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on Knowledge Management and Knowledge Economy |
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Issue of 2010‒12‒04
four papers chosen by Laura Stefanescu European Research Centre of Managerial Studies in Business Administration |
| 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=knm |
| By: | Maurizio Iacopetta (Observatoire Français des Conjonctures Économiques) |
| Abstract: | In this paper, I examine the transitional dynamics of an economy populated by individuals who split their time between acquiring a formal education, producing final goods, and innovating. The paper has two objectives: (i) uncovering the macroeconomic circumstances that favored the rise of formal education; (ii) to reconcile the remarkable growth of the education sector with the constancy of other key macroeconomic variables, such as the interest rate, the consumption-output ratio, and the growth rate of per capita income (Kaldor facts). The transitional dynamics of human capital growth models, such as Lucas (1988), would attribute the arrival of education to the diminishing marginal productivity of physical capital. Conversely, the model proposed here suggests that it is the rate of learning that catches up with the rate of return on physical capital. As technical knowledge expands, the rate of return on education increases, and this induces individuals to stay longer in school. The model's transitional paths are matched with long run U.S. educational and economic data. |
| Keywords: | Public Knowledge, Learning Rate, Transitional Dynamics, Calibration. JEL codes: J24, N30, O33. |
| Date: | 2010–11 |
| URL: | http://d.repec.org/n?u=RePEc:fce:doctra:1033&r=knm |
| By: | Andrés Solimano; Diego Avanzini |
| Abstract: | International migration analysis often focuses on mass migration rather than on the international mobility of elites, which is the focus of this paper. The paper offers a three-fold classification of elites: (a) knowledge elites, (b) entrepreneurial elites and (c) political elites. We explore the concept of elites and their main motivation to move across nations and review indirect empirical evidence relevant to this type of mobility, highlighting some channels through which elites can affect international development. |
| Keywords: | international migration, entrepreneurial, political migrants, talent mobility |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:unu:wpaper:wp2010-113&r=knm |
| 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=knm |