Università degli Studi di Cagliari e Sassari Innovation clusters in European regions Rosina...

Università degli Studi di Cagliari e Sassari

Innovation clusters in European regions

Rosina Moreno-SerranoUniversity of Barcelona

Raffaele Paci

University of Cagliari and CRENoS

Stefano UsaiUniversity of Cagliari and CRENoS

Milano, Università Cattolica

27 Aprile 2005


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


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.


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



• 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).



Feldman and Audretsch, 1999

Paci and Usai, 2000

Massard and Riou, 2002 Greunz, 2004

USA Italy France Europe

Dependent variable

no. of innovation in sector j and city i

no. of EPO patents per capita in sector j and local labour system i

no. of EPO patents in industry j and department i

no. of EPO patents in sector j and region i

specialisation negative positive negative positive

diversity - positive negative positive

data panel panel panel+sector panel

Empirical estimations of KPF at the local industry level have been just a few


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.


CRENoS database

• Original and updated statistical databank on regional patenting at the European Patent Office

• 1978-2001

• 17 countries in Europe (the 15 members of the EU -10 new members excluded- plus Switzerland and Norway)

• 175 regions

• 23 (2digit ISIC) manufacturing sectors


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)

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

1 AT11 Burgenland 2 44 DEA2 Koeln 2 88 FR26 Bourgogne 2 131 IT71 Abruzzo 2

2 AT12 Niederoesterreich 2 45 DEA3 Muenster 2 89 FR3 Nord-Pas-De-Calais 2 132 IT72 Molise 2

3 AT13 Wien 2 46 DEA4 Detmold 2 90 FR41 Lorraine 2 133 IT8 Campania 2

4 AT21 Kaernten 2 47 DEA5 Arnsberg 2 91 FR42 Alsace 2 134 IT91 Puglia 2

5 AT22 Steiermark 2 48 DEB1 Koblenz 2 92 FR43 Franche-Comte 2 135 IT92 Basilicata 2

6 AT31 Oberoesterreich 2 49 DEB2 Trier 2 93 FR51 Pays De La Loire 2 136 IT93 Calabria 2

7 AT32 Salzburg 2 50 DEB3 Rheinhessen-Pfalz 2 94 FR52 Bretagne 2 137 ITA Sicilia 2

8 AT33 Tirol 2 51 DEC Saarland 2 95 FR53 Poitou-Charentes 2 138 ITB Sardegna 2

9 AT34 Vorarlberg 2 52 DED1 Chemnitz 2 96 FR61 Aquitaine 2 139 LU Luxembourg 0

10 BE1 Reg.Bruxelles-Cap 1 53 DED2 Dresden 2 97 FR62 Midi-Pyrenees 2 140 NL1 Noord-Nederland 1

11 BE2 Vlaams Gewest 1 54 DED3 Leipzig 2 98 FR63 Limousin 2 141 NL2 Oost-Nederland 1

12 BE3 Region Wallonne 1 55 DEE1 Dessau 2 99 FR71 Rhone-Alpes 2 142 NL3 West-Nederland 1

13 CH01 Région Lémanique 2 56 DEE2 Halle 2 100 FR72 Auvergne 2 143 NL4 Zuid-Nederland 1

14 CH02 Espace Mittelland 2 57 DEE3 Magdeburg 2 101 FR81 Languedoc-Roussillon 2 144 NO01 Oslo Og Akershus 2

15 CH03 Nordwestschweiz 2 58 DEF Schleswig-Holstein 2 102 FR82 Provence-Alpes-Cote D'Azur 2 145 NO02 Hedmark Og Oppland 2

