nep-net New Economics Papers
on Network Economics
Issue of 2016‒07‒30
five papers chosen by
Yi-Nung Yang
Chung Yuan Christian University

  1. Innovation, Pricing and Targeting in Networks By Panebianco, Fabrizio; Verdier, Thierry; Zenou, Yves
  2. A Note on Shapley Ratings in Brain Networks By Musegaas, Marieke; Dietzenbacher, Bas; Borm, Peter
  3. Liquidity Risk, Bank Networks, and the Value of Joining the Federal Reserve System By Calomiris, Charles W.; Jaremski, Matthew; Park, Haelim; Richardson, Gary
  4. Acquiring information through peers By Bernard Herskovic; Joao Ramos
  5. The co-evolution of innovation networks: Collaboration between West and East Germany from 1972 to 2014 By Jun, Bogang; Yi, Seung-Kyu; Buchmann, Tobias; Mueller, Matthias

  1. By: Panebianco, Fabrizio; Verdier, Thierry; Zenou, Yves
    Abstract: Consider a network of firms where a firm T is given the opportunity to innovate a product (first-generation innovation). If successful, this firm can temporarily sell this innovation to her direct neighbors because this will give her access to a larger market. However, if her direct neighbors innovate themselves on top of firm T's innovation (second-generation innovations), then firm T loses the right to sell her initial innovation to the remaining firms in the market. We analyze this game where each firm (T and her direct neighbors) has to decide at which price they want to sell their innovation. We show that the optimal price policy of each firm depends on the level of property rights protection, the position of firm T in the network, her degree and the size of the market. We then analyze the welfare implications of our model where the planner that maximizes total welfare has to decide which firm to target. We show that it depends on the level of property rights protection and on the network structure in a non-trivial way.
    Keywords: diffusion centrality; innovation.; Networks; targets
    JEL: D85 L1 Z13
    Date: 2016–07
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:11398&r=net
  2. By: Musegaas, Marieke (Tilburg University, Center For Economic Research); Dietzenbacher, Bas (Tilburg University, Center For Economic Research); Borm, Peter (Tilburg University, Center For Economic Research)
    Abstract: We consider the problem of computing the in uence of a neuronal structure in a brain network. Abraham, Kotter, Krumnack, and Wanke (2006) computed this influence by using the Shapley value of a coalitional game corresponding to a directed network as a rating. Kotter, Reid, Krumnack, Wanke, and Sporns (2007) applied this rating to large-scale brain networks, in particular to the macaque visual cortex and the macaque prefrontal cortex. We introduce an alternative coalitional game that is more intuitive from a game theoretical point of view. We use the Shapley value of this game as an alternative rating to analyze the macaque brain networks and corroborate the findings of Kotter et al. (2007). Moreover, we show how missing information on the existence of certain connections can readily be incorporated into this game and the corresponding Shapley rating.
    Keywords: brain networks; coalitional games; Shapley value
    JEL: C71
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:tiu:tiucen:3562c06a-1612-4d93-a299-4b939ea95c91&r=net
  3. By: Calomiris, Charles W. (Columbia University); Jaremski, Matthew (Colgate University); Park, Haelim (Office of Financial Research, Department of Treasury); Richardson, Gary (Federal Reserve Bank of Richmond)
    Abstract: Reducing systemic liquidity risk related to seasonal swings in loan demand was one reason for the founding of the Federal Reserve System. Existing evidence on the post-Federal Reserve increase in the seasonal volatility of aggregate lending and the decrease in seasonal interest rate swings suggests that it succeeded in that mission. Nevertheless, less than 8 percent of state-chartered banks joined the Federal Reserve in its first decade. Some have speculated that nonmembers could avoid higher costs of the Federal Reserve’s reserve requirements while still obtaining access indirectly to the Federal Reserve discount window through contacts with Federal Reserve members. We find that individual bank attributes related to the extent of banks’ ability to mitigate seasonal loan demand variation predict banks’ decisions to join the Federal Reserve. Consistent with the notion that banks could obtain indirect access to the discount window through interbank transfers, we find that a bank’s position within the interbank network (as a user or provider of liquidity) predicts the timing of its entry into the Federal Reserve System and the effect of Federal Reserve membership on its lending behavior. We also find that indirect access to the Federal Reserve was not as good as direct access. Federal Reserve member banks saw a greater increase in lending than nonmember banks.
    JEL: G21 G28 N22
    Date: 2016–07–06
    URL: http://d.repec.org/n?u=RePEc:fip:fedrwp:16-06&r=net
  4. By: Bernard Herskovic (UCLA Anderson School of Management); Joao Ramos (NYU)
    Abstract: We study information acquisition from peers when agents’ actions balance adaptation and coordination motives. Agents acquire information personally and may obtain additional information by connecting to other agents. Although equally informative regarding adaptation, the source’s relative position in the information structure is relevant to form expectations about actions of other players. In our setting, information sources are not perfectly substitutable, and the information of an “opinion maker†—an agent whose information is more public—is more informative of how others act. We show that, when players choose their connections, (i) it is always preferable to connect to opinion makers, and (ii) opinion makers have less incentives to form links. These two results characterize the endogenous shape of the network: Any strict equilibrium of the network formation game generates a hierarchical information structure. Furthermore, if the marginal cost of acquiring information is increasing, the information structure is “core-periphery†. We take advantage of the simplicity of the equilibrium information structure to provide two applications. First, we analyze how much of the aggregate volatility of forecast can the information structure account for. Second, we study the origins of leadership: how individual characteristics influence the role of the agent in the information structure.
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:red:sed016:248&r=net
  5. By: Jun, Bogang; Yi, Seung-Kyu; Buchmann, Tobias; Mueller, Matthias
    Abstract: This paper describes the co-evolution of East and West German innovation networks after the German reunification in 1990 by analyzing publication data from 1972 to 2014. This study uses the following four benchmark models to interpret and classify German innovation networks: the random graph model, the small-world model, the Barabási-Albert model, and the evolutionary model. By comparing the network characteristics of empirical networks with the characteristics of these four benchmark models, we can increase our understanding of the particularities of German innovation networks, such as development over time as well as structural changes (i.e., new nodes or increasing/decreasing network density). We first confirm that a structural change in East-West networks occurred in the early 2000s in terms of the number of link between the two. Second, we show that regions with few collaborators dominated the properties of German innovation networks. Lastly, the change in network cliquishness, which reflects the tendency to build cohesive subgroups, and path length, which is a strong indicator of the speed of knowledge transfer in a network, compared with the four benchmark models show that East and West German regions tended to connect to new regions located in their surroundings, instead of entering distant regions. Our findings support the German federal government's continuous efforts to build networks between East and West German regions.
    Keywords: Innovation networks,Network dynamics,German reunification
    Date: 2016
    URL: http://d.repec.org/n?u=RePEc:zbw:hohdps:092016&r=net

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