“Report on the” Geography of inventive activity in OECD regions Cagliari 18 dicembre 2009 Thanks to a contribution by OECD, Directorate for Science Technology and Industry within the research project on THE IMPACT OF BUSINESS STRUCTURES AND STRATEGIES ON THE DEGREE AND PATTERNS OF INNOVATION AT REGIONAL LEVEL 1

Technological progress as an engine of growth 07/05/2018 Geography of innovation in OECD regions

Research line Technological activity is acknowledged as the main engine of growth and we contribute in investigating on how this engine works at the regional level First systematic, albeit preliminary, attempt to analyse comparatively the processes of knowledge creation and dissemination across regions (and possibly in the future also sectors) in OECD countries 07/05/2018 Geography of innovation in OECD regions

TABLE OF CONTENTS 1 Introduction 2 Theoretical and empirical background 3 Some methodological and data issues 4 Descriptive statistics 4.1 Spatial concentration 4.2 Patents and other indicators 4.3 Sector Analysis 5 Econometric estimation Cross region KPF Cross region-industry KPF 6 Conclusions and Policy implications 7 Appendix 07/05/2018 Geography of innovation in OECD regions

Main Objectives To estimate a Knowledge Production Function (KPF) at the regional level (and later at the regional-industry) We assess the importance of local and external factors and among them knowledge spillovers (both pecuniary and technological) in facilitating innovative activity We, therefore, also assess the importance of geographical proximity 07/05/2018 Geography of innovation in OECD 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) the ability to exploit technological achievements from outside, that is external technological spillovers (through several channels) In this respect geographical (Glaeser et al, 1992; Henderson, 1997, Paci and Usai, 2000) and technological (Keller, 2000, Verspagen, 2000, Paci and Usai, 2005) proximity have been considered and proved relevant. 07/05/2018 Geography of innovation in OECD regions

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; Carlino et al, 2007; O hUchallain and Leslie, 2007; Soon and Storper, 2007 For the EU case: Maurseth and Verspagen, 1999; Bottazzi and Peri, 2003; Moreno, Paci and Usai, 2005, 2006a, 2006b, Rodriguez Pose and Crescenzi, 2007 For the US and the EU together*: Crescenzi, Rodriguez-Pose and Storper, 2007. *with heterogenous datasets Never done for the whole of developed countries with a homogenous dataset 07/05/2018 Geography of innovation in OECD regions

The literature behind us: EPO patents for EU regions Clusters of regional innovative systems have formed across Europe thanks to internal and external factors They are within national innovation systems: different paths for each country They are getting stronger and bigger, thanks to spatial dependence All in all, it is clear that sector and regional dimension should be combined to have a clear picture of innovation distribution and dynamics across countries… 07/05/2018 Geography of innovation in OECD regions

The literature behind us: EPO patents for EU regions The role of technological specialisation is significative and positive in many sectors Contrary to results for US and France (but for some sectors) Such a role is deepening along time in most sectors Contrary to specialisation in production: there are less delocalisation processes in innovation. Diversity is almost always positive but never significant Contrary to most previous analyses…but for results for France… 07/05/2018 Geography of innovation in OECD regions

OECD Regional Database (ORDB)/1 ORDB provides quantitative information on socio-economic issues (demographics, economy and labour market, social issues) for potentially 2014 regions within 30 OECD member countries Innovative activity is measured both with input and output indicators, among the latter patents We concentrate on the latter and in particular on Patent Cooperation Treaty (PCT) applications...but before that 07/05/2018 Geography of innovation in OECD regions

07/05/2018 Geography of innovation in OECD regions

07/05/2018 Geography of innovation in OECD regions

http://stats.oecd.org/ 07/05/2018 Geography of innovation in OECD regions

07/05/2018 Geography of innovation in OECD regions

http://stats.oecd.org/OECDregionalstatistics/ 07/05/2018 Geography of innovation in OECD regions

Territorial grids by country Large Regions (TL2) Small Regions (TL3) Australia 8 States/Territories 60 Statistical Divisions Austria 9 Bundesländer 35 Gruppen von Politischen Bezirken Belgium 3 Régions 11 Provinces Canada 12 Provinces and Territories 288 Census Divisions Czech Republic 8 Groups of Kraje 14 Kraje Denmark 3 Regions 15 Amter Finland 5 Suuralueet 20 Maakunnat France (without DOM-TOM) 22 Régions 96 Départements Germany 16 Länder 97 Spatial planning regions (groups of Kreise) Greece 4 Groups of Development regions 13 Development regions Hungary 7 Tervezesi‑statisztikai regio 20 Megyek (+Budapest) Iceland 2 regions 8 Landsvaedi Ireland 2 Groups Regional Authority Regions 8 Regional Authority Regions Italy 21 Regioni 103 Province Japan 10 Groups of prefectures 47 Prefectures 07/05/2018 Geography of innovation in OECD regions

