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Università degli Studi di Cagliari e Sassari Innovation clusters in European regions Rosina Moreno-Serrano University of Barcelona Raffaele Paci University.

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Presentation on theme: "Università degli Studi di Cagliari e Sassari Innovation clusters in European regions Rosina Moreno-Serrano University of Barcelona Raffaele Paci University."— Presentation transcript:

1 Università degli Studi di Cagliari e Sassari Innovation clusters in European regions Rosina Moreno-Serrano University of Barcelona Raffaele Paci University of Cagliari and CRENoS Stefano Usai University of Cagliari and CRENoS Milano, Università Cattolica 27 Aprile 2005

2 Università degli Studi di Cagliari e Sassari Research line Technological activity is the main engine of growth. We want to contribute in investigating on how this engine works at the regional level Investigate on the role of knowledge creation and diffusion by exploring the evolution of technological activity across regions and sectors in Europe. Concentrate on industrial heterogeneity in order to examine the formation and evolution of specialised innovative clusters at the regional level

3 Università degli Studi di Cagliari e Sassari Aims of research line Estimate a Knowledge Production Function (KPF) at the regional level both for total knowledge and sector innovative activity. At the sectoral level it is possible to assess the presence of local externalities within the sector and across sectors, that is specialisation and diversity externalities respectively Analyse the importance of geographical proximity and technological similarity in the creation and diffusion of knowledge in manufacturing industries in the European regions.

4 Università degli Studi di Cagliari e Sassari The literature behind us/1 From a theoretical point of view: knowledge and technological progress are engines of economic dynamics in most endogenous growth models (since Romer, 1986). In the spatial context this implies that local growth depends on the amount of technological activity which is carried out locally (depending on several factors among which internal technological spillovers) and on the ability to exploit technological achievements from outside, that is external technological spillovers (Martin and Ottaviano, 2001; Coe and Helpman, 1995). Importance of geographical (Glaeser et al, 1992; Henderson, 1997, Paci and Usai, 2000) and technological (Keller, 2000, Verspagen, 2000) proximity for sharing innovations and knowledge and other local advantages

5 Università degli Studi di Cagliari e Sassari The literature behind us/2 From an empirical point of view: a useful starting point is the KNOWLEDGE PRODUCTION FUNCTION (KPF) –originally formalised by Griliches, 1979, and mainly applied at the firm level and refocused by Jaffe, 1989, to study knowledge spillovers from university to firms at the local level Empirical estimations of general KPF have been carried out for different levels of aggregation: –For the US case (Acs et al, 1994; Audretsch and Feldman, 1996; Anselin et al, 1997, Feldman and Audretsch, 1999) –For the EU case: Maurseth and Verspagen, 1999; Bottazzi and Peri, 2003, Moreno, Paci and Usai, 2005).

6 Università degli Studi di Cagliari e Sassari The literature behind us/3 Empirical estimations of KPF at the local industry level have been just a few

7 Università degli Studi di Cagliari e Sassari What’s really new… We focus on European regions (as in Greunz, 2003) but with a larger sample of countries and a methodology based on a different set of indicators and measures. As in Massard and Riou (2002) we measure specialisation and diversity externalities based on innovation itself instead of using production indexes. Moreover the use of specific econometric techniques should allow to analyse the nature other than the spatial scope of the diffusion of technological spillovers (as in Paci and Usai, 1999). We perform both panel analysis and a set of cross sections at the industry level (as in Massard and Riou, 2002). Contrary to previous papers we replicate the analysis for two periods in order to check the robustness of some results along the time dimension.

8 Università degli Studi di Cagliari e Sassari CRENoS database Original and updated statistical databank on regional patenting at the European Patent Office countries in Europe (the 15 members of the EU -10 new members excluded- plus Switzerland and Norway) 175 regions 23 (2digit ISIC) manufacturing sectors

9 Università degli Studi di Cagliari e Sassari Measurement issues Two types of indicators: –technology input measures (such as R&D expenditure and employees) –technology output measures (such as patents and new product announcements).

10 Università degli Studi di Cagliari e Sassari Some critical features of the database Patents are a technology output measure Patent applications (not granted patents) at EPO provide a measure which is of a sufficiently homogenous quality: potentially highly remunerative innovations Indicator for both product and process innovations Long time span: three-year averages to smooth data Data on inventor and legal applicant: use of the inventor’s residence instead of applicant’s residence. Specific treatment of multiple inventors Use of Yale Technology Concordance. –Such a concordance uses the probability distribution of each IPC across industries of manufacture in order to attribute each patent proportionally to the different sectors where the innovation may have originated (or used) Very wide sectoral disaggregation –(paradoxically there are more problems with data on economic activity at least at the European level)

