Michele Sabatino (p), Giuseppina Talamo

The recent debate on European development policies is articulated around two major filelds of researches that are highly integrated with each other. One of this is investments in research, innovation and the innovative capacity of the European regions. Another is the degree of competitiveness of production and European regional systems. This paper enters the debate on development and regional competitiveness related to innovation and research, by presenting recent data on investments in research and development in the different European regions. Additionally, we also present the degree of innovation of European regions refering one of the main document “The Regional Innovation Scoreboard 2014”. This document provides a comparative assesment of the performance in terms of innovation in 190 EU regions of the EU, making use of a limited number of indicators of research and innovation. It also shows what the differences in the level of innovation performance among EU Member States are still considerable and are reduced only slowly. In particular, at regional level, the innovation gap is widening, and in almost one fifth of the regions of the EU's innovation performance is worse. Related to this document, we present recent data on the degree of competitiveness of the regions by specifying the selected indicators of competitiveness within the EU with a base map existing (The Europe 2020 Competitiveness Report 2014). The aim of this paper is to enter in the debate on investment in research and degree of competitiveness comparing first the above mentioned dataset and mapping the most innovative regions and one of the most competitive regions. In the light of this comparison the differences and similarities will be highlighted, as well as the correlation between the index of innovation and to regional competitiveness. At the end are presented some policy indications on possible courses of action for innovation and competitiveness of European regions.

Anja Kukuvec (p)

Until recently, the geographical coverage of data sets on the sub-national level was usually rather small, biased towards industrialized economies and hardly included developing countries. However, recent empirical literature and regional data collection have paved the way for new regional growth analysis. Employing such a data set, the present paper investigates the role of human capital and technology spillovers on regional total factor productivity growth between the years 1980 and 2005 for 568 regions in 30 countries (including 15 non-OECD countries). Non-linearities in the effects of the explanatory variables as well as spatial spillovers caused by a spatial autoregressive process in the dependent and independent variables are considered in the estimation model.

The general dynamic of total factor productivity (TFP) is defined according to the model put forward
by Benhabib & Spiegel (1994), where the growth rate of technology is a function of human capital and its interaction with technological backwardness. By imposing a spatial econometric structure on the Benhabib & Spiegel model, the specification allows for technology spillovers via three channels. First, technological distance to the technology leader can influence the intensity of the spillovers. Second, technology spillovers may depend on the stock of human capital, which determines the speed of technology adoption. Third, technology spillovers can be affected by geographical distance, supposing that regions have greater access to technology resources of neighbors than of nonneighbors.

The findings confirm significant positive direct impacts of technological catch-up and human capital
on regional TFP growth. Notably, the coefficient estimate for human capital alone is not very significant, but considering the additional effect of its interaction with the technology gap to the technology leader, the average direct impact is highly significant. Moreover, a negative average indirect impact of the technology gap is observed, which is interpreted as positive spatial spillovers of technology levels. In contrast, spatial autocorrelation of technology growth is only significant when not controlling for country-specific effects. The results are robust to including population density, a dummy for whether the capital city is located in the region, geographic characteristics, per capita oil and gas production and a dummy for OECD membership as further explanatory variables. Additional robustness checks are computed for more variations in the explanatory variables, for variations in the spatial diffusion patterns, as well as for using GDP per capita growth as an alternative estimate of TFP growth. Different time periods of TFP growth are also considered.

Ferhan Gezici (p), Sinem Metin Kacar

There have been different approaches to explain the regional development, however all of them have tried to determine the factors, which affected the local system. As it is known that the space has a significant role among the factors of development. The existing resources have long been identified as initial advantages of the regions, while recent literature and policies emphasize how important to have a capacity for utilizing the local resources and to be able to connect to the world, attract new activities and people. Furthermore increasing importance of knowledge in the world economy brought forward the concept of knowledge community and knowledge cities.
Especially for Turkey and the other developing countries while the national economy has been growing, regional development has been one of the hot topics considering the regional disparities. Therefore, several approaches and methodologies were used to measure the regional development, whereas socio-economic development index, human development index, competitiveness index are the well-known ones. This paper displays a new and innovative insight with its contribution to the regional policy in Turkey from the knowledge-based development perspective.

Since Turkey’s nation wide goals require knowledge based development to support increasing competitiveness in knowledge economy, it is important to determine which provinces have the potential for knowledge- based development to channel resources efficiently. Evaluated existing models show that it is crucial to develop a framework locally and determine the sub-categories for multiple aspects of Knowledge Based Urban Development. Therefore; KBUD potential index of Turkish provinces was developed locally in four categories of economic development, societal development, spatial development and institutional development. The results put forward that different potentials of provinces in Turkey do not only contribute to the overall picture of KBUD development potential in Turkey; but sub-categories also make it easy to determine the strengths and weaknesses of each province and provide guidance for urban and regional policies. Furthermore, the results underline that mainly economic development is not related to knowledge infrastructure in Turkey. Furthermore, the knowledge based development potential is higher especially in the neighboring provinces of metropolitan cities, while we could catch potentials of different provinces considering sub-categories.

Patricia Mello (p)

The contribution of this paper consists of a model to assess how Science and Technology Parks (STPs) are impacting cities development. Brazilian policy makers claim them to be a strategic policy for development of cities. If so its formulation and implementation would be accordingly to the result development expected in those cities they are located and policy evaluation would capture previously planned benefits. However it seems to be not clear what kind of development STP is supposed to reach or how this policy can be adjusted institutionally if its needed. Maybe this reflects the lack of studies considering STP a policy. Still even if one presumes it according to the multicentric view of policies (SECCHI, 2010), those who examine policy cycles would neither cogitate STP as a possible policy to be studied.

Thus, the ambition of this paper is to address these gaps. Based on about 200 previous case studies developed by other authors, the following research question is answered: “how to observe STP’s development effect on city’s where they are located according to studies previously done?” Hypothetically by constructing a model to observe STP effect on cities indicators.

To do so by applying Systematic Literature Review (LEVY E ELLIS, 2006) combined with Coding Process (MILES AND HUBERMAN, 2014) with the support of Atlas Ti software and analyzing data from Development Theories in perspective, the following steps was taken:

(i) Based on various development and public policy studies we learnt what kind of development authors think STPs are supposed to promote in cities and what indicators could measure them;
(ii) Ascertain what incentives generated by the STP could connect with those types of development expected;

The result was:

1) Seven types of development STP can cause in cities;
2) Seven classes of indicators for each development;
3) Seven groups of STP internal incentives connected to those developments.
4) Possible data sources in Brazil.
5) Seven possible development theories to explain how such incentives and indicators can justify that type of development.

Therefore the objective of this study lies on finding out what should STP promote in cities and how to observe it. In general it represents an attempt to shed light on what should be expected by and required to STPs in its daily administration, more than to construct a perfect impact model risking to loose all immeasurable externalities produced by STPs.