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Geography of inventive activity in OECD regions. Stefano Usai CRENoS, University of Cagliari 8 july 2010 DIMETIC Summer School, Pecs. - PowerPoint PPT Presentation
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Geography of inventive activityin OECD regions
Stefano UsaiCRENoS, University of Cagliari
8 july 2010DIMETIC Summer School, Pecs
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
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
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Technological progress as an engine of growth
PCT-GDP per capita 1998-2000
0
20,000
40,000
60,000
80,000
100,000
120,000
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000
pct
gdp p
er
capita (
euro
s)
PCT-GDP per capita 2002-2004
0
20,000
40,000
60,000
80,000
100,000
120,000
0 2,000 4,000 6,000 8,000 10,000
pct
gdp p
er
capita (
euro
s)
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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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TABLE OF CONTENTS
• 1 Introduction• 2 Theoretical and empirical background• 3 Some methodological and data issues• 4 Descriptive statistics for patents and citations
– Spatial concentration• 5 Econometric estimation
– Cross region Knowledge Production Function (KPF)– Across regions Knowledge Gravity Model (KGM)
• 6 (Many) Other things to do
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Main Objectives
• To estimate a Knowledge Production Function (KPF) at the regional level– (and later at the regional-industry)
• To estimate a Knowledge Gravity Model (KGM) at the regional level (also for some sectors)
• We assess the importance of local and external factors and among them knowledge spillovers (both pecuniary and technological) in facilitating innovative activity
• We also assess the importance of geographical proximity
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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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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 ofdeveloped countries
with a homogenous dataset
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www.oecd.org/gov/regional/statisticindicators
OECD Regional Database (ORDB)/1
• The OECD Regional Database provides a unique set of comparable statistics and indicators on about 2000 regions in 30 countries. It currently encompasses yearly time-series for around 40 indicators of demography, economic accounts, labour market, social and innovation themes in the OECD member countries and other economies.
• Regions in OECD member countries have been classified according to two territorial levels (TL) to facilitate international comparability. The higher level (Territorial level 2) consists of macro-regions, while the lower level (Territorial level 3) is composed of micro-regions.
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OECD Regional Database (ORDB)/2
• In addition, OECD small regions (Territorial level 3) are classified according to their geography into predominantly rural, intermediate or predominantly urban. This typology of regions has been refined to take into account remoteness of rural regions: the extended typology comprises remote rural regions, rural regions close to a city, intermediate and predominantly urban regions.
• The OECD metrodatabase provides statistics on 90 large metropolitan areas in the OECD countries and shows how these regions have changed over the past decade.
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http://stats.oecd.org/OECDregionalstatistics/
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Country Large Regions (TL2) Small Regions (TL3)Australia 8 States/Territories 60 Statistical Divisions
Austria 9 Bundesländer35 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 Länder97 Spatial planning regions (groups of Kreise)
Greece4 Groups of Development regions
13 Development regions
Hungary 7 Tervezesi‑statisztikai regio 20 Megyek (+Budapest)
Iceland 2 regions 8 Landsvaedi
Ireland2 Groups Regional Authority Regions
8 Regional Authority Regions
Italy 21 Regioni 103 Province
Japan 10 Groups of prefectures 47 Prefectures
Territorial grids by countryTerritorial grids by country
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Country Large Regions (TL2) Small Regions (TL3)
Korea 7 Regions16 Special city, Metropolitan area and Province
Luxembourg 1 State 1 StateMexico 32 Estados 209 Grupos de MunicipiosNetherlands 4 Landsdelen 12 Provinces
New Zealand2 Groups of regional Councils
14 Regional Councils
Norway 7 Landsdeler 19 FylkerPoland 16 Voïvodships 45 Subregions
Portugal5 Comissaoes de coordenaçao regional + 2 Regioes autonomas
30 Grupos de Concelhos
Slovak Republic 4 Zoskupenia Karajov 8 KrajSpain 19 Comunidades autonomas 52 ProvinciasSweden 8 Riksomraden 21 LänSwitzerland 7 Grandes régions 26 CantonsTurkey 26 Regions 81 Provinces
United Kingdom12 Government Office Regions + Countries
133 groups of authorities or districts
United States 51 States 179 BEA Economic Areas
the OECD Patent Database fully covers:
• Patent applications to the European Patent Office (EPO) (from 1978 onwards);
• Patents granted by the US Patent and Trademark Office (USPTO) (from 1976 onwards);
• Patents filed under the Patent Co-operation Treaty (PCT), at international phase, that designate the EPO (from 1978 onwards);
• Patents that belong to Triadic Patent Families (OECD definition): i.e. sub-set of patents all filed at the EPO, at the Japanese Patent Office (JPO) and granted by the USPTO, protecting the same set of inventions.
• EPO and PCT patent counts are based on data received from the EPO (EPO Bibliographic database, patent published until November 2009). Series on Triadic patent families are mainly derived from EPO's Worldwide Statistical Patent Database (PATSTAT, September 2009). Regional data are based on OECD, REGPAT database, January 2010.
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OECD Regional Database (ORDB)/3
• PCT provide a unified preliminary procedure for filing patent applications to protect inventions in each of its Contracting States.