16 CH04 Zürich 2 59 DEG Thueringen 2 103 FR83 Corse 2 146 NO03 Sør-Østlandet 2

17 CH05 Ostschweiz 2 60 DK Denmark 0 104 GR11 Anatoliki Makedonia, Thraki 2 147 NO04 Agder Og Rogaland 2

18 CH06 Zentralschweiz 2 61 ES11 Galicia 2 105 GR12 Kentriki Makedonia 2 148 NO05 Vestlandet 2

19 CH07 Ticino 2 62 ES12 Asturias 2 106 GR13 Dytiki Makedonia 2 149 NO06 Trøndelag 2

20 DE11 Stuttgart 2 63 ES13 Cantabria 2 107 GR14 Thessalia 2 150 NO07 Nord-Norge 2

21 DE12 Karlsruhe 2 64 ES21 Pais Vasco 2 108 GR21 Ipeiros 2 151 PT11 Norte 2

22 DE13 Freiburg 2 65 ES22 Navarra 2 109 GR22 Ionia Nisia 2 152 PT12 Centro (P) 2

23 DE14 Tuebingen 2 66 ES23 Rioja 2 110 GR23 Dytiki Ellada 2 153 PT13 Lisboa E Vale Do Tejo 2

24 DE21 Oberbayern 2 67 ES24 Aragon 2 111 GR24 Sterea Ellada 2 154 PT14 Alentejo 2

25 DE22 Niederbayern 2 68 ES3 Madrid 2 112 GR25 Peloponnisos 2 155 PT15 Algarve 2

26 DE23 Oberpfalz 2 69 ES41 Castilla-Leon 2 113 GR3 Attiki 2 156 SE01 Stockholm 2

27 DE24 Oberfranken 2 70 ES42 Castilla-La Mancha 2 114 GR41 Voreio Aigaio 2 157 SE02 Oestra Mellansverige 2

28 DE25 Mittelfranken 2 71 ES43 Extremadura 2 115 GR42 Notio Aigaio 2 158 SE04 Sydsverige 2

29 DE26 Unterfranken 2 72 ES51 Cataluna 2 116 GR43 Kriti 2 159 SE06 Norra Mellansverige 2

30 DE27 Schwaben 2 73 ES52 Comunidad Valenciana 2 117 IE01 Border, Midland And Western 2 160 SE07 Mellersta Norrland 2

31 DE3 Berlin 2 74 ES61 Andalucia 2 118 IE02 Southern And Eastern 2 161 SE08 Oevre Norrland 2

32 DE4 Brandenburg 2 75 ES62 Murcia 2 119 IT11 Piemonte 2 162 SE09 Smaaland Med Oearna 2

33 DE5 Bremen 2 76 FI13 Ita-Suomi 2 120 IT12 Valle D'Aosta 2 163 SE0A Vaestsverige 2

34 DE6 Hamburg 2 77 FI14 Vali-Suomi 2 121 IT13 Liguria 2 164 UKC North East 1

35 DE71 Darmstadt 2 78 FI15 Pohjois-Suomi 2 122 IT2 Lombardia 2 165 UKD North West 1

36 DE72 Giessen 2 79 FI16 Uusimaa (Suuralue) 2 123 IT31 Trentino-Alto Adige 2 166 UKE Yorkshire And The Humber 1

37 DE73 Kassel 2 80 FI17 Etela-Suomi 2 124 IT32 Veneto 2 167 UKF East Midlands 1

38 DE8 Mecklenburg-Vorpommern 2 81 FI2 Aland 2 125 IT33 Friuli-Venezia Giulia 2 168 UKG West Midlands 1

39 DE91 Braunschweig 2 82 FR1 Ile De France 2 126 IT4 Emilia-Romagna 2 169 UKH Eastern 1

40 DE92 Hannover 2 83 FR21 Champagne-Ardenne 2 127 IT51 Toscana 2 170 UKJ South East 1

41 DE93 Lueneburg 2 84 FR22 Picardie 2 128 IT52 Umbria 2 171 UKI London 1

42 DE94 Weser-Ems 2 85 FR23 Haute-Normandie 2 129 IT53 Marche 2 172 UKK South West 1

43 DEA1 Duesseldorf 2 86 FR24 Centre 2 130 IT6 Lazio 2 173 UKL Wales 1

87 FR25 Basse-Normandie 2 174 UKM Scotland 1

175 UKN Northern Ireland 1


Geography of innovative activity/1

Countryno of

