nep-net New Economics Papers
on Network Economics
Issue of 2020‒11‒02
eight papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Network Comparative Statics By Harkins, Andrew
  2. Assessing the Impact of Social Network Structure on the Diffusion of Coronavirus Disease (COVID-19): A Generalized Spatial SEIRD Model By Giorgio Fagiolo
  3. The Role of Social Networks in Bank Lending By Oliver Rehbein; Simon Rother
  4. “Friends Are Thieves of Time": Heuristic Attention Sharing in Stable Friendship Networks By Tenev, Anastas P.
  5. On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity By Ruben Hipp
  6. Network innovation versus innovation through networks By M.Z. Yaqub; Marijana Sreckovic; Gérard Cliquet; G. Hendrikse; J. Windsperger
  7. INDUSTRIAL CLUSTERS, NETWORKS AND RESILIENCE TO THE COVID-19 SHOCK IN CHINA By Ruochen Dai; Dilip Mookherjee; Yingyue Quan; Xiaobo Zhang
  8. The Invisible Collateral By Muduli, Silu; Dash, Shridhar Kumar

  1. By: Harkins, Andrew (University of Warwick)
    Abstract: This paper develops a framework for analyzing the effect of arbitrary changes to network structure in linear-quadratic games on networks. Changes to network structure which increase total activity and total utility are studied for the case of strategic complements and strategic substitutes. Changes which are welfare increasing are found to depend on a new measure of centrality which counts the total length of walks from a node. Two optimal network design problems are then considered. Total activity is found to be a convex function of the edge weights of the network, which allows for convex optimization techniques to be applied to minimize total activity as in the traditional ‘key player’ problem. Welfare maximizing network structures are also studied and previous results which associate optimal networks with nested split graphs are generalized.
    Date: 2020
  2. By: Giorgio Fagiolo
    Abstract: In this paper, I study epidemic diffusion in a generalized spatial SEIRD model, where individuals are initially connected in a social or geographical network. As the virus spreads in the network, the structure of interactions between people may endogenously change over time, due to quarantining measures and/or spatial-distancing policies. I explore via simulations the dynamic properties of the co-evolutionary process dynamically linking disease diffusion and network properties. Results suggest that, in order to predict how epidemic phenomena evolve in networked populations, it is not enough to focus on the properties of initial interaction structures. Indeed, the co-evolution of network structures and compartment shares strongly shape the process of epidemic diffusion, especially in terms of its speed. Furthermore, I show that the timing and features of spatial-distancing policies may dramatically influence their effectiveness.
    Keywords: Corona Virus Disease; COVID-19; Diffusion Models on Networks; Spatial SEIRD Models.
    Date: 2020–10–22
  3. By: Oliver Rehbein (University of Bonn, Institute for Finance & Statistics); Simon Rother (University of Bonn, Institute for Finance & Statistics)
    Abstract: This paper analyzes social connectedness as an information channel in bank lending. We move beyond the inefficient lending between peers in exclusive networks by exploiting Facebook data that reflect social ties within the U.S. population. After accounting for physical and cultural distances, social connectedness increases cross-county lending, especially when lending requires more information and screening incentives are intact. On average, a standard-deviation increase in social connectedness increases cross-county lending by 24.5%, which offsets the lending barrier posed by 600 miles between borrower and lender. While the ex-ante risk of a loan is unrelated to social connectedness, borrowers from well-connected counties cause smaller losses if they default. Borrowers' counties tend to profit from their social proximity to bank lending, as GDP growth and employment increase with social proximity. Our results reveal the important role of social connectedness in bank lending, partly explain the large effects of physical distance, and suggest implications for antitrust policies.