• PCT procedure is costly and a step ahead the national award, it is assumed that most innovations are valuable ones.
• Comparing PCT and TPF (triadic patent families):– TPF are less numerous (they share one or more priorities at
USPTO, JPTO, EPO)– Both indexes do not suffer from home-bias– The latter provides a stronger profit-based indicator for an
international report even though both refer to valuable inventions
– PCT permit a wider perspective and its regionalisation is more straightforward
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OECD Regional Database for the KPF
• Macro areas: Europe, Asia/Pacific and North America.• Countries: 30 countries• Regional level (tl2 and tl3)
– Most of this report and the econometric analysis is based on TL2
– The regions of OECD are 324 (some countries at TL0 included)
• Temporal dimension: (1998-2000 and 2002-2004)• Sectoral level: (potentially 44 NACE-ISIC sectors)
• Main indicator:– Absolute value of PCT (around 600,000 in total)– PCT per million population
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OECD Regional Database for the KGM
• It is difficult to keep track of knowledge flows: one way is through citations, which are provided under request by the OECD STI office
• Citations EPO on EPO
• We use only data on Europe for 22 countries (EU15 plus Slovak Republic, Poland, Hungary, Czech Republic, Turkey and Norway and Switzerland)
• It provides a dynamic picture for the period going from 1990 to 2000
• Most importantly provides disaggregated estimations for some selected sectors, that is Chemicals, Machinery, Traditionals
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Some features of the RDB
• 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• Use of “Schmlook et al.” Technology Concordance (still
to be done)– 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
Regions in ourdatabase
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OCSE
Nation
Australia 8 TL2 962,919 2,484,040
Austria 9 TL2 9,319 902,893
Belgium 3 TL2 10,173 3,458,922
Canada 12 TL2 766,934 2,639,336
Czech Republic 8 TL2 9,859 1,276,004
Denmark 1 TL0 43,098 5,389,733
Finland 5 TL2 67,629 1,042,780
France 22 TL2 24,726 2,735,994
Germany 41 TL2 8,708 2,012,461
Greece 4 TL2 32,907 2,756,083
Hungary 7 TL2 13,290 1,447,386
I celand 1 TL0 102,696 290,661
I reland 1 TL0 69,797 3,993,767
I taly 21 TL2 14,349 2,745,044
J apan 10 TL2 37,758 12,758,000
Korea 7 TL2 14,209 6,835,549
Luxembourg 1 TL2 2,586 449,733
Mexico 1 TL0 1,959,248 101,970,271
Netherlands 4 TL2 8,471 4,054,683
New Zealand 1 TL0 277,039 4,002,267
Norway 7 TL2 43,928 650,180
Poland 16 TL2 19,543 2,387,900
Portugal 7 TL2 13,135 1,491,029
Slovak Republic 4 TL2 12,259 1,345,067
Spain 19 TL2 26,631 2,210,705
Sweden 8 TL2 55,168 1,119,863
Switzerland 7 TL2 5,898 1,052,053
Turkey 1 TL0 769,603 70,228,333
United Kingdom 37 TL2 6,573 1,610,381
United States 51 TL2 119,774 5,702,560
Total 324 94,757 3,556,366
average region (km2)
average region (population 2002-04)Regions Level
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Geographical distribution of innovative activity
OECD
Nations
Num. Of Regions 1998-2000 2002-2004 var % Nations
Num. Of Regions 1998-2000 2002-2004 var %
Australia 8 78.7 94.2 19.8 Korea 7 27.5 69.1 151.4
Austria 9 77.4 106.2 37.2 Luxembourg 1 112.1 70.6 -37.0
Belgium 3 72.1 74.9 4.0 Mexico 1 0.8 1.3 56.7
Canada 12 61.1 73.6 20.4 Netherlands 4 158.7 160.1 0.9
Czech Republic 8 7.0 7.0 0.0 New Zealand 1 68.1 84.8 24.5
Denmark 1 150.1 184.0 22.6 Norway 7 111.1 98.2 -11.6
Finland 5 255.5 228.3 -10.6 Poland 16 1.6 2.1 27.4
France 22 68.6 82.0 19.5 Portugal 7 2.3 1.6 -30.7
Germany 41 143.5 172.5 20.2 Slovak Republic 4 4.9 5.4 9.9
Greece 4 4.2 3.7 -11.7 Spain 19 13.5 16.8 24.2
Hungary 7 14.6 15.1 3.8 Sweden 8 285.3 204.9 -28.2
Iceland 1 103.8 142.4 37.2 Switzerland 7 175.0 233.0 33.2
Ireland 1 51.2 66.2 29.3 Turkey 1 1.0 1.9 93.0
Italy 21 25.9 34.7 34.0 United Kingdom 37 69.0 67.4 -2.3
J apan 10 62.2 129.5 108.0 United States 51 125.2 141.2 12.7
Total 324 2331.9 2572.6 10.3
PCT_ per million Population PCT_ per million Population
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OECD: PCT per million Population, 1998-2000
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OECD: PCT per million Population, 2002-2004
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