1. “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

2. Technological progress as an engine of growth
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Geography of innovation in OECD regions

3. 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
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4. 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
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5. 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
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6. 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.
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7. 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
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8. 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…
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9. 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…
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10. 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
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13. http://stats.oecd.org/ 07/05/2018
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15. http://stats.oecd.org/OECDregionalstatistics/ 07/05/2018
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16. 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
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17. 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
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18. 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
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19. 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
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20. 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: ( and )
Sectoral level: (potentially 44 NACE-ISIC sectors)
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21. Regions in our database
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22. Geographical distribution of innovative activity
Us , japan 50000, germany portugal 50, slovak rep. 80, lux 95.
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23. OECD: PCT per million Population, 1998-2000
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24. OECD: PCT per million Population, 2002-2004
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25. Europe: PCT per million, 1998-2000
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26. Europe: PCT per million, 2002-2004
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27. North America: PCT per million, 1998-2000
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28. North America: PCT per million, 2002-2004
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29. Asia/Pacific: PCT per million, 1998-2000
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30. Asia/Pacific: PCT per million, 2002-2004
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31. Synthetic indicators/1
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32. Synthetic indicators/2
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33. PCT per million in 30 best performing regions, 1998-2000
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34. PCT per million in 30 best performing regions, 2002-2004
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35. 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
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36. Spatial distribution of innovative activity/2
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37. 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
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38. Moran scatterplot map, 2002-2004
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39. OECD Regions: PCT per million population variability, 1998-2000
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40. OECD Regions: PCT per million population variability, 2002-2004
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41. Moran scatterplot map Europe, 2002-2004
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42. Moran LISA map,
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43. Moran LISA map Europe, 2002-2004 07/05/2018
Geography of innovation in OECD regions

44. Convergence in innovative efforts? National level
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45. Convergence in innovative efforts? Regional level
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46. 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.
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47. 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.

48. 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
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49. 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.

50. 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
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51. Econometric results

52. 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)

53. KPF estimation with interactive dummies

54. KPF estimation with spatial lag of RD

55. KPF estimation with distance matrix

56. KPF estimation with Japan and Korea

57. KPF estimation with PCT per worker

58. 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
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59. 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?
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60. 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?
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61. 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
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62. 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
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