regions 81-83 rank 88-90 rank 95-97 rank 99-01 rank 82-90 90-96 96-00Switzerland 7 17,6 1 26,1 1 26,3 1 34,4 1 5,6 0,1 6,7Germany 40 10,6 2 19,2 2 16,8 3 25,2 2 8,5 -1,9 10,1Finland 6 1,9 11 6,7 7 14,5 4 24,7 3 17,8 11,1 13,3Sweden 8 8,5 4 10,8 3 17,7 2 24,1 4 3,4 7,1 7,8Netherlands 4 5,6 5 10,6 4 11,6 5 18,4 5 9,1 1,3 11,5Luxembourg 1 9,3 3 6,2 9 8,8 8 15,6 6 -5,8 5,0 14,3Denmark 1 3,0 9 5,6 10 10,0 6 15,2 7 8,9 8,2 10,6Austria 9 4,2 7 8,4 6 8,8 9 13,0 8 9,7 0,7 9,8France 22 4,9 6 8,4 5 9,1 7 11,9 9 7,6 1,1 6,8Belgium 3 2,8 10 5,6 11 8,4 10 11,9 10 9,9 6,0 8,6United Kingdom 12 4,2 8 6,7 8 6,6 11 9,0 11 6,6 -0,1 7,7Norway 7 1,5 12 3,5 13 5,3 12 7,8 12 11,4 6,1 9,5Italy 20 1,4 13 3,7 12 4,6 13 6,3 13 14,5 2,9 7,9Ireland 2 0,6 14 1,6 14 2,8 14 5,2 14 14,2 7,4 15,9Spain 15 0,1 15 0,5 15 1,1 15 1,8 15 19,0 9,3 13,3Greece 13 0,1 16 0,2 16 0,3 16 0,5 16 12,4 6,0 12,1Portugal 5 0,0 17 0,1 17 0,2 17 0,4 17 17,1 9,8 19,0

patents per 100.000 inhabitants (annual average) variation (annual average)


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 Finlandmap3

• the degree of disparities in the regional distribution of innovative activities has decreased, but not in an homogeneous way


Concentration of innovation across regions and along time

no of regions 81-83 88-90 94-96 99-01 81-83 88-90 94-96 99-01

Austria 9 24.0% 20.7% 21.4% 20.9% 0.45 0.44 0.48 0.55Belgium 3 56.3% 63.7% 69.2% 60.0% 0.07 0.22 0.30 0.06Finland 6 56.9% 49.0% 52.1% 51.8% 0.96 1.08 0.91 0.84France 22 50.7% 43.8% 42.5% 42.2% 0.87 0.76 0.70 0.68Germany 40 13.0% 11.6% 10.7% 12.4% 0.77 0.61 0.71 0.67Greece 13 74.2% 62.6% 62.2% 65.4% 1.53 1.24 1.23 1.29Ireland 2 86.9% 84.1% 86.1% 81.1% 0.61 0.46 0.55 0.30Italy 20 35.5% 40.1% 35.9% 34.1% 1.03 1.04 1.01 0.98Netherlands 4 43.9% 48.8% 41.2% 56.1% 0.74 0.83 0.58 1.00Norway 7 41.8% 41.2% 39.2% 37.7% 0.93 0.74 0.69 0.60Portugal 5 80.2% 51.7% 61.2% 49.7% 1.22 1.01 0.59 0.70Spain 15 54.1% 48.4% 42.0% 40.8% 1.19 1.19 1.00 0.97Sweden 8 29.4% 30.1% 34.0% 33.8% 0.52 0.42 0.50 0.53Switzerland 7 32.1% 25.2% 22.9% 21.0% 0.71 0.45 0.36 0.31United Kingdom 12 26.2% 24.1% 22.3% 23.4% 0.60 0.58 0.53 0.56

EU 175 8.4% 7.1% 6.8% 6.0% 1.42 1.17 1.05 1.05

CR1 CV


Spatial dependence of innovative activity

Tab. 3 Spatial autocorrelation in the innovative activity (Moran's I test, normal approximation)

1981-83 1988-90 1994-96 1999-01 contiguity Z-value Prob Z-value Prob Z-value Prob Z-value Prob

1st- order 3.4 0.0 4.1 0.0 4.3 0.0 4.5 0.0

2nd-order 2.8 0.0 3.6 0.0 4.2 0.0 4.3 0.0

Total manufacturing

3rd-order 3.4 0.0 3.4 0.0 3.7 0.0 3.5 0.0

Presence of a strong and positive spatial autocorrelation

process in the innovative activity among contiguous areas


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


Index of technological specialisation (top sector) in the European

regions (annual average, 1999-

2001)


