Knowledge Spillovers, Externalities and Regional Economic Growth in the EU : Theories and Empirical Evidences

Coesione e competitività sono i principali obiettivi della politica regionale Europea. È però possibile che investimenti in competitività possano beneficiare maggiormente le regioni più sviluppate, a discapito della coesione. Si tratta di una contraddizione? In questo lavoro si risponde a questa domanda approfondendo tre argomenti. Il primo riguarda le determinanti della crescita regionale. Il secondo interessa il ruolo degli spillover di conoscenza per l’innovazione regionale. Il terzo è relativo al contributo delle infrastrutture di conoscenza all’attività innovativa regionale. I principali risultati possono essere sintetizzati in questa maniera. Una più alta crescita nella regioni meno sviluppate è importante ma non sufficiente a garantire convergenza. La crescita, nel lungo periodo, è determinata dagli investimenti in conoscenza, che producono rendimenti crescenti. La mancanza di sviluppo nelle regioni più arretrate può essere attribuita a questi divari di conoscenza, non sempre facili da colmare. Le esternalità alla base dei rendimenti crescenti sono estremamente localizzate e non si diffondono facilmente nelle economie. D’altra parte la conoscenza non si produce solamente con investimenti in ricerca. La promozione della ristrutturazione economica regionale verso modelli basati sulla conoscenza dovrebbe dedicare particolare attenzione alle fonti esterne di conoscenza, quali, accanto alle università, i servizi ad alto contenuto di conoscenza.Cohesion and competitiveness are the two main objectives of the EU regional policy. It seems however that improving competitiveness will benefit developed regions more, implying a less cohesive Europe. Is that a contradiction? This work answers this question by studying three related topics. The first concerns the determinants of regional growth in Europe. The second is about the importance of interregional knowledge spillovers for the regional innovative activity. The third is related to the way knowledge infrastructures can shape regional innovative activity. The main results can be summarized as follows. The higher growth in least developed region is important but not sufficient to catch-up. Growth, in the long-run, is determined by investments in knowledge, which produce increasing returns. The lack of development of lagging regions could be accordingly ascribed to the existence of knowledge gaps which are not as easy to be filled. Knowledge externalities, the essence of the more than proportional returns, are extremely localized and do not necessarily spread across the economies. However knowledge is not only produced through R&D. The promotion of economic restructuring of lagging regions toward a knowledge-based economy should deserve special attention to external knowledge sources like, alongside universities, Knowledge Intensive Business Services

On specifying heterogeneity in knowledge production functions

Within the Geography of Innovation literature, the Knowledge Production Function approach has become a reference framework to investigate the presence of localized knowledge spillovers and spatial econometric tools have been applied to study interregional spillovers. A linear specification for the KPF is assumed linking patents to R&D expenditure. This approach however suffers of different drawbacks. First patent applications are count data in nature. Patents per inhabitants may produce an unrealistic picture of the spatial distribution of innovative activities. Secondly, spatial heterogeneity is not usually observed, producing both omitted variables bias and spatial correlation in the error structure. Third, a positive R&D-patents linkage may arise as a spurious correlation if market size is not observed, causing R&D to be endogenous. This paper uses a regional cross section model to study the spatial distribution of high tech patents across 232 European regions in the period 2005/2006 to address these issues. Two main processes drive technological change in the model: research activities and knowledge generated outside firms and in a second moment embedded through either formal or informal acquisition. Among the different knowledge sources we particularly focus on the role of firms working in Knowledge Intensive Business Services and on that of universities. In developing the empirical model we take into account that a) patents are count data; b) the exclusion of market size will cause biased and inconsistent model parameters estimates; c) estimates of interregional spillovers may be biased by the omission of heterogeneity in the model specification. Empirical results indicate that, as expected, a count data distribution best fits the data, producing less spatially autocorrelated residuals. Regional innovative activity is explained by both investments in research and localization of KIBS, but only the first generates positive interregional externalities. Scientific universities do not directly affect the production of new knowledge. However, different knowledge production processes characterize regions with and without scientific universities, with R&D driving innovation in the sooner and KIBS in the latter. Finally, most of what are assumed to be interregional spillovers reveal to be, at a more careful inquiry, effect due to unaccounted spatial heterogeneity in regional innovation.

