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on Network Economics |
| By: | Laura Rinaldi (Second University of Naples) |
| Keywords: | Network Externalities, Mobile Telecommunication, Critical Mass |
| Date: | 2008–01 |
| URL: | http://d.repec.org/n?u=RePEc:prt:wpaper:1_2008&r=net |
| By: | Lidia CERIANI; Massimo FLORIO |
| Abstract: | Starting from an industry where production is provided by a public monopolist, we look at the effects on the consumers' surplus of a sequence of reforms in network industry. Using a simple comparative statics framework, we find indifference conditions in consumers' surplus between respectively public monopoly, unregulated private monopoly, regulated private monopoly, vertically disintegrated monopoly, duopoly and liberalized market. The results are determined by the relative size of the x-inefficiencies of the public monopolist, allocative inefficiencies of private monopoly, the cost of unbundling and costs related to establishing a competitive market. |
| Keywords: | Privatization, unbundling, liberalization, network industries |
| JEL: | D40 L51 L32 L33 |
| Date: | 2008–07–08 |
| URL: | http://d.repec.org/n?u=RePEc:mil:wpdepa:2008-23&r=net |
| By: | Lidia CERIANI; Raffaele DORONZO; Massimo FLORIO |
| Abstract: | In this paper we examine the emergence over the last two decades in the EU of a dominant policy paradigm on the reform of network industries. We consider the broad recommendations by the OECD and the European Commission, and the Directives adopted by the European Union on the reform of some public services, such as electricity, gas, and telecom. These recommendations, in their strongest form, advocate the divestiture of public ownership (openly by the OECD, but not by the EC), unbundling (by both organizations, but with differences across sectors), liberalization (again by both organization, but with variations in the role of market regulation). We contrast the predictions and prescriptions of the paradigm, with a theoretical discussion of the welfare impact of the reforms. This discussion, based on a review of some standard microeconomic assumptions on the role of ownership, economies of scale and scope, governance, and market forms, shows that the dominant policy paradigm oversimplifies a very complex story. We suggest that the actual success of the reform is conditional to a large number of economic and institutional factors, and that it is far from obvious that the adoption of the same policy pattern in any and all the EU countries is always welfare improving. Empirical analysis does not support the paradigm. |
| Keywords: | Privatization, unbundling, liberalization, network industries |
| JEL: | L10 L22 L32 L51 |
| Date: | 2009–03–23 |
| URL: | http://d.repec.org/n?u=RePEc:mil:wpdepa:2009-09&r=net |
| By: | Ron Boschma; Koen Frenken |
| Abstract: | We propose an evolutionary perspective on the geography of network formation that is grounded in a dynamic proximity framework. In doing so, we root the proximity concept in an evolutionary approach to the geography of innovation networks. We discuss three topics. The first topic focuses on explaining the structure of networks. The second topic concentrates on explaining the effects of networks on the performance of actors. The third topic deals with the changing role of proximity dimensions in the formation and performance of innovation networks in the longer run. |
| Keywords: | evolutionary economic geography, knowledge networks, innovation networks, dynamic proximity |
| JEL: | R0 R1 R12 |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:0905&r=net |
| By: | Silvia SALINI; Ron S. KENETT |
| Abstract: | A Bayesian Network is a probabilistic graphical model that represents a set of variables and their probabilistic dependencies. Formally, Bayesian Networks are directed acyclic graphs whose nodes represent variables, and whose arcs encode the conditional dependencies between the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable or a hypothesis. They are not restricted to representing random variables, which forms the "Bayesian" aspect of a Bayesian network. Efficient algorithms exist that perform inference and learning in Bayesian Networks. Bayesian Networks that model sequences of variables are called Dynamic Bayesian Networks. Harel et. al (2007) provide a comparison between Markov Chains and Bayesian Networks in the analysis of web usability from e-commerce data. A comparison of regression models, SEMs, and Bayesian networks is presented Anderson et. al (2004). In this paper we apply Bayesian Networks to the analysis of Customer Satisfaction Surveys and demonstrate the potential of the approach. Bayesian Networks offer advantages in implementing models of cause and effect over other statistical techniques designed primarily for testing hypotheses. Other advantages include the ability to conduct probabilistic inference for prediction and diagnostic purposes with an output that can be intuitively understood by managers |
| Keywords: | Bayesian Networks, Customer Satisfaction, Eurobarometer, Service Quality |
| JEL: | C11 C42 |
| Date: | 2007–10–08 |
| URL: | http://d.repec.org/n?u=RePEc:mil:wpdepa:2007-33&r=net |
| By: | William A. Brock; Steven N. Durlauf |
| Abstract: | This paper considers the observational implications of social influences on adoption decisions for an environment of perfect foresight adopters. We argue that social influences can produce two observable effects: 1) discontinuities in unconditional adoption curves and 2) pattern reversals in conditional adoption curves, in which earlier adoption is found for one group of actors versus another when "fundamentals" suggest the reverse ordering should occur; in turn the presence of either of these features can, under weak assumptions, be interpreted as evidence of social influences. As such, these properties are robust implications of social effects. |
| JEL: | C1 D01 O33 |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:15065&r=net |