nep-net New Economics Papers
on Network Economics
Issue of 2020‒07‒27
fourteen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Dynamic Networks in Large Financial and Economic Systems By Jozef Barunik; Michael Ellington
  2. Mixed Logit Models and Network Formation By Harsh Gupta; Mason A. Porter
  3. Networks in risk spillovers: A multivariate GARCH perspective By Monica Billio; Massimiliano Caporin; Lorenzo Frattarolo; Loriana Pelizzon
  4. Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers By Kenan Huremovic; Jiménez Gabriel; Enrique Moral-Benito; José-Luis Peydró; Fernando Vega-Redondo
  5. Information and the Acquisition of Social Network Connections By Toman Barsbai; Victoria Licuanan; Andreas Steinmayr; Erwin Tiongson; Dean Yang
  6. Robust communication on networks By Marie Laclau; Ludovic Renou; Xavier Venel
  7. Public Health Interventions in the Face of Pandemics: Network Structure, Social Distancing, and Heterogeneity By Mohammad Ghaderi
  8. Insider Networks By Selman Erol; Michael Junho Lee
  9. Socioeconomic Network Heterogeneity and Pandemic Policy Response By Mohammad Akbarpour; Cody Cook; Aude Marzuoli; Simon Mongey; Abhishek Nagaraj; Matteo Saccarola; Pietro Tebaldi; Shoshana Vasserman; Hanbin Yang
  10. Optimal Risk-Sharing Across a Network of Insurance Companies By Nicolas Ettlin; Walter Farkas; Andreas Kull; Alexander Smirnow
  11. Econometric Models of Network Formation By De Paula, Áureo
  12. The Determinants and Effects of Social Connectedness in Europe By Michael Bailey; Theresa Kuchler; Dominic Russel; Bogdan State; Johannes Stroebel
  13. Polarization in networks By Kenan Huremovic; Ali Ozkes
  14. Peers and Motivation at Work: Evidence from a Firm Experiment in Malawi By Brune, Lasse; Chyn, Eric T.; Kerwin, Jason Theodore

  1. By: Jozef Barunik; Michael Ellington
    Abstract: We propose new measures to characterize dynamic network connections in large financial and economic systems. In doing so, our measures allow one to describe and understand causal network structures that evolve throughout time and over horizons using variance decomposition matrices from time-varying parameter VAR (TVP VAR) models. These methods allow researchers and practitioners to examine network connections over any horizon of interest whilst also being applicable to a wide range of economic and financial data. Our empirical application redefines the meaning of big in big data, in the context of TVP VAR models, and track dynamic connections among illiquidity ratios of all S\&P500 constituents. We then study the information content of these measures for the market return and real economy.
    Date: 2020–07
  2. By: Harsh Gupta; Mason A. Porter
    Abstract: The study of network formation is pervasive in economics, sociology, and many other fields. In this paper, we model network formation as a ``choice'' that is made by nodes in a network to connect to other nodes. We study these ``choices'' using discrete-choice models, in which an agent chooses between two or more discrete alternatives. One framework for studying network formation is the multinomial logit (MNL) model. We highlight limitations of the MNL model on networks that are constructed from empirical data. We employ the ``repeated choice'' (RC) model to study network formation \cite{TrainRevelt97mixedlogit}. We argue that the RC model overcomes important limitations of the MNL model and is well-suited to study network formation. We also illustrate how to use the RC model to accurately study network formation using both synthetic and real-world networks. Using synthetic networks, we also compare the performance of the MNL model and the RC model; we find that the RC model estimates the data-generation process of our synthetic networks more accurately than the MNL model. We provide examples of qualitatively interesting questions -- the presence of homophily in a teen friendship network and the fact that new patents are more likely to cite older, more cited, and similar patents -- for which the RC model allows us to achieve insights.
    Date: 2020–06
  3. By: Monica Billio (Department of Economics, University Of Venice Cà Foscari); Massimiliano Caporin (Department of Statistical Sciences, University Of Padua); Lorenzo Frattarolo (Department of Economics, University Of Venice Cà Foscari); Loriana Pelizzon (SAFE-Goethe University Frankfurt (Germany); Department of Economics, University Of Venice Cà Foscari)
    Abstract: We propose a spatiotemporal approach for modeling risk spillovers using time-varying proximity matrices based on observable financial networks and introduce a new bilateral Multivariate GARCH speci_cation. We study covariance stationarity and identification of the model, develop the quasi-maximum-likelihood estimator and analyze its consistency and asymptotic normality. We show how to isolate risk channels and we discuss how to compute target exposure able to reduce system variance. An empirical analysis on Euroarea bond data shows that Italy and Ireland are key players in spreading risk, France and Portugal are major risk receivers, and we uncover Spain's non-trivial role as risk middleman.
