nep-net New Economics Papers
on Network Economics
Issue of 2016‒03‒23
eight papers chosen by
Yi-Nung Yang
Chung Yuan Christian University

  1. The Economic Consequences of Social Network Structure By Jackson, Matthew O.; Rogers, Brian; Zenou, Yves
  2. Contagion and Stability in Financial Networks By Seyyed Mostafa Mousavi; Robert Mackay; Alistair Tucker
  3. Networks and the macroeconomy: an empirical exploration By Acemoglu, Daron; Akcigit, Ufuk; Kerr, William R.
  4. The old boy network: The impact of professional networks on remuneration in top executive jobs By Lalanne, Marie; Seabright, Paul
  5. Networks in risk spillovers: a multivariate GARCH perspective By Monica Billio; Massimiliano Caporin; Lorenzo Frattarolo; Loriana Pelizzon
  6. Introduction to network modeling using Exponential Random Graph models (ERGM) By Johannes Van Der Pol
  7. Risk and Return Spillovers among the G10 Currencies By Matthew Greenwood-Nimmo; Viet Hoang Nguyen; Barry Rafferty
  8. Partners in Crime: Schools, Neighborhoods and the Formation of Criminal Networks By Stephen B. Billings; David J. Deming; Stephen L. Ross

  1. By: Jackson, Matthew O. (Stanford University); Rogers, Brian (Washington University); Zenou, Yves (Monash University,, Department of Economics,)
    Abstract: We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of `macro' and `micro' characteristics of social interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors.
    Keywords: Social networks; Social economics; Homophily; Diffusion; Social learning contagion; Centrality measures; Endogeneity; Network formation
    JEL: C72 D85 L14 Z13
    Date: 2016–03–07
  2. By: Seyyed Mostafa Mousavi; Robert Mackay; Alistair Tucker
    Abstract: This paper investigates two mechanisms of financial contagion that are, firstly, the correlated exposure of banks to the same source of risk, and secondly the direct exposure of banks in the interbank market. It will consider a random network of banks which are connected through the inter-bank market and will discuss the desirable level of banks exposure to the same sources of risk, that is investment in similar portfolios, for different levels of network connectivity when peering through the lens of the systemic cost incurred to the economy from the banks simultaneous failure. It demonstrates that for all levels of network connectivity, certain levels of diversifying individual banks diversifications are not optimum under any condition. So, given an acceptable level of systemic cost, the regulator could let banks decrease their capital buffers by moving away from the non-optimum area.
    Date: 2016–03
  3. By: Acemoglu, Daron; Akcigit, Ufuk; Kerr, William R.
    Abstract: The propagation of macroeconomic shocks through input-output and geographic networks can be a powerful driver of macroeconomic fluctuations. We first exposit that in the presence of Cobb-Douglas production functions and consumer preferences, there is a specific pattern of economic transmission whereby demand-side shocks propagate upstream (to input-supplying industries) and supply-side shocks propagate downstream (to customer industries) and that there is a tight relationship between the direct impact of a shock and the magnitudes of the downstream and the upstream indirect effects. We then investigate the short-run propagation of four different types of industry-level shocks: two demand-side ones (the exogenous component of the variation in industry imports from China and changes in federal spending) and two supply-side ones (TFP shocks and variation in knowledge/ideas coming from foreign patenting). In each case, we find substantial propagation of these shocks through the input-output network, with a pattern broadly consistent with theory. Quantitatively, the network-based propagation is larger than the direct effects of the shocks. We also show quantitatively large effects from the geographic network, capturing the fact that the local propagation of a shock to an industry will fall more heavily on other industries that tend to collocate with it across local markets. Our results suggest that the transmission of various di¤erent types of shocks through economic networks and industry interlinkages could have first-order implications for the macroeconomy.
    Keywords: economic fluctuations, geographic collocation, input-output linkages, networks, propagation, shocks
    JEL: E32
    Date: 2015–12–09
  4. By: Lalanne, Marie; Seabright, Paul
    Abstract: We investigate the impact of social networks on earnings using a dataset of over 20,000 senior executives of European and US firms. The size of an individual's network of influential former colleagues has a large positive association with current remuneration. An individual at the 75th percentile in the distribution of connections could expect to have a salary nearly 20 per cent higher than an otherwise identical individual at the median. We use a placebo technique to show that our estimates reflect the causal impact of connections and not merely unobserved individual characteristics. Networks are more weakly associated with women's remuneration than with men's. This mainly reflects an interaction between unobserved individual characteristics and firm recruitment policies. The kinds of firm that best identify and advance talented women are less likely to give them access to influential networks than are firms that do the same for the most talented men.