A Playful Experiential Learning System With Educational Robotics

This article reports on two studies that aimed to evaluate the effective impact of educational robotics in learning concepts related to Physics and Geography. The reported studies involved two courses from an upper secondary school and two courses froma lower secondary school. Upper secondary school classes studied topics ofmotion physics, and lower secondary school classes explored issues related to geography. In each grade, there was an “experimental group” that carried out their study using robotics and cooperative learning and a “control group” that studied the same concepts without robots. Students in both classes were subjected to tests before and after the robotics laboratory, to check their knowledge in the topics covered. Our initial hypothesis was that classes involving educational robotics and cooperative learning are more effective in improving learning and stimulating the interest and motivation of students. As expected, the results showed that students in the experimental groups had a far better understanding of concepts and higher participation to the activities than students in the control groups

Cloning, expression, and localization of a rat brain high-affinity glycine transporter

A cDNA clone encoding a glycine transporter has been isolated from rat brain by a combined PCR and plaque-hybridization strategy. mRNA synthesized from this clone (designated GLYT1) directs the expression of sodium-and chloride-dependent, high-affinity uptake of [3H]glycine by Xenopus oocytes. [3H]Glycine transport mediated by clone GLYT1 is blocked by sarcosine but is not blocked by methylaminoisobutyric acid or L-alanine, a substrate specificity similar to that described for a previously identified glycine-uptake system called system Gly. In situ hybridization reveals that GLYT1 is prominently expressed in the cervical spinal cord and brainstem, two regions of the central nervous system where glycine is a putative neurotransmitter. GLYT1 is also strongly expressed in the cerebellum and olfactory bulb and is expressed at lower levels in other brain regions. The open reading frame of the GLYT1 cDNA predicts a protein containing 633 amino acids with a molecular mass of ≈70 kDa. The primary structure and hydropathicity profile of GLYT1 protein reveal that this protein is a member of the sodium- and chloride-dependent superfamily of transporters that utilize neurotransmitters and related substances as substrates

Metacognizione, attenzione e intelligenza emotiva: uno studio sperimentale

Il tema dell'intelligenza emotiva è piuttosto attuale e dibattuto all'interno del panorama scientifico della psicologia moderna. In generale, c'è unanimità nel definire l'intelligenza emotiva come la capacità di riconoscere le emozioni proprie e altrui in modo da poter strutturare e regolare adeguate relazioni sociali

Knowledge Creation vs Knowledge Co-Production: Knowledge Intensive Business Servises and Innovative Activity in EU Regions

Regional economies are continuously evolving shifting from more traditional manufacturing toward more service-oriented production systems. Despite the increasing relevance of services, however, the analysis of innovation at the regional aggregate level has mainly focused on manufacturing, gathering the attention on the role of R&D expenditure as input in the production process and, in some cases, accounting for research-based knowledge externalities. In this paper the role of Knowledge Intensive Business Services is studied and their contribution to the regional aggregate innovation is evaluated. The aim is twofold. First is to provide insights on the role covered by KIBS as a second knowledge infrastructure. Second is to examine the extent to which KIBS operate as bridges between the general purpose analytical knowledge produced by scientific universities and more specific requirement of innovative firms. A role commonly acknowledged to KIBS is in fact that of knowledge transferors. If on the one side it is however clear to whom they transfer knowledge, their client firms, on the other it is not as clear from whom the knowledge is originally transferred. For this reason a major attention in this work is dedicated to scientific universities considered as a primary source of knowledge. Being this knowledge analytical and highly codified, it probably can be more easily accessed by nearby located firms having higher opportunities of research collaboration and less easily by firms located in different regions. It is argued that KIBS, in transferring knowledge from universities to firms, are therefore specially important in the latter case. To test hese hypothesis a knowledge production function is estimated for a sample of 200 EU NUTS II regions including also information of university research and KIBS concentration. Parameters are estimated using the heteroschedasticity-consistent G2SLS estimator for spatial models and the evidence suggests that the contribution of KIBS to regional innovation is considerable. In fact accounting for the knowledge embedded in business services can considerably contribute to explain the cross-regional variation in innovative activities. Furthermore it is find that the KIBS contribution is more sizeable in regions in which there are not scientific universities. The highlighted results have important policy implications asking to rethink to how much effective an R&D-centered innovation strategy could be, at least in some regions

Increasing Returns, Decreasing Returns and Regional Economic Convergence in the EU