    Keywords: Spatial GARCH, network, risk spillover, financial spillover
    JEL: C58 G10
    Date: 2020
  4. By: Kenan Huremovic; Jiménez Gabriel; Enrique Moral-Benito; José-Luis Peydró; Fernando Vega-Redondo
    Abstract: We show that bank shocks originating in the financial sector propagate upstream and downstream along the production network and triple the impact of direct bank shocks. Our identification relies on the universe of both supplier-customer transactions and bank loans in Spain, a standard operationalization of credit-supply shocks during the 2008-09 global crisis, and the proposed theoretical framework. The impact on real effects is strong, and similarly so, when considering: (i) direct bank shocks to firms versus first-order interfirm contagion; (ii) first-order versus higher-order network effects; (iii) downstream versus upstream propagation; (iv) firm-specific versus economy-wide shocks. Market concentration amplifies these effects.
    Keywords: networks; supply chains; shock propagation; credit supply; real effects of finance
    JEL: D85 E44 E51 G01 G21
    Date: 2020–07
  5. By: Toman Barsbai; Victoria Licuanan; Andreas Steinmayr; Erwin Tiongson; Dean Yang
    Abstract: How do information interventions affect individual efforts to expand social networks? We study a randomized controlled trial of a program providing information on settling in the U.S. for new immigrants from the Philippines. Improved information leads new immigrants to acquire fewer new social network connections. Treated immigrants make 16-28 percent fewer new friends and acquaintances and are 65 percent less likely to receive support from organizations of fellow immigrants. The treatment has no effect on employment, wellbeing, or other outcomes. Consistent with a simple model, the treatment reduces social network links more in places likely to have lower costs of acquiring network links (those with more prior fellow immigrants). Information and social network links appear to be substitutes in this context: better-informed immigrants invest less in expanding their social networks upon arrival. Our results suggest that endogenous reductions in acquisition of social network connections can reduce the effectiveness of information interventions.
    JEL: D83 D85 F22
    Date: 2020–06
  6. By: Marie Laclau; Ludovic Renou; Xavier Venel
    Abstract: We consider sender-receiver games, where the sender and the receiver are two distinct nodes in a communication network. Communication between the sender and the receiver is thus indirect. We ask when it is possible to robustly implement the equilibrium outcomes of the direct communication game as equilibrium outcomes of indirect communication games on the network. Robust implementation requires that: (i) the implementation is independent of the preferences of the intermediaries and (ii) the implementation is guaranteed at all histories consistent with unilateral deviations by the intermediaries. Robust implementation of direct communication is possible if and only if either the sender and receiver are directly connected or there exist two disjoint paths between the sender and the receiver.
    Date: 2020–07
  7. By: Mohammad Ghaderi
    Abstract: Complexity, resulting from interactions among many components, is a characterizing property of healthcare systems and related decisions. Such complexity scales up quickly in the face of pandemics, where multiple sources of uncertainty are involved and various contextual factors interacting with policy parameters yield outcome distribution. This paper presents a uni ed framework to assist and inform policy decisions in confronting pandemics. The general framework consists of a model of contagion that makes the policy- relevant variables explicit and exogenous, establishes links between them and the main features of the environment in which the policy is going to be implemented, and treats various sources of uncertainty at different layers of the system. At the macro level, special attention is devoted to the network structure, for which we provide a simple characterization based on two constructive factors. Our results show that by conditioning on these two factors, a large proportion of the stochasticity resulted from the inherent randomness in the network can be captured. Components of the model are synthesized in a broader agent-based model that enables accounting for heterogeneous individual-level attributes that collectively yield the macro-level outcomes. Using several stylized examples and a comprehensive controlled experiment, insights on the overall tendency of the complex system in terms of multidimensional outputs are derived across a range of scenarios and under various types of policy conditions.
    Keywords: public health interventions, social contagion, random networks, social distancing, simulation
    JEL: C6 C54 C32 I1
    Date: 2020–07
  8. By: Selman Erol; Michael Junho Lee
    Abstract: Modern-day financial systems are highly complex, with billions of exchanges in information, assets, and funds between individuals and institutions. Though daunting to operationalize, regulating these transmissions may be desirable in some instances. For example, securities regulators aim to protect investors by tracking and punishing insider trading. Recent evidence shows that insiders have formed sophisticated networksthat enable them to pursue activities outside the purview of regulatory oversight. In understanding the cat-and-mouse game between regulators and insiders, a key consideration is the networks that insiders might form in order to circumvent regulation, and how regulators might cope with insiders’ tactics. In this post, we introduce a theoretical framework that considers network formation in response to regulation and review the key insights.
