Product variety, product quality, and evidence of Schumpeterian endogenous growth: a note

Using US manufacturing industry data, this paper re-examines empirical evidence of first- and second-generation Schumpeterian models of endogenous growth focusing on innovation (patent) quality. It shows that semi-endogenous growth models behave better than the other strands of Schumpeterian theory especially in the knowledge-intensive section of the economy.fully endogenous growth theory, semi-endogenous growth theory, innovation quality, US manufacturing, high-tech industries.

Looking into the black box of Schumpeterian Growth Theories: an empirical assessment of R&D races

This paper assesses whether the most important R&D technologies at the roots of second-generation Schumpeterian growth theories are consistent with patenting and innovation statistics. Using US manufacturing industry data, we estimate various systems of simultaneous equations modeling the innovation functions underlying growth frameworks based on variety expansion, diminishing technological opportunities and rent protection activities. Our evidence indicates that innovation functions characterized by the increasing difficulty of R&D activity fit US data better. This finding relaunches the debate on the soundness of the new Schumpeterian strand of endogenous growth literature.R&D, patenting, Schumpeterian growth, US manufacturing.

R&D and productivity in high-tech manufacturing: a comparison between Italy and Spain

Using data for twelve manufacturing industries over the period 1980-2006, we perform for Italy and Spain a dynamic panel estimation of the long-run elasticity of TFP with respect to R&D capital. The results show that in Spain high-tech industries have experienced a similar or slightly higher R&D elasticity than their Italian counterparts. This is mainly attributable to what occurred from the mid 1990s onwards when, thanks to increasing R&D efforts, the Spanish industries have been able to catch up with the respect to the Italian ones. The policy implications of the above findings are discussed.Manufacturing industries; Italy and Spain; Productivity growth; R&D capital

Offshoring and Specialisation: Are Industries Moving Abroad?

This paper investigates the impact of off-shoring on specialisation via its effect on national endowments and productivity. We use different definition of off-shoring to properly capture international fragmentation of production, while controlling for countries? stocks of R&D and ICT capital. Using industry data for the US, Japan and Europe we show that while offshoring of materials can benefit a wide range of industries, service and intra-industry offshoring can decrease specialisation in high-tech industry, both within manufacturing and services. This effect can be compensated with increasing R&D investments.

R&D Policy and Schumpeterian Growth: Theory and Evidence

In recent years, a large body of empirical research has investigated whether the predictions of secondgeneration growth models are consistent with actual data. This strand of literature has focused on the longrun properties of these models by using productivity and innovation data but has not directly assessed the effectiveness of R&D policy in promoting innovation and economic growth. In the present paper, we fill this gap in the literature by providing a unified growth setting that is empirically tested with US manufacturing industry data. Our analysis shows that R&D policy has a persistent, if not permanent, impact on the rate of economic growth and that the economy rapidly adjusts to policy changes. The impact of R&D tax credits on economic growth appears to be long lasting and statistically robust. Conversely, more generous R&D subsidies are associated with an increase in the rate of economic growth in the short run only, indicating that, at best, this policy instrument has only temporary effects. Overall, the evidence regarding the effectiveness of R&D policy provides more support for fully endogenous growth theory than for semi-endogenous growth theory

River Contracts in north-east Italy: Water management or participatory processes?

River Contracts (RCs) are voluntary agreements between stakeholders for managing water bodies and involve participatory, evidence-based action plans. Increasingly, European authorities recognize that effective water policies require bottom-up, inclusive decision-making. Despite widely held assumptions about the benefits of including stakeholders in river basin management and encouraging participatory mechanisms of decision-making, the growing rhetoric about the need for public engagement implies that this ‘new’ paradigm of water management remains filled with ambiguities. Adopting ethnographic methods and drawing on a variety of primary and secondary sources, this paper analyses three RCs in the Friuli Venezia Giulia region of Italy. These case studies reveal the potential for RCs as tools not only for water management, but also for increasing stakeholders’ involvement through place-making activities conceived as potential hydrophilic encounters. In order to understand whether RCs contribute to a fluvial sense of place, we looked at the effects of top-down versus participatory processes. we asked whether RCs were considered participatory processes designed to achieve a co-designed outcome or simply territorial management projects that objectify the river as something to be developed. We looked at the weights of either top-down or participatory processes and whether RCs were considered territorial management projects or participatory processes. We found that ratifying an RC was not, in itself, proof of an effective process; rather the nature and quality of an RC was determined by the degree and type of participation. We contend that participatory events and sharing information are not sufficient in themselves to achieve the active involvement of all stakeholders. We argue that the best framework for enabling place-making and enhancing a sense of place is to develop RCs within a process that includes a high degree of participation. This enables citizens to shift from simply being passive recipients of plans and becoming effective territorial actors

La valutazione delle performance sportive: il progetto FCI (Fabio Capello Index)

Introduzione – 1. La valutazione delle performance e le scelte strategiche nel calcio-Players’ Management: selezione, talenti, retribuzioni – 2. Il progetto FCI – Conclusioni e fattori di sviluppo del progettoGestione delle risorse umane, job based pay, non-traditional pay plan, resource-based view, valutazione delle perfomance

Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs

Clustering (or community detection) on multilayer graphs poses several additional complications with respect to standard graphs as different layers may be characterized by different structures and types of information. One of the major challenges is to establish the extent to which each layer contributes to the cluster assignment in order to effectively take advantage of the multilayer structure and improve upon the classification obtained using the individual layers or their union. However, making an informed a-priori assessment about the clustering information content of the layers can be very complicated. In this work, we assume a semi-supervised learning setting, where the class of a small percentage of nodes is initially provided, and we propose a parameter-free Laplacian-regularized model that learns an optimal nonlinear combination of the different layers from the available input labels. The learning algorithm is based on a Frank-Wolfe optimization scheme with inexact gradient, combined with a modified Label Propagation iteration. We provide a detailed convergence analysis of the algorithm and extensive experiments on synthetic and real-world datasets, showing that the proposed method compares favourably with a variety of baselines and outperforms each individual layer when used in isolation

Louvain-like Methods for Community Detection in Multi-Layer Networks

In many complex systems, entities interact with each other through complicated patterns that embed different relationships, thus generating networks with multiple levels and/or multiple types of edges. When trying to improve our understanding of those complex networks, it is of paramount importance to explicitly take the multiple layers of connectivity into account in the analysis. In this paper, we focus on detecting community structures in multi-layer networks, i.e., detecting groups of well-connected nodes shared among the layers, a very popular task that poses a lot of interesting questions and challenges. Most of the available algorithms in this context either reduce multi-layer networks to a single-layer network or try to extend algorithms for single-layer networks by using consensus clustering. Those approaches have anyway been criticized lately. They indeed ignore the connections among the different layers, hence giving low accuracy. To overcome these issues, we propose new community detection methods based on tailored Louvain-like strategies that simultaneously handle the multiple layers. We consider the informative case, where all layers show a community structure, and the noisy case, where some layers only add noise to the system. We report experiments on both artificial and real-world networks showing the effectiveness of the proposed strategies.Comment: 16 pages, 4 figure