Regional economic development is driven by the accumulation of production factors. More traditional factors like labour and physical capital are accumulated under the law of diminishing returns. This, in turn, allows less developed regions to better perform. Recent branches of theoretical and empirical literature have paid attention to the role of increasing returns in an attempt to explain the persistence in regional economic disparities. Increasing returns are commonly attributed either to the accumulation of non- traditional inputs such as human and knowledge capital or to the presence of local externalities generated by the spatial concentration of economic activities. The aim of the present paper is to address the importance of both decreasing and increasing returns for the economic growth of European regions. While economies will definitively converge in the long-run if production is characterized by decreasing returns, divergence is the likely outcome in presence of increasing returns. As regional growth is probably the result of a combination of both, from a policy perspective it is important to understand where the sooner stops and the latter starts. In this paper the economic performance of 186 European regions is analysed by using the ordinary growth regression approach. An empirical specification which simultaneously accounts for the presence of both decreasing and increasing returns is derived. The study is intended to examine the extent to which regional development originates from the (un)balance between convergence, driven by diminishing returns and divergence, boosted by increasing returns. Results indicate that the accumulation of traditional inputs leads the economic development of less favoured areas while the presence of increasing returns plays a more crucial role in developed regions. More in the detail increasing returns seem to be generated by the accumulation of knowledge and human capital while there is no clear evidence of agglomeration externalities. By using a non-linear specification for the growth equation it is also found evidence of important threshold effects in entering the stage of development characterized by increasing returns. Regional development process is accordingly depicted as a far more complex process than what the simple dualism between increasing and decreasing returns may help to figure out, with very important implications for policy

On specifying heterogeneity in knowledge production functions

Within the Geography of Innovation literature, the Knowledge Production Function approach has become a reference framework to investigate the presence of localized knowledge spillovers and spatial econometric tools have been applied to study interregional spillovers. A linear specification for the KPF is assumed linking patents to R&D expenditure. This approach however suffers of different drawbacks. First patent applications are count data in nature. Patents per inhabitants may produce an unrealistic picture of the spatial distribution of innovative activities. Secondly, spatial heterogeneity is not usually observed, producing both omitted variables bias and spatial correlation in the error structure. Third, a positive R&D-patents linkage may arise as a spurious correlation if market size is not observed, causing R&D to be endogenous. This paper uses a regional cross section model to study the spatial distribution of high tech patents across 232 European regions in the period 2005/2006 to address these issues. Two main processes drive technological change in the model: research activities and knowledge generated outside firms and in a second moment embedded through either formal or informal acquisition. Among the different knowledge sources we particularly focus on the role of firms working in Knowledge Intensive Business Services and on that of universities. In developing the empirical model we take into account that a) patents are count data; b) the exclusion of market size will cause biased and inconsistent model parameters estimates; c) estimates of interregional spillovers may be biased by the omission of heterogeneity in the model specification. Empirical results indicate that, as expected, a count data distribution best fits the data, producing less spatially autocorrelated residuals. Regional innovative activity is explained by both investments in research and localization of KIBS, but only the first generates positive interregional externalities. Scientific universities do not directly affect the production of new knowledge. However, different knowledge production processes characterize regions with and without scientific universities, with R&D driving innovation in the sooner and KIBS in the latter. Finally, most of what are assumed to be interregional spillovers reveal to be, at a more careful inquiry, effect due to unaccounted spatial heterogeneity in regional innovation

Investment behaviour of EU arable crop farms in selected EU countries and the impact of policy reforms. Factor Markets Working Document No. 42, May 2013

This deliverable provides a comparative analysis, among selected EU member states, of the investment demand of a sample of specialised field crop farms for farm buildings, machinery and equipment as determined by different types and levels of Common Agricultural Policy support. It allows for the existence of uncertainty in the price of output farmers receive and for both long- and short-run determinants of investment levels, as well as for the presence of irregularities in the cost adjustment function due to the existence of threshold-type behaviours. The empirical estimation reveals that three investment regimes are consistently identified in Germany and Hungary, across asset and support types, and in France for machinery and equipment. More traditional disinvestment-investment type behaviours characterise investment in farm building in France and the UK, across support types, and Italy for both asset classes under coupled payments. The long-run dynamic adjustment of capital stocks is consistently and significantly estimated to be towards a – mostly non-stationary – lower level of capitalisation of the farm analysed. By contrast, the expected largely positive short-run effects of an increase in output prices are often not significant. The effect of CAP support on both types of investment is positive, although seldom significant, while the proxy for uncertainty employed fails to be significant yet, in most cases, has the expected effect of reducing the investment levels