    Keywords: insider trading; money laundering; capital control; transmission networks; regulation
    JEL: G2 G3 G1
    Date: 2020–06–25
  9. By: Mohammad Akbarpour; Cody Cook; Aude Marzuoli; Simon Mongey; Abhishek Nagaraj; Matteo Saccarola; Pietro Tebaldi; Shoshana Vasserman; Hanbin Yang
    Abstract: We develop a heterogeneous-agents network-based model to analyze alternative policies during a pandemic outbreak, accounting for health and economic trade-offs within the same empirical framework. We leverage a variety of data sources, including data on individuals' mobility and encounters across metropolitan areas, health records, and measures of the possibility to be productively working from home. This combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions. We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas. Our results highlight how outcomes vary across areas in relation to the underlying heterogeneity in population density, social network structures, population health, and employment characteristics. We find that policies by which individuals who can work from home continue to do so, or in which schools and firms alternate schedules across different groups of students and employees, can be effective in limiting the health and healthcare costs of the pandemic outbreak while also reducing employment losses.
    JEL: H12 H75 I18
    Date: 2020–06
  10. By: Nicolas Ettlin (University of Basel, Actuarial Science, Department of Mathematics and Computer Science); Walter Farkas (University of Zurich - Department of Banking and Finance; Swiss Finance Institute; ETH Zürich); Andreas Kull (University of Basel, Actuarial Science, Department of Mathematics and Computer Science; BerninaRe Ltd.); Alexander Smirnow (University of Zurich - Department of Banking and Finance)
    Abstract: Risk transfer is a key risk and capital management tool for insurance companies. Transferring risk between insurers is used to mitigate risk and manage capital re- quirements. We investigate risk transfer in the context of a network environment of insurers and consider capital costs and capital constraints at the level of individual insurance companies. We demonstrate that the optimisation of profitability across the network can be achieved through risk transfer. Considering only individual in- surance companies, there is no unique optimal solution and, a priori, it is not clear which solutions are fair. However, from a network perspective, we derive a unique fair solution in the sense of cooperative game theory. Implications for systemic risk are briefly discussed.
    Keywords: risk transfer, risk-based capital, reinsurance, return optimisation, conditional expected shortfall
    JEL: G13 G22 D85 C57 C71
    Date: 2020–06
  11. By: De Paula, Áureo
    Abstract: This article provides a selective review on the recent literature on econometric models of network formation. The survey starts with a brief exposition on basic concepts and tools for the statistical description of networks. I then offer a review of dyadic models, focussing on statistical models on pairs of nodes and describe several developments of interest to the econometrics literature. The article also presents a discussion of non- dyadic models where link formation might be influenced by the presence or absence of additional links, which themselves are subject to similar influences. This is related to the statistical literature on conditionally specified models and the econometrics of game theoretical models. I close with a (non-exhaustive) discussion of potential areas for further development.
    Keywords: dyadic models; Network econometrics; strategic network formation models
    Date: 2020–01
  12. By: Michael Bailey; Theresa Kuchler; Dominic Russel; Bogdan State; Johannes Stroebel
    Abstract: We use aggregated data from Facebook to study the structure of social networks across European regions. Social connectedness declines strongly in geographic distance and at country borders. Historical borders and unions — such as the Austro-Hungarian Empire, Czechoslovakia, and East/West Germany — shape present-day social connectedness over and above today’s political boundaries. All else equal, social connectedness is stronger between regions with residents of similar ages and education levels, as well as between those that share a language and religion. In contrast, region-pairs with dissimilar incomes tend to be more connected, likely due to increased migration from poorer to richer regions. We find more socially connected region-pairs to have more passenger train trips between them, even after controlling for distance and travel time. We also find that regions with a higher share of connections to other countries have higher rates of trust in the E.U. and lower rates of voting for anti-E.U. political parties.
    Keywords: social connectedness, Europe, homophily, border effects, migration
    JEL: D72 J61 O52 R23 Z13
    Date: 2020
  13. By: Kenan Huremovic; Ali Ozkes
    Abstract: We introduce a model of polarization in networks as a unifying framework for the measurement of polarization that covers a wide range of applications. We consider a sufficiently general setup for this purpose: node- and edge-weighted, undirected, and connected networks. We generalize the axiomatic characterization of Esteban and Ray (1994) and show that only a particular instance within this class can be used justifiably to measure polarization of networks.
    Date: 2020–07
  14. By: Brune, Lasse; Chyn, Eric T.; Kerwin, Jason Theodore
    Abstract: This paper studies workplace peer effects by randomly varying work assignments at a tea estate in Malawi. We find that increasing mean peer ability by 10 percent raises productivity by 0.3 percent. This effect is driven by the responses of women. Neither production nor compensation externalities cause the effect because workers receive piece rates and do not work in teams. Additional analyses provide no support for learning or socialization as mechanisms. Instead, peer effects appear to operate through “motivation”: given the choice to be reassigned, most workers prefer working near high-ability co-workers because these peers motivate them to work harder.
    Date: 2020–06–10

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