    Keywords: professional networks,gender wage gap,executive compensation,placebo technique
    JEL: A14 J16 J31 J33
    Date: 2016
  5. By: Monica Billio (Department of Economics, University Of Venice Cà Foscari); Massimiliano Caporin (Department of Economics, University Of Padova); Lorenzo Frattarolo (SAFE-Goethe University Frankfurt); Loriana Pelizzon (SAFE-Goethe University Frankfurt)
    Abstract: We propose a spatial approach for modeling risk spillovers using financial time-varying proximity matrices based on observable networks. We show how these methods could be useful in (i) isolating risk channels, risk spreaders and risk receivers, (ii) investigating the role of portfolio composition in risk transfer, and (iii) computing target exposure structures able to reduce the forecasted system variance and thus the risk of the system. Our empirical analysis builds on banks’ foreign exposures provided by the Bank of International Settlements (BIS) as a proxy for Euro area cross-country holdings. We find, in the European sovereign bond markets, that Germany, Italy and, to a lesser extent, Greece are playing a central role in spreading risk, and Ireland and Spain are the most susceptible receivers of spillover effects that can be traced back to a physical claim channel: banks’ foreign exposures. We additionally show that acting on these physical channels before the sovereign crisis, it would have been possible to have a clear risk mitigation outcome
    Keywords: spatial GARCH, network, risk spillover, financial spillover
    JEL: C58 G10
    Date: 2016
  6. By: Johannes Van Der Pol (GRETha / UMR 5113 - Groupe de Recherche en Economie Théorique et Appliquée (GREThA) (CNRS /Université de Bordeaux))
    Abstract: Exponential Family Random Graph Models (ERGM) are increasingly used in the study of social networks. These models are build to explain the global structure of a network while allowing inference on tie prediction on a micro level. The number of paper within economics is however limited. Applications for economics are however abundant. The aim of this document is to provide an explanation of the basic mechanics behind the models and provide a sample code (using R and the packages statnet and ergm) to operationalize and interpret results and analyze goodness of fit. After reading this paper the reader should be able to launch their own analysis.
    Keywords: ERGM, Social and economic networks, Exponential Random Graph Model, P-star, Innovation networks
    Date: 2016–03–08
  7. By: Matthew Greenwood-Nimmo (Department of Economics, The University of Melbourne); Viet Hoang Nguyen (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Barry Rafferty (Department of Economics, The University of Melbourne)
    Abstract: We study spillovers among daily returns and innovations in option-implied risk-neutral volatility and skewness of the G10 currencies. An empirical network model uncovers substantial time variation in the interaction of risk measures and returns, both within and between currencies. We find that aggregate spillover intensity is countercyclical with respect to the federal funds rate and increases in periods of financial stress. During these times, volatility spillovers and especially skewness spillovers between currencies increase, reflecting greater systematic risk. Likewise, linkages between returns and risk measures strengthen in times of stress, with returns becoming more sensitive to risk measures and vice versa. Classification-C58, F31, G01, G15
    Keywords: Foreign exchange markets, risk-neutral volatility, risk-neutral skewness, spillovers, coordinated crash risk
    Date: 2016–02
  8. By: Stephen B. Billings (University of North Carolina-Charlotte); David J. Deming (Harvard University); Stephen L. Ross (University of Connecticut)
    Abstract: Why do crime rates differ greatly across neighborhoods and schools? Comparing youth who were assigned to opposite sides of newly drawn school boundaries, we show that concentrating disadvantaged youth together in the same schools and neighborhoods increases total crime. We then show that these youth are more likely to be arrested for committing crimes together – to be “partners in crime”. Our results suggest that direct peer interaction is a key mechanism for social multipliers in criminal behavior. As a result, policies that increase residential and school segregation will – all else equal – increase crime through the formation of denser criminal networks.
    Keywords: Youth Crime, Schools, Criminal Partnerships, Neighborhood Effects, Social Interactions
    JEL: I2 J1 K4 R2
    Date: 2016–03

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