Composition of innovative activity (patenting) per sector

Patents per sector

88-90 94-96 99-01 88-90 94-96 99-01 88-90 - 94-96 94-96 - 99-01Food and beverages 186.6 215.6 304.1 0.67 0.71 0.63Tobacco 15.9 20.8 20.2 0.06 0.07 0.04Textiles 301.2 330.1 488.0 1.08 1.08 1.01Wearing apparel 69.7 80.7 122.7 0.25 0.26 0.25Leather and footwear 90.2 94.0 111.0 0.32 0.31 0.23Wood products, except furniture 234.7 245.6 339.6 0.84 0.81 0.70Paper 254.3 301.1 392.3 0.91 0.99 0.81Printing and publishing 86.0 89.5 135.8 0.31 0.29 0.28Coke and refined petroleum products 405.6 377.3 455.4 1.45 1.24 0.95Chemicals and chemical products 5482.2 5844.0 8400.0 19.56 19.18 17.43Rubber and plastic 673.1 743.1 1081.6 2.40 2.44 2.24Non metallic mineral products 609.6 667.3 956.1 2.18 2.19 1.98Basic metals 203.7 222.7 306.8 0.73 0.73 0.64Fabricated metal products 1925.0 2084.9 3009.4 6.87 6.84 6.25Machinery 6354.9 6683.3 9985.9 22.68 21.93 20.72Office, computing 490.9 562.5 1155.2 1.75 1.85 2.40Electrical machinery 2822.4 3111.9 5339.1 10.07 10.21 11.08Radio, television, communication equip. 1893.6 2339.4 4823.6 6.76 7.68 10.01

Precision and medical instruments 2420.0 2670.6 4751.1 8.64 8.76 9.86Motor vehiclel, trailers and semitrailers 1182.7 1275.3 2049.7 4.22 4.19 4.25Other transport equipment 1034.7 1125.2 1904.7 3.69 3.69 3.95Furniture 1178.0 1268.1 1894.5 4.20 4.16 3.93Recycling and other 106.2 118.7 158.4 0.38 0.39 0.33Total manufacturing 28021.3 30471.6 48185.2 100.00 100.00 100.00

sectorabsolute values % values % variation

15.5 41.1

30.6 -3.1

9.6 47.8

15.7 52.1

4.3 18.1

4.7 38.2

18.4 30.3

4.0 51.8

-7.0 20.7

6.6 43.7

10.4 45.6

9.5 43.3

9.3 37.8

8.3 44.3

5.2 49.4

14.6 105.4

10.3 71.6

23.5 106.2

10.4 77.9

7.8 60.7

8.7 69.3

7.7 49.4

11.8 33.5

8.7 58.1


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,…).

ii2i1ii eZZRDI 321


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


Basic regression

I = patents per capita (annual average 1999-2001)

• RD = share of GDP devoted to R&D (1989-96)• DENS = density of population (1997-99)• MAN = share of manufacturing employment (1997-99)• NAT = national dummies

Extensions:Spatial lag dependent variableSpatial lag R&D with distance decay effectsSpatial spillovers within and across national borders Spatial spillovers and technological similarities

17

1,,3,2,1, loglogloglog

ctiiccstistiqtiti NATMANDENSRDI


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


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)

23

1,

17

1,5,4,3

,2,1,

dtijjdd

cjccstjstjstj

qtijqtijtji

SCTNATRDGDPDENS

DIVISTI


Use of spatial econometric techniques

W may be: - Contiguity – Technology - Technology*contiguity

17

1

23

1,,6,5

,4,3,2,1,

c dtijjddicctjistj

stjstjqtijqtijtji

SCTNATWIRD

GDPDENSDIVISTI

ititit W

The error term is specified as follows:

LAG MODEL:

ERROR MODEL:


coeff. p-value coeff. p-valuespecialisation 7.16 0.00 16.40 0.00diversity -0.05 0.00 0.00 0.00agglomeration -0.01 0.00 -0.01 0.00home market 1.39 0.00 2.41 0.00R&D expenditure 7.23 0.00 12.15 0.00

Food and beverages 0.00 0.00 -42.63 0.00

Tobacco -1.35 0.00 -42.32 0.00

Textiles 13.05 0.00 -40.05 0.00Wearing apparel 3.92 0.00 -49.00 0.00Leather and footwear 18.66 0.00 -45.33 0.00Wood products, except furniture 17.18 0.00 -44.13 0.00Paper -1.00 0.00 -42.24 0.00Printing and publishing 4.25 0.00 -47.99 0.00Coke and petroleum products 6.38 0.00 -38.84 0.00Chemicals and chemical products 2.27 0.00 132.84 0.00Rubber and plastic 8.08 0.00 -24.32 0.00Non metallic mineral products 3.26 0.00 -28.19 0.00Basic metals 4.07 0.00 -43.15 0.00Fabricated metal products 11.63 0.00 26.09 0.00Machinery -6.95 0.00 205.33 0.00