16 Special city, Metropolitan area and Province Country Large Regions (TL2) Small Regions (TL3) Korea 7 Regions 16 Special city, Metropolitan area and Province Luxembourg 1 State Mexico 32 Estados 209 Grupos de Municipios Netherlands 4 Landsdelen 12 Provinces New Zealand 2 Groups of regional Councils 14 Regional Councils Norway 7 Landsdeler 19 Fylker Poland 16 Voïvodships 45 Subregions Portugal 5 Comissaoes de coordenaçao regional + 2 Regioes autonomas 30 Grupos de Concelhos Slovak Republic 4 Zoskupenia Karajov 8 Kraj Spain 19 Comunidades autonomas 52 Provincias Sweden 8 Riksomraden 21 Län Switzerland 7 Grandes régions 26 Cantons Turkey 26 Regions 81 Provinces United Kingdom 12 Government Office Regions + Countries 133 groups of authorities or districts United States 51 States 179 BEA Economic Areas 07/05/2018 Geography of innovation in OECD regions

OECD Regional Database (RDB)/2 PCT provide a unified preliminary procedure for filing patent applications to protect inventions in each of its Contracting States. PCT does not suffer from home-bias PCT procedure is costly and a step ahead the national award, it is assumed that most innovations are valuable ones. One alternative: triadic patent families (TPF), that is those patents which share one or more priorities at USPTO, JPTO, EPO....but its regionalisation is not possible Its procedure is an intermediate step between the priority application and filing for patent protection abroad 07/05/2018 Geography of innovation in OECD regions

Some features of the PCT PCT provide a measure which is of a sufficiently homogenous quality: potentially highly remunerative innovations. Indicator for both product and process innovations Medium time span (potentially long): three-year averages to smooth data Use of the inventor’s residence instead of applicant’s residence. Specific treatment of multiple inventors 07/05/2018 Geography of innovation in OECD regions

OECD Regional Database (RDB)/3 Macro areas: Europe, Asia/Pacific and North America. Countries: 30 countries Regional level: TL2 (regioni) and TL3 (province) Most of this report and the econometric analysis is based on TL2: 324 regions even though some TL0 are included Temporal dimension: (1998-2000 and 2002-2004) Sectoral level: (potentially 44 NACE-ISIC sectors) 07/05/2018 Geography of innovation in OECD regions

Regions in our database 07/05/2018

Geographical distribution of innovative activity Us 120000, japan 50000, germany 42000. portugal 50, slovak rep. 80, lux 95. 07/05/2018 Geography of innovation in OECD regions

OECD: PCT per million Population, 1998-2000 07/05/2018 Geography of innovation in OECD regions

OECD: PCT per million Population, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Europe: PCT per million, 1998-2000 07/05/2018 Geography of innovation in OECD regions

Europe: PCT per million, 2002-2004 07/05/2018 Geography of innovation in OECD regions

North America: PCT per million, 1998-2000 07/05/2018 Geography of innovation in OECD regions

North America: PCT per million, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Asia/Pacific: PCT per million, 1998-2000 07/05/2018 Geography of innovation in OECD regions

Asia/Pacific: PCT per million, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Synthetic indicators/1 07/05/2018 Geography of innovation in OECD regions

Synthetic indicators/2 07/05/2018 Geography of innovation in OECD regions

PCT per million in 30 best performing regions, 1998-2000 07/05/2018 Geography of innovation in OECD regions

PCT per million in 30 best performing regions, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Spatial distribution of innovative activity/1 The degree of disparities in the regional distribution of innovative activities has increased across OECD countries for three out of four indexes. CV decreases mainly because the average value has changed This phenomenon has not been homogeneous across macroareas (in particular, it is stable in EU and it decreases in the United States) We would like to perform the same analysis across sectors to assess potential differences 07/05/2018 Geography of innovation in OECD regions

Spatial distribution of innovative activity/2 07/05/2018 Geography of innovation in OECD regions

Spatial dependence of innovative activity/3 Presence of strong and positive spatial autocorrelation among contiguous areas. Spatial dependence extends until the 3th order of contiguity The extent of such a dependence is stable along time Spatial dependence is also detected when distances are used instead of contiguities This process has favoured the formation of clusters of innovative regions…(we need sector data in order to see if such a process is differentiated across sectors and how much) Let us see these clusters 07/05/2018 Geography of innovation in OECD regions

