Given the importance of tourism as a catalyst for local economic growth, the identification of tourist\ud flows is also important. This paper reports results from an econometric study of tourism flows for the 107\ud Italian provinces based on originedestination (OD) spatial interaction models. In addition to distance, the\ud set of explanatory variables includes both pull and push characteristics to assess their relative roles in\ud determining the attractiveness of the provinces to tourists. Hence measures are incorporated for income,\ud density, accessibility, and natural, cultural and recreational attractions. The main results indicate the\ud importance of spatial dependency induced by neighbouring provinces as both origins of, and destinations\ud for trips, a factor commonly overlooked by previous contributions relying on the gravity specification.\ud Inter-neighbouring interactive effects are thus important, and most explanatory variables exhibit\ud the expected effect, with distance and population density showing a negative impact on tourists’ decisions\ud when choosing a specific destination, while income, accessibility and attractions are crucial\ud determinants of tourism flows.\ud \ud JEL code: C21, D12, L83, Q26, R1

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Raffaele Paci University of Cagliari and CRENoS Stefano Usai University of Sassari and CRENoS WP CRENoS 2003/10 December 2003 AbstractThis paper explores the spatial distribution of innovative activity and the role of technological spillovers in the process of knowledge creation across 138 regions of 17 countries in Europe (the 15 members of the European Union plus Switzerland and Norway). The analysis is based on an original statistical databank set up by CRENoS on regional patenting at the European Patent Office spanning from 1978 to 1997 and classified by ISIC sectors (3 digit). In a first step, a deep exploratory spatial data analysis of the dissemination of innovative activity in Europe is performed. Some global and local indicators for spatial association are presented, summarising the presence of a dependence process in the distribution of innovative activity for different periods and sectors. Secondly, we attempt to model the behaviour of innovative activity at the regional level on the basis of a knowledge production function. Econometric results points to the relevance of internal factors (R&D expenditure, economic performance, agglomeration economies). Moreover, the production of knowledge by European regions seems to be also affected by spatial spillovers due to innovative activity performed in other regions.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Raffaele Paci University of Cagliari and CRENoS Stefano Usai University of Sassari and CRENoS WP CRENoS 2003/10 December 2003 AbstractThis paper explores the spatial distribution of innovative activity and the role of technological spillovers in the process of knowledge creation across 138 regions of 17 countries in Europe (the 15 members of the European Union plus Switzerland and Norway). The analysis is based on an original statistical databank set up by CRENoS on regional patenting at the European Patent Office spanning from 1978 to 1997 and classified by ISIC sectors (3 digit). In a first step, a deep exploratory spatial data analysis of the dissemination of innovative activity in Europe is performed. Some global and local indicators for spatial association are presented, summarising the presence of a dependence process in the distribution of innovative activity for different periods and sectors. Secondly, we attempt to model the behaviour of innovative activity at the regional level on the basis of a knowledge production function. Econometric results points to the relevance of internal factors (R&D expenditure, economic performance, agglomeration economies). Moreover, the production of knowledge by European regions seems to be also affected by spatial spillovers due to innovative activity performed in other regions.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. There is a large consensus among social researchers on the positive role played by human capital on economic performances. The standard way to measure the human capital endowment is to consider the educational attainments by the resident population, usually the share of people with a university degree. Recently, Florida (2002) suggested a different measure of human capital -the "creative class" -based on the actual occupations of individuals in specific jobs like science, engineering, arts, culture, entertainment. However, the empirical analyses carried out so far overlooked a serious measurement problem concerning the clear definition of the education and creativity components of human capital. This paper aims to disentangle this issue by proposing a disaggregation of human capital into three nonoverlapping categories of creative graduates, bohemians and non creative graduates. Using a spatial error model to account for spatial dependence, we assess the concurrent effect of the human capital indicators on total factor productivity for 257 regions of EU27. Our results indicate that the highly educated creative group is the most relevant one in explaining production efficiency, non creative graduates exhibit a lower impact, while the bohemians do not show a significant impact on regional performance. Moreover, a relevant influence is exerted by technological capital, cultural diversity and industrial and geographical characteristics thus providing robust evidence that a highly educated, innovative, open and culturally diverse environment is becoming more and more central for productivity enhancements. Terms of use: Documents in

In the last decade there has been an upsurge of studies on international comparisons of Total Factor Productivity (TFP). The empirical evidence suggests that countries and regions differ not only in traditional factor endowments (labour and physical capital) but mainly in productivity and technology. Therefore, a crucial issue is the analysis of the determinants of such differences in the efficiency levels across economies. In this paper we try to assess these issues by pursuing a twofold aim. First, we derive a regression based measure of regional TFP which have the nice advantage of not imposing a priori restrictions on the inputs elasticities; this is done by estimating a Cobb-Douglas production function relationship for 199 European regions over the period , which includes the traditional inputs as well as a measure of spatial interdependences across regions. Secondly, we investigate the determinants of the TFP levels by analyzing the role played by intangible factors: human capital, social capital and technological capital. It turns out that a large part of TFP differences across the European regions are explained by the disparities in the endowments of such assets. This outcome indicates the importance of policy strategies which aim at increasing the level of knowledge and social capital as stressed by the Lisbon agenda. Estimation is carried out by applying the spatial 2SLS method and the SHAC estimator to account for both heteroskedasticity and spatial autocorrelation.

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