Office, computing 9.31 0.00 -24.80 0.00

Electrical machinery 7.29 0.00 79.80 0.00Radio, television and other 8.01 0.00 64.09 0.00Precision and medical instruments 0.00 0.00 63.83 0.00Motor vehicles -26.97 0.00 -0.57 0.00Other transport equipment -28.61 0.00 -5.36 0.00Furniture -24.60 0.00 -4.04 0.00Recycling and other -31.40 0.00 -49.01 0.00

National dummies yes yes

observations 3864 3910Adjusted R-squared 0.52 0.47

1994-96 1999-01

Panel estimation,

• 94-96• 99-01


Some sector regressions, 94-96, contiguity matrix

Dependent variable: patents per capita, 1994-96169 observations; national dummies included

TextilesPrinting and publishing

ChemicalsBasic metals

Fabricated metal products

MachineryOffice,

computingElectrical machinery

Precision and medical

instruments

Motor vehicles

W_I 35.090 15.333 23.524 41.775 48.695 49.272 25.839 31.248 23.172 38.522

0.000 0.041 0.005 0.000 0.000 0.000 0.000 0.000 0.000 0.000

IST-1 3.838 1.431 264.388 3.109 19.369 55.105 11.641 81.478 53.395 32.408

0.014 0.001 0.000 0.001 0.043 0.148 0.000 0.000 0.002 0.000

DIV-1 0.039 0.001 7.929 0.035 0.211 0.301 0.069 0.601 0.371 0.245

0.354 0.967 0.293 0.226 0.302 0.349 0.245 0.091 0.195 0.105

DENS-1 -0.002 -0.001 -0.029 -0.001 -0.009 -0.028 -0.002 -0.011 -0.010 -0.007

0.004 0.000 0.085 0.240 0.006 0.004 0.022 0.066 0.032 0.010

GDP-1 0.409 0.143 10.625 0.283 2.949 8.355 0.780 4.291 3.387 1.907

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

R&D-1 3.305 0.799 49.771 1.133 12.341 63.508 7.484 38.592 39.416 17.544

0.000 0.000 0.001 0.005 0.000 0.000 0.000 0.000 0.000 0.000

est. method ML ML ML ML ML ML ML ML ML ML

R2 adj 0.75 0.70 0.59 0.71 0.82 0.86 0.78 0.76 0.80 0.78LIK 268.55 470.93 -273.21 325.47 -1.29 -179.06 209.47 -99.50 -55.86 51.21AIC -491.09 -895.85 592.42 -604.93 48.57 404.11 -372.94 245.00 157.72 -56.42

LM (error) 2.934 0.010 0.223 0.109 0.007 1.633 0.583 0.142 0.450 1.6620.087 0.920 0.637 0.741 0.933 0.201 0.445 0.706 0.503 0.197

a selection of sectors


Dependent variable: patents per capita, 1999-01171 observations; national dummies included


Chemicals Basic metalsFabricated

metal products

MachineryOffice,

computingElectrical machinery


instrumentsMotor vehicles

W_I 35.555 19.855 22.725 26.790 48.228 46.253 20.232 38.751 23.671 36.6490.000 0.006 0.003 0.000 0.000 0.000 0.007 0.000 0.000 0.000

