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on Network Economics |
| By: | Co-Pierre Georg (School of Economics and Business Administration, Friedrich-Schiller-University Jena) |
| Abstract: | In the wake of the financial crisis it has become clear that there is a need for macroprudential oversight in addition to the existing microprudential banking supervision. One of the lessons from the crisis is that the network structure of the banking system has to be taken into account to assess systemic risk. There exists, however, no analysis on the influence of the network topology on contagion in financial networks. This paper therefore compares contagion in Barabási-Albert (scale-free) with Watts-Strogatz (small-world) and random networks. A network model of banks, a firm- and household-sector as well as a central bank is used. Banks optimize a portfolio of risky investments and risk-free excess reserves according to their risk and liquidity preferences. They form a network via interbank loans and face a stochastic supply of household deposits. Contagion effects from the default of a large bank are studied in different network topologies. The results indicate that contagion is more severe in random and scale-free networks than in small-world networks. This situation changes when the central bank is not active in which case small-world networks are less stable than scale-free and random networks. It is also shown that interbank liquidity above a certain threshold leads to endogenous instability, regardless of the network topology. The results further indicate that network heterogeneity does not contribute to financial instability. |
| Keywords: | systemic risk, contagion, interbank markets, network models |
| JEL: | C63 E52 E58 G21 |
| Date: | 2010–10–05 |
| URL: | http://d.repec.org/n?u=RePEc:hlj:hljwrp:12-2010&r=net |
| By: | Jackie Krafft (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS : UMR6227 - Université de Nice Sophia-Antipolis); Francesco Quatraro (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS : UMR6227 - Université de Nice Sophia-Antipolis, Department of Economics, University of Turin - University of Turin); Pier-Paolo Saviotti (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS : UMR6227 - Université de Nice Sophia-Antipolis, GAEL - Grenoble Applied Economic laboratory - Aucune) |
| Abstract: | This paper applies the methodological tools typical of social network analysis (SNA) within an evolutionary framework, to investigate the knowledge base dynamics of the biotechnology sector. Knowledge is here considered a collective good represented as a co-relational and a retrieval-interpretative structure. The internal structure of knowledge is described as a network the nodes of which are small units within traces of knowledge, such as patent documents, connected by links determined by their joint utilisation. We used measures referring to the network, like density, and to its nodes, like degree, closeness and betweenness centrality, to provide a synthetic description of the structure of the knowledge base and of its evolution over time. Eventually, we compared such measures with more established properties of the knowledge base calculated on the basis of co-occurrences of technological classes within patent documents. Empirical results show the existence of interesting and meaningful relationships across the different measures, providing support for the use of SNA to study the evolution of the knowledge bases of industrial sectors and their lifecycles. |
| Keywords: | Knowledge Base, Social Network Analysis, Variety, Coherence, Industry lifecycles; exploration/exploitation |
| Date: | 2010–11–23 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00539002_v1&r=net |
| By: | Michel Grabisch (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I); Agnieszka Rusinowska (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I) |
| Abstract: | In the paper, a yes-no model of influence is generalized to a multi-choice framework. We introduce and study weighted influence indices of a coalition on a player in a social network, where players have an ordered set of possible actions. Each player has an inclination to choose one of the actions. Due to mutual influence among players, the final decision of each player may be different from his original inclination. In a particular case, the decision of the player is closer to the inclination of the influencing coalition than his inclination was, i.e., the distance between the inclinations of the player and of the coalition is greater than the distance between the decision of the player and the inclination of the coalition in question. The weighted influence index which captures such a case is called the weighted positive influence index. We also consider the weighted negative influence index, where the final decision of the player goes farther away from the inclination of the coalition. We consider several influence functions defined in the generalized model of influence and study their properties. The concept of a follower of a given coalition, and its particular case, a perfect follower, are defined. The properties of the set of followers are analyzed. |
| Keywords: | weighted positive influence index, weighted negative influence index, influence function, follower of a coalition, perfect follower, kernel |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00539009_v1&r=net |
| By: | Muhamed Kudic; P. Bönisch; Iciar Dominguez Lacasa |
| Abstract: | Empirical and theoretical contributions provide strong evidence that firm-level performance outcomes in terms of innovativeness can either be determined by the firm’s position in the social space (network effects) or by the firm’s position in the geographical space (co-location effects). Even though we can observe quite recently first attempts in bringing together these traditionally distinct research streams (Whittington et al. 2009), research on interdependent network and geographical co-location effects is still rare. Consequently, we seek to answer the following research question: considering that the effects of social and geographic proximity on firm’s innovativeness can be interdependent, what are the distinct and combined effects of firm’s network and geographic position on firm-level innovation output? We analyze the innovative performance of German laser source manufacturers between 1995 and 2007. We use an official database on publicly funded R&D collaboration projects in order to construct yearly networks and analyze firm’s network positions. Based on information on population entries and exits we calculate various types of geographical proximity measures between private sector and public research organizations (PRO). We use patent grants as dependent variable in order to measure firm-level innovation output. Empirical results provide evidence for distinct effect of network degree centrality. Distinct effect of firm’s geographical co-location to laser-related public research organization promotes patenting activity. Results on combined network and co-location effects confirms partially the existence of in-terdependent proximity effects, even though a closer look at these effects reveals some ambiguous but quite interesting findings. |
| Keywords: | geographical co-location, network positioning, innovation output |
| JEL: | O31 O32 L25 |
| Date: | 2010–10 |
| URL: | http://d.repec.org/n?u=RePEc:iwh:dispap:22-10&r=net |