11 European Regions CRENoS database (ID-CRENoS; ID-NUTS; Region; Nuts level)

12 Università degli Studi di Cagliari e Sassari Geography of innovative activity/1

13 Università degli Studi di Cagliari e Sassari Geography of innovative activity/2

14 Università degli Studi di Cagliari e Sassari Spatial distribution of innovation Early eighties: strong centre-periphery distribution of innovation activity map1 Late nineties: the intensity to innovate has increased considerably over the two decades in all countries the innovations have been spreading to some more regions in the South of Europe (Spain, North-Centre Italy) and Finland map3 the degree of disparities in the regional distribution of innovative activities has decreased, but not in an homogeneous way

15 Università degli Studi di Cagliari e Sassari Concentration of innovation across regions and along time

16 Università degli Studi di Cagliari e Sassari Spatial dependence of innovative activity Presence of a strong and positive spatial autocorrelation process in the innovative activity among contiguous areas

17 Università degli Studi di Cagliari e Sassari Spatial dependence of innovative activity Presence of strong and positive spatial autocorrelation also found at the sectoral level determining the formation of specialised clustering of innovative regions in different sectors Table3 The scatter map allows to distinguish the sign of such autocorrelation: mainly positive in the centre, mainly negative in the periphery… Map3

18 Università degli Studi di Cagliari e Sassari Index of technological specialisation (top sector) in the European regions (annual average, )

19 Università degli Studi di Cagliari e Sassari Composition of innovative activity (patenting) per sector

20 Università degli Studi di Cagliari e Sassari The determinants of innovative activity the main determinants of the local process of innovative activity are on a blend of: internal factors - production factors (R&D) - externalities within the region (agglomeration economies, knowledge not codified, institutions) external regional factors (spillovers through trade across sectors, common markets for skilled labour,…).

21 Università degli Studi di Cagliari e Sassari Estimation strategy Firstly, OLS to check for the presence of spatial dependence Then ML for when spatial lag model or spatial error model is used to correct for spatial dependence Check for different contiguity matrices

22 Università degli Studi di Cagliari e Sassari Basic regression I = patents per capita (annual average ) RD = share of GDP devoted to R&D ( ) DENS = density of population ( ) MAN = share of manufacturing employment ( ) NAT = national dummies Extensions: Spatial lag dependent variable Spatial lag R&D with distance decay effects Spatial spillovers within and across national borders Spatial spillovers and technological similarities

23 Università degli Studi di Cagliari e Sassari Econometric analysis: previous results for the general KPF Main results –Importance of internal R&D expenditure –Role played by other internal factors (economic performance, agglomeration economies and national differences in production structure, institutions and others) –External effects count: Both through patenting activity and the R&D efforts in other regions A decay process of knowledge diffusion Mostly constrained by national borders (national innovation systems or social/cultural proximity?) Spatial proximity effects are enhanced when regions are technologically homogeneous

24 Università degli Studi di Cagliari e Sassari The determinants of innovative activity at the local industry level I = local patents per sector (per capita) IST = technological specialisation index based on location quotient (ij) DIV= diversity index based on herfhindhal (ij) DENS = population per km2 (j) GDP = gross value added per capita (j) RD = share of GDP devoted to expenditure in research and development(j) NAT = national dummies, SCT = sector dummies (i)

25 Università degli Studi di Cagliari e Sassari Use of spatial econometric techniques W may be: - Contiguity – Technology - Technology*contiguity The error term is specified as follows: LAG MODEL: ERROR MODEL:

26 Università degli Studi di Cagliari e Sassari Panel estimation,

27 Università degli Studi di Cagliari e Sassari Some sector regressions, 94-96, contiguity matrix

28 Università degli Studi di Cagliari e Sassari Some sector regressions, 99-01, contiguity matrix

29 Università degli Studi di Cagliari e Sassari Some sector regressions, 99-01, cont*tech matrix

30 Università degli Studi di Cagliari e Sassari Robustness tests Main results are robust with respect to: –Dependent variable expressed in absolute values –Double log –Tobit estimation (but without lags)

31 Università degli Studi di Cagliari e Sassari Some preliminary comments Technological specialisation is deepening (contrary to specialisation in production: less delocalisation processes in action). Such an effect increases along time Diversity is not relevant Density is always negatively (often significantly) related to innovative activity (!?!) Other local factors are significant Spatial autocorrelation often disappears after first contiguity Spatial plus technological proximity has different effects depending on sectors

32 Università degli Studi di Cagliari e Sassari Further steps Need to understand better the relationship among sectors across regions –Better indicators for some phenomena, especially for density… –Experiment other matrices in terms of both geographical and technological distance –Test for the significance of other spatially lagged variables –Use of panel techniques to take into account simultaneously the three dimensions in hand, that is space, industrial and time.

33 Università degli Studi di Cagliari e Sassari Distribution of innovative activity in the European regions, (patents per capita, annual average)

34 Università degli Studi di Cagliari e Sassari Distribution of innovative activity in the European regions, (patents per capita, annual average)

35 Università degli Studi di Cagliari e Sassari Spatial autocorrelation, Moran index

36 Università degli Studi di Cagliari e Sassari MORAN Scatterplot for innovative activity in the European regions, (patents per capita, annual average; number of regions in parenthesis )


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