Moran scatterplot map, 2002-2004 07/05/2018 Geography of innovation in OECD regions

OECD Regions: PCT per million population variability, 1998-2000 07/05/2018 Geography of innovation in OECD regions

OECD Regions: PCT per million population variability, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Moran scatterplot map Europe, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Moran LISA map, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Moran LISA map Europe, 2002-2004 07/05/2018 Geography of innovation in OECD regions

Convergence in innovative efforts? National level 07/05/2018 Geography of innovation in OECD regions

Convergence in innovative efforts? Regional level 07/05/2018 Geography of innovation in OECD regions

Summary of main novelties… We focus on OECD regions. We have a set of homogeneous indicators for all the countries. We are going to estimate KPF at both the regional level (and later potentially at the industry level) We are going to use specific econometric techniques to analyse the nature and the spatial scope of knowledge creation and diffusion. 07/05/2018 Geography of innovation in OECD regions

Estimation setting The basic KPF relates the inventive output in region i to R&D input in the same region and a set of further factors related to the economic and institutional environment However, the production of knowledge in a region may depend not only on its own research efforts, but also on the knowledge stock available in the whole economy and on its ability to exploit it.

The determinants of innovative activity at the local level: knowledge production function I = local patents (per capita) in region j RD= quota of R&D on GDP (j) HK= tertiary education (j) DENS= population density (j) NAT = national dummies; DU, DR, DCAP= dummies for urban, rural, capital regions DGDP= dummy for above and below average GDP per capita Note: Variables in log Time lags are considered 07/05/2018 Geography of innovation in OECD regions

DATA sample There are several missing values for some important indicators in a handful of countries. No R&D for Japan and Korea. This makes our preliminary analysis basically focus on the comparison of the European and the North American areas. No regionalised data for patents are present for some countries among those included in the analysis, namely Denmark, Iceland, Ireland, Luxembourg, and New Zealand. Also for Mexico and Turkey data are not regionalised, but they are not included. UK number of regions for which the econometric analysis is performed (12) does not coincide with that provided in the descriptive analysis. Eleven regions are excluded from the analysis since they display zero values and therefore their logarithm was not feasible. The econometric analysis is therefore done with 271 observations referring to 25 nations out of a potential 30 countries.

Estimation strategy OLS to assess significance of coefficients and the presence of spatial dependence Discriminate between spatial lag model or spatial error model and re-estimate with ML 07/05/2018 Geography of innovation in OECD regions

Econometric results

Some robustness checks Interactive dummies: DGDP*HK and DGDP*RD Spatial Lag of RD KPF with distance matrix (only for EU and North America) KPF including Japan and Korea (estimation of some variables) KPF with PCT per worker (instead of per capita)

KPF estimation with interactive dummies

KPF estimation with spatial lag of RD

KPF estimation with distance matrix

KPF estimation with Japan and Korea

KPF estimation with PCT per worker

Final remarks Clusters of regional innovative systems have formed across OECD countries Main determinants of knowledge creation are at work both at the local and at the external level Human capital has larger effects than R&D Main determinants are within national innovation systems 07/05/2018 Geography of innovation in OECD regions

Final remarks and questions Clusters of regional innovative systems have formed across OECD countries Main determinants of knowledge creation are at work both at the local and at the external level Are they different with respect to industrial specialisation? Are they within national innovation systems? Are they getting stronger or bigger? 07/05/2018 Geography of innovation in OECD regions

The research agenda for what we have done so far There are still some missing values in the database (Korea and Japan, for example) No detail about RD Public vs private (possible for some countries) Not all spatial externalities are appropriately measured Citations can be used to measure spillovers both within and across regions No measure of other local public knowledge University and research centers? 07/05/2018 Geography of innovation in OECD regions

The research agenda: main options To replicate the descriptive analysis at a more disaggregated territorial level (that is TL3)…the replication of the econometric analysis is problematic since most data for explanatory variables are lacking To focus on industrial disaggregation and to replicate the analysis for all sectors or for a set of them (some high tech). Problem of data… Direct studies of a proxy of knowledge spillovers, that is citation flows 07/05/2018 Geography of innovation in OECD regions

The determinants of innovative activity at the local industry level Note: Variables in log Time lags are considered I = local industry patents (per capita) in sector i and region j IST = technological specialisation index based on location quotient (ij) DIV= diversity index based on herfhindhal (ij) GDP= GDP per capita (j) DENS= population density (j) EDU= tertiary education RD = quota of R&D on GDP (j) NAT = national dummies; Other controls for macroareas, urban and rural regions, citations 07/05/2018 Geography of innovation in OECD regions