IST-1 6.630 1.077 315.674 6.939 46.616 174.707 14.783 158.031 56.329 52.678

0.001 0.019 0.000 0.000 0.001 0.005 0.048 0.000 0.049 0.000

DIV-1 0.002 0.000 11.660 0.001 0.015 0.038 0.007 0.041 0.020 0.014

0.429 0.684 0.323 0.526 0.235 0.097 0.362 0.136 0.376 0.256

DENS-1 -0.003 -0.001 -0.041 -0.002 -0.017 -0.055 -0.005 -0.029 -0.023 -0.015

0.002 0.003 0.030 0.002 0.001 0.001 0.062 0.016 0.019 0.005

GDP-1 0.728 0.160 18.205 0.608 4.765 15.107 2.430 8.781 8.911 3.330

0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

R&D-1 5.260 1.623 76.825 1.521 22.561 116.642 16.787 75.002 69.574 37.213

0.000 0.000 0.000 0.008 0.000 0.000 0.000 0.000 0.000 0.000

est. method ML ML ML ML ML ML ML ML ML ML

R2 adj 0.78 0.75 0.69 0.69 0.79 0.84 0.66 0.75 0.75 0.72LIK 223.82 441.81 -286.53 262.07 -74.27 -258.32 32.23 -213.50 -178.29 -73.06AIC -401.64 -837.62 619.05 -478.14 194.54 562.64 -18.47 473.01 402.58 192.12

LM (error) 0.095 9.224 0.802 0.021 1.469 0.007 0.616 2.009 2.017 3.2240.758 0.002 0.370 0.885 0.225 0.932 0.433 0.156 0.156 0.073


Some sector regressions, 99-01, contiguity matrix


Dependent variable: patents per capita, 1999-01171 observations; national dummies included; technologicy*contiguity matrix


ChemicalsBasic metals

Fabricated metal

productsMachinery

Office, computing

Electrical machinery


instruments

Motor vehicles

W_I 36.554 19.408 26.808 27.949 49.904 45.830 16.403 35.158 22.097 35.416

0.000 0.009 0.000 0.000 0.000 0.000 0.036 0.000 0.002 0.000

IST-1 7.005 1.109 311.035 7.548 43.028 129.147 14.292 160.492 60.891 52.060

0.000 0.017 0.000 0.000 0.003 0.045 0.059 0.000 0.040 0.000

DIV-1 0.002 0.000 8.579 0.001 0.012 0.028 0.006 0.044 0.022 0.014

0.409 0.648 0.470 0.582 0.336 0.235 0.386 0.128 0.351 0.263

DENS-1 -0.003 -0.001 -0.040 -0.002 -0.015 -0.054 -0.006 -0.029 -0.023 -0.015

0.004 0.003 0.029 0.003 0.005 0.001 0.052 0.019 0.019 0.006

GDP-1 0.672 0.155 17.529 0.563 4.123 13.853 2.397 8.086 8.547 3.199

0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001

R&D-1 5.154 1.590 74.978 1.480 21.896 110.879 16.518 72.891 67.867 36.630

0.000 0.000 0.000 0.010 0.000 0.000 0.000 0.000 0.000 0.000

estimation method ML ML ML ML ML ML ML ML ML ML

R2 adj 0.78 0.74 0.69 0.69 0.79 0.83 0.65 0.73 0.75 0.71LIK 222.37 438.28 -284.11 260.44 -74.77 -261.85 29.95 -216.46 -179.37 -74.58AIC -398.73 -830.55 614.22 -474.88 195.55 569.69 -13.90 478.91 404.73 195.15

LM (error) 0.115 5.922 1.327 0.224 1.423 0.106 1.029 0.802 1.825 1.9740.735 0.015 0.249 0.636 0.233 0.745 0.310 0.371 0.177 0.160


Some sector regressions, 99-01, cont*tech matrix


Robustness tests

• Main results are robust with respect to:– Dependent variable expressed in

absolute values

– Double log

– Tobit estimation (but without lags)


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


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.


Distribution of innovative

activity in the European

regions, 1989-1991

(patents per capita, annual

average)


Distribution of innovative

activity in the European

regions, 1999-2001

(patents per capita, annual

average)


Spatial autocorrelation, Moran index

sector period 1 2 3 4 sector period 1 2 3 4

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

88-90 88-90

94-96 94-9699-01 99-01

Total manufacturing

Non metallic mineral products

Basic metals

Fabricated metal products

Machinery

Office, computing

Electrical machinery

Radio, television and communication

equipment


instruments

Motor vehiclel, trailers and semitrailers

Other transport equipment

Food and beverages

Tobacco

Textiles

Wearing apparel

Leather and footwear

Wood products, except furniture

Paper

Coke and refined petroleum products

Printing and publishing

Chemicals and chemical products

Rubber and plastic

Furniture

Recycling and other


MORAN Scatterplot for

innovative activity in the

European regions, 1999-

2001 (patents per

capita, annual average;

number of regions in

parenthesis )

Università degli Studi di Cagliari e Sassari Innovation clusters in European regions Rosina...

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