    Keywords: bank lending, social networks, information frictions, distance, culture
    JEL: D82 D83 G21 O16 L14 Z13
    Date: 2020–10
  4. By: Tenev, Anastas P. (General Economics 0 (Onderwijs), RS: GSBE Theme Conflict & Cooperation)
    Abstract: This paper studies a model of network formation in which agents create links following a simple heuristic -- they invest their limited resources proportionally more in neighbours who have fewer links. This decision rule captures the notion that when considering social value more connected agents are on average less beneficial as neighbours and node degree is a useful proxy when payoffs are difficult to compute. The decision rule illustrates an externalities effect whereby an agent's actions also influence his neighbours' neighbours. Besides complete networks and fragmented networks with complete components, the pairwise stable networks produced by this model include many non-standard ones with characteristics observed in real life networks like clustering and irregular components. Multiple stable states can develop from the same initial structure -- the stable networks could have cliques linked by intermediary agents while sometimes they have a core-periphery structure. The observed pairwise stable networks have close to optimal welfare. This limited loss of welfare is due to the fact that when a link is established, this is beneficial to the linking agents, but makes them less attractive as neighbours for others, thereby partially internalising the externalities the new connection has generated.
    JEL: A13 C72 D85
    Date: 2020–10–12
  5. By: Ruben Hipp
    Abstract: We investigate the causal structure of financial systems by accounting for contemporaneous relationships. To identify structural parameters, we introduce a novel non-parametric approach that exploits the fact that most financial data empirically exhibit heteroskedasticity. The identification works locally and, thus, allows structural matrices to vary smoothly with time. With this causality in hand, we derive a new measure for systemic relevance. An application on volatility spillovers in the US financial market demonstrates the importance of structural parameters in spillover analyses. Finally, we highlight that the COVID-19 period is mostly an aggregate crisis, with financial firms’ spillovers edging slightly higher.
    Keywords: Econometric and statistical methods; Financial markets; Financial stability
    JEL: C32 C58 L14
    Date: 2020–10
  6. By: M.Z. Yaqub (King Abdulaziz University); Marijana Sreckovic (TU Wien - Technische Universität Wien); Gérard Cliquet (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR1 - Université de Rennes 1 - UNIV-RENNES - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); G. Hendrikse (Erasmus University Rotterdam); J. Windsperger (Universität Wien)
    Abstract: In today's dynamic, complex and interconnected environments, interfirm networks in its various forms (e.g. franchising, retail and service chains, cooperatives, financial networks, joint ventures, strategic alliances, clusters, public-private partnerships, digital platforms) are becoming increasingly important in helping firms improve their competitive position through an enhanced access to innovation, complementary resources and capabilities otherwise not available to them. Driven by increased performance pressures in unpredictable environments, firms embedded in networks are increasingly moving from cooperators to collaborators as value co-creators. The aim of this introductory article is to discuss the role of innovation in business networks by focusing on two major topics: Network innovation versus innovation through networks. In addition, we provide an overview of the articles included in the special issue on Networks and Innovation focusing on the questions: (1) what is the impact of network characteristics on a firm's innovation?; and (2) what are the determinants of innovation in interfirm networks? © 2020 Elsevier Inc.
    Keywords: Innovation through networks,Network innovation,Networked firms,Theoretical perspectives on networks and innovation,Value co-creation,innovation through networks
    Date: 2020
  7. By: Ruochen Dai (Central University of Finance and Economics); Dilip Mookherjee (Boston University); Yingyue Quan (Peking University); Xiaobo Zhang (Peking University and IFPRI)
    Abstract: We examine how exposure of Chinese firms to the Covid-19 shock varied with a cluster index (measuring spatial agglomeration of firms in related industries) at the county level. Two data sources are used: entry flows of newly registered firms in the entire country, and an entrepreneur survey regarding operation of existing firms. Both show greater resilience in counties with a higher cluster index, after controlling for industry dummies and local infection rates, besides county and time dummies in the entry data. Reliance of clusters on informal entrepreneur hometown networks and closer proximity to suppliers and customers help explain these findings.
    Keywords: Clusters, Covid-19, China, Firms, Social Networks
    JEL: J12 J16 D31 I3
    Date: 2020–10
  8. By: Muduli, Silu; Dash, Shridhar Kumar
    Abstract: A borrower may hesitate to borrow from her close relatives and family members as it costs them in terms of reduction in social insurance in the case of default. This invisible cost reduces credit risk. India’s household indebtedness survey shows some evidence on these borrowing preferences. This perspective on borrowing decisions derived from the community can be used as one of the dimensions in credit risk evaluation and in policy formulation.
    Keywords: Network, Trust, Credit Risk
    JEL: C92 D82 G21
    Date: 2019–12–07

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