeholders from an emerging market viewpoint identifying the major sectors driving the volatility of KSE-100 index.
    Keywords: Complex network analysis,Intraday returns,Realised jumps,Realised volatility,Jump risk
    Date: 2020–12
  11. By: Woan Foong Wong; Simon Fuchs
    Abstract: The movement of goods from origin to destination takes place over multiple modes of transportation. Correspondingly, intermodal terminals play an important role in facilitating transportation over the multimodal network. This paper studies multimodal transport networks and their impact on infrastructure investments. We propose a tractable theory of transportation across domestic transportation networks with multiple modes of transportation by embedding multimodal routes into a spatial equilibrium model with endogenous stochastic route choice. We calibrate the model to US domestic freight flows using high-resolution geographic information system information and detailed data on traffic along road, rail, and international ports. We estimate the strength of intermodal port congestion from ship dwell times and its multimodal impact on railcar dwell times. We then employ the model to evaluate the welfare effects of terminal investments across the United States. We identify important bottlenecks in the US transportation system, with the reduction of the transportation cost by 1 percent in the most important nodes generating welfare gains equivalent to $US200–300 million of additional GDP (in 2012 USD).
    Keywords: infrastructure investments; multimodal transport; spatial equilibrium
    JEL: F11 R12 R42
    Date: 2022–10–05
  12. By: Carattini, Stefano; Gillingham, Kenneth T.; Meng, Xiangyu; Yoeli, Erez
    Abstract: Observability and social rewards have been demonstrated to influence the adoption of pro-social behavior in a variety of contexts. This study implements a field experiment to examine the influence of observability and social rewards in the context of a novel pro-social behavior: peer-to-peer solar. Peer-to-peer solar offers an opportunity to households who cannot have solar on their homes to access solar energy from their neighbors. However, unlike solar installations, peer-to-peer solar is an invisible form of pro-environmental behavior. We implemented a set of randomized campaigns using Facebook ads in the Massachusetts cities of Cambridge and Somerville, in partnership with a peer-to-peer company, which agreed to offer to a subsample of customers the possibility to share “green reports” online, providing shareable information about their greenness. We find that interest in peer-to-peer solar increases by up to 30% when “green reports,” which would make otherwise invisible behavior visible, are mentioned in the ads
    Keywords: Peer to peer solar; pro-environmental behavior; social rewards; visibility; Facebook
    JEL: C93 D91 Q20
    Date: 2022–11–09
  13. By: Chong Mo; Song Li; Geoffrey K. F. Tso; Jiandong Zhou; Yiyan Qi; Mingjie Zhu
    Abstract: Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature.
    Date: 2022–11

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