The territorial dynamics of innovation in a global world EU, US, China and India in comparison

Size: px

Start display at page:

Download "The territorial dynamics of innovation in a global world EU, US, China and India in comparison"

Marjorie Gaines
5 years ago
Views:

1 The territorial dynamics of innovation in a global world EU, US, China and India in comparison Andrés London School of Economics with Riccardo Crescenzi 1 and Michael Storper 1,2 1 London School of Economics, 2 Sciences Po, Paris Regional innovation in a global economy Centre for Innovation Research/IRIS/University of Stavanger Stavanger, December 11 th, 2012

2 The territorial dynamics of innovation There has been a lot of attention paid to the similarities and differences in the territorial dynamics of innovation between the EU and the US But their position as the most innovative poles is the world is being challenged by emerging countries 1. And especifically by the BRIICS, in general, and China and India, in particular But very little is know about the territorial dynamics of innovation in emerging countries 1. There has been a tendency to assume that these countries are in an earlier stage of the innovative process than the EU or the US 2. And that they will tend to follow a similar path in the future 3. But, the territorial dynamics of innovation are rather different in the EU with respect to the US 4. So will they follow the EU path? Or that of the US? 5. Are they building their own territorial dynamics of innovation? 1

3 Structure of the presentation The facts The model and data The results Conclusions 2

The facts There is significant catching up in patent applications

significantly above Europe PCT applications per million inhabitants

30 20 10 0 Source: OECD 2010 But the gap is still considerable [9

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 USA

4 The facts There is significant catching up in patent applications China displays a greater level of convergence than India The US is significantly above Europe PCT applications per million inhabitants - EU/US Axis Source: OECD 2010 But the gap is still considerable [9 to 1 in the case of China, 50 to 1 in the case of India] USA EU27 EU15 China India Axis China and India 3

The facts (II) The gap, albeit closing rapidly, is greater in terms of patent intensity PCT applications per million inhabitants - Primary Axis USA 180 170 160 150 140 130 120 110 100 90 80 70 60 50

5 The facts (II) The gap, albeit closing rapidly, is greater in terms of patent intensity PCT applications per million inhabitants - Primary Axis USA Patent intensity increased by a factor of 25 in China Patent Intensity, PCT Applications per capita China, India and USA ( ) Secondary Axis (China & India) Source: OECD PatStat USA China India Most of this growth takes place since

6 The facts (III) Smaller differences in terms of R&D R&D expenditure as a % of GDP Source: OECD 2010 China India USA EU15 EU27

The territorial dimension of innovation Clear difference between mature and emerging systems In India and,

(Average PCT applicants) 100 90 80 70 60 50 40 30 20 10 0 10 9 8 7 6 5 4 3 2 1 Source: OECD, 2010 Note: China data

and Malta not included) 1994-2007, 841 OECD TL3 EU15 data 1994-200 Region's Ranking (less innovative to most

7 The territorial dimension of innovation Clear difference between mature and emerging systems In India and, especially, in China, patent applications are much more concentrated than in the EU and US Cumulative percentage (Average PCT applicants) Source: OECD, 2010 Note: China data , 31 provinces India data , 24 states USA data , 179 BEA EA EU24 data (Cyprus, Lithuania and Malta not included) , 841 OECD TL3 EU15 data Region's Ranking (less innovative to most innovative) Cumulative distribution in China Cumulative distribution in India Cumulative distribution in USA Cumulative distribution in EU24 6

8 The territorial dimension of innovation (II) Increasing concentration of innovative activity in China throughout the 1990s and 2000s 1. In 1994 innovative activity is far more concentrated territorially in India than in China 2. Since 2000 patenting is more geographically clustered in Chinese provinces than in Indian states Lowest concentration in the EU 7

The territorial dimension of innovation (III) Top ten innovative regions, relative to population China India USA 1 Beijing Delhi San Jose-San Francisco-Oakland, CA 2 Shanghai Haryana San

9 The territorial dimension of innovation (III) Top ten innovative regions, relative to population China India USA 1 Beijing Delhi San Jose-San Francisco-Oakland, CA 2 Shanghai Haryana San Diego-Carlsbad-San Marcos, CA 3 Guangdong Chandigarh Appleton-Oshkosh-Neenah, WI 4 Tianjin Maharashtra Minneapolis-St. Paul-St. Cloud, MN- WI 5 Zhejiang Andhra Pradesh Boston-Worcester-Manchester, MA- NH 6 Fujian Karnataka Cincinnati-Middletown-Wilmington, OH-KY-IN 7 Jiangsu Goa Rochester-Batavia-Seneca Falls, NY 8 Liaoning Gujarat Austin-Round Rock, TX 9 Shandong Tamil Nadu Philadelphia-Camden-Vineland, PA- NJ-DE-MD 10 Hunan Pondicherry Albany-Schenectady-Amsterdam, NY 8

10 The territorial dimension of innovation (IV) China Huge concentration of innovation in main cities and coastal provinces 9

11 The territorial dimension of innovation (V) India Concentration around Delhi and Mumbai, with a significant presence of innovation in southern states (Karnataka, Tamil Nadu) 10

12 The territorial dimension of innovation (VI) US Concentration along the northeast and the west coast, but strong poles throughout the country (Minneapolis, Milwakee- Madison, Cincinnati, Austin) 11

13 The territorial dimension of innovation (VII) The level of concentration is reproduced across sectors e.g. biotech but also nanotech, ICT, pharmaceutic als 12

14 Conceptual Framework Source: Crescenzi and Rodriguez-Pose 2011

15 The model Modified Cobb-Douglass knowledge production function (KPF): I = i AK β i RD γ i SpillRD δ i C ζ i SpillC η i Where: 1. I is level of innovative output of region i, 2. A is constant, 3. K is the initial stock of knowledge available in the region i, 4. RD is the knowledge created in the region or regional technological activity, 5. SpillRD is a vector of neighbouring regions innovative efforts which may spill over into and contribute to the local production of innovative output, 6. C is a vector of local economic and socio-institutional characteristics, 7. SpillC is a vector of external economic and socio-institutional characteristics.

16 The empirical model y Modified regional knowledge production function (KPF): i, t = i + τ t + βr & Di, t + γwr & α D + δsf + ζwsf + ϑx + ε Where: 1. y is regional patent intensity, 2. R&D is the share of R&D expenditure in regional GDP, 3. SF is the social filter index, 4. WR&D and WSF are spatial lags of R&D and SF respectively, 5. X is a set of additional determinants of innovation i, t i, t i, t i, t i, t

17 The structure of the empirical model Variable Internal Factors External Factors R&D Local Investment in S&T/R&D Investment in S&T/R&D in neighbouring areas Social filter Specialisation Structural characteristics that would make a region more innovation prone, including: Human Capital Sectoral composition Use of resources (unemployment) Demographics Krugman Index Same characteristics in neighbouring areas Relative wealth Agglomeration economies Infrastructure endowment Mobility of people Fixed effects GDP per capita Population Density Kilometres (Kms) of motorways/railways Migration rate Region/Province-specific fixed effect + Time Trends

18 Dataset China Provinces, 4 Autonomous Regions, 4 Municipalities 2. 2 Special Administrative Regions (Hong Kong and Macau) and 1 Autonomous Region (Tibet) excluded India States and 3 Union Territories that will be covered by the analysis US 1. For BEA economic areas 2. Relevant regional markets surrounding metropolitan or micropolitan statistical areas 3. Redefined in Data for 179 areas, covering the whole of the US for all countries

19 Results: China Table 1a - China, Social Filter Index, No Province-specific trends, (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) VARIABLES PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc Regional R&D/S&T Expenditure * (0.131) (0.131) (0.133) (0.123) (0.123) (0.123) (0.124) (0.0898) (0.0601) (0.0701) (0.267) (0.0707) Spatially Weighted S&T (Inverse Dist) -4.13e e e e e e-08*** -7.98e-09*** -7.45e-09*** -7.84e-09*** (3.64e-09) (3.36e-09) (3.44e-09) (3.42e-09) (3.01e-09) (3.85e-09) (2.56e-09) (2.28e-09) (2.59e-09) Spatially Weighted S&T (First Order Contiguity) 2.37e e-10 (1.13e-09) (1.06e-09) Social Filter ** *** ** ** e ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Spatially Weighted Social Filter (Inverse Dist) ( ) Spatially Weighted Social Filter (First Order Contiguity) ** * *** *** ( ) ( ) ( ) ( ) ( ) ( ) Krugman Index *** *** *** *** ( ) ( ) ( ) ( ) Railway Density 0.183** 0.134** 0.141*** 0.134** (0.0725) (0.0604) (0.0496) (0.0615) Population Density *** *** *** *** (5.52e-05) (4.91e-05) (5.86e-05) (5.19e-05) Net Migration -1.81e e-05*** 4.57e-05*** 2.79e-05*** (2.24e-05) (1.04e-05) (1.06e-05) (1.07e-05) GDP Per Capita 8.27e-07*** (2.63e-07) Int.Term Exp.S&T*Pop.Density ** ( ) Stateowned industrial firms (% industrial firms) ( ) Constant e *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (0.0185) (0.0167) (0.0215) (0.0173) Year Dummies X X X X X X X X X X X X Observations R-squared Number of id Robust standard errors in parenthes *** p<0.01, ** p<0.05, * p<0.1

20 Results: India Table 2a - India, Social Filter Index, No State-specific trends, (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) VARIABLES PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc PCT_app_pc Regional R&D Expenditure 1.734* 1.832* 1.787* * 1.641* 1.505* * 1.545* * (0.968) (1.064) (0.952) (1.067) (0.963) (0.875) (0.806) (0.885) (0.774) (0.810) (0.321) (0.713) Spatially Weighted R&D (Inverse Dist) 2.14e e-09 (4.41e-09) (4.36e-09) Spatially Weighted R&D (First Order Contiguity) 2.37e-09** 2.20e-09** 2.04e-09** 1.71e-09* 1.75e-09* 1.04e e e e-09 (1.03e-09) (1.05e-09) (9.16e-10) (8.81e-10) (9.07e-10) (8.96e-10) (9.54e-10) (8.19e-10) (8.92e-10) Social Filter ** ** ** ** ** * ** (8.87e-05) (8.90e-05) ( ) ( ) ( ) ( ) ( ) (9.07e-05) ( ) Spatially Weighted Social Filter (Inverse Dist) ( ) Spatially Weighted Social Filter (First Order Contiguity) * * ( ) ( ) ( ) ( ) ( ) ( ) Krugman Index e e-05 ( ) ( ) ( ) ( ) Road Density -3.94e e-05** -3.69e-05** -1.86e-05 (8.34e-06) (2.12e-05) (1.75e-05) (2.68e-05) Population Density -3.56e e e e-06 (2.87e-06) (1.26e-06) (1.07e-06) (1.12e-06) GDP Per Capita -6.04e-08 (3.80e-08) Gross Migration (Inter-State) 1.75e-05*** 1.74e-05** 1.30e-05** 1.66e-05** (6.53e-06) (7.55e-06) (6.07e-06) (6.32e-06) Int.Term Exp.S&T*Pop.Density *** ( ) Religious Fractionalisation (0.0703) Linguistic Fractionalisation * ( ) Constant * ** ** ** ** * ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (0.0218) Year Dummies X X X X X X X X X X X X DelhiTrend X X X X X X X X X X X X Observations R-squared Number of id Robust standard errors in parenthes *** p<0.01, ** p<0.05, * p<0.1

21 Results: US VARIABLES Dependent Variable: Patent Intensity (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Regional R&D Expenditure 2769*** 2792*** 2757*** 2782*** 2745*** 2737*** 2787*** 2586*** 2441*** 2334*** (668.4) (672.0) (666.3) (671.5) (665.9) (661.4) (665.6) (580.0) (574.3) (527.2) (906.9) Spatially Weighted R&D (Inverse Dist) Spatially Weighted R&D (First Order Contiguity) Social Filter (1580) (1584) (1603) (1588) (1551) (1538) (1525) (1608) (454.8) (449.1) ** * ** * ** * Spatially Weighted Social Filter (Inverse Dist) *** Spatially Weighted Social Filter (First Order Contiguity) Krugman index ** * *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) *** *** * ( ) ( ) ( ) ( ) ( ) *** * (0.0239) (0.0225) (0.0224) Road Density 4.31e-05*** 3.29e-05** 3.20e-05** Population Density (1.58e-05) (1.42e-05) (1.36e-05) ** * ** * ** * ( ) ( ) ( ) Net Domestic Migration -1.50e-07** -8.07e e-08 (6.37e-08) (5.75e-08) (5.94e-08) GDP Per Capita 5.77e-06*** 5.30e-06*** 4.95e-06*** (9.60e-07) (9.24e-07) (8.11e-07) Int.Term Exp.R&D*Pop.Density 7.904* (4.509) Constant *** *** *** *** *** ** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) ( ) (0.0452) (0.0321) (0.0469) (0.0404) Year Dummies X X X X X X X X X X X Observations R-squared Number of BEA- Economic Areas

Results: China Pure agglomeration, NEGtype of story VARIABLES (9) (10) (11) (12) PCT_app_ PCT_app_ pc pc PCT_app_ pc PCT_app_ pc Regional R&D/S&T Expenditure -0.0533-0.0963 0.492* -0.0961 (0.0601) (0.

22 Results: China Pure agglomeration, NEGtype of story VARIABLES (9) (10) (11) (12) PCT_app_ PCT_app_ pc pc PCT_app_ pc PCT_app_ pc Regional R&D/S&T Expenditure * (0.0601) (0.0701) (0.267) (0.0707) Spatially Weighted S&T (Inverse Dist) -1.63e- 08*** -7.98e- 09*** -7.45e- 09*** -7.84e- 09*** (3.85e-09) (2.56e-09) (2.28e-09) (2.59e-09) Spatially Weighted S&T (First Order Contiguity) Social Filter 2.76e ( ) ( ) ( ) ( ) Spatially Weighted Social Filter (Inverse Dist) Spatially Weighted Social Filter (First Order Contiguity) * *** *** ( ) ( ) ( ) ( ) Krugman Index *** *** *** ( ) ( ) ( ) Railway Density 0.134** 0.141*** 0.134** (0.0604) (0.0496) (0.0615) Population Density *** *** *** *** (5.52e-05) (4.91e-05) (5.86e-05) (5.19e-05) Net Migration -1.81e e-05*** 4.57e-05*** 2.79e-05*** (2.24e-05) (1.04e-05) (1.06e-05) (1.07e-05) GDP Per Capita 8.27e-07*** (2.63e-07) Richer, agglomerated more-specialised regions with a good endowment of infrastructure more innovative

Results: China (II) VARIABLES (9) (10) (11) (12) PCT_app_ PCT_app_ pc pc PCT_app_ pc PCT_app_ pc Regional R&D/S&T Expenditure -0.0533-0.0963 0.492* -0.0961 (0.0601) (0.0701) (0.267) (0.

59e-09) Spatially Weighted S&T (First Order Contiguity) But the key variaibles of interest (R&D and social filter) are insignificant Social Filter 2.76e-06-0.000552-0.000377-0.000537 (0.000520) (0.

000742) (0.00100) (0.000766) Krugman Index 0.0204*** 0.0213*** 0.0205*** (0.00408) (0.00427) (0.00419) Railway Density 0.134** 0.141*** 0.134** (0.0604) (0.0496) (0.0615) Population Density 0.

23 Results: China (II) VARIABLES (9) (10) (11) (12) PCT_app_ PCT_app_ pc pc PCT_app_ pc PCT_app_ pc Regional R&D/S&T Expenditure * (0.0601) (0.0701) (0.267) (0.0707) Spatially Weighted S&T (Inverse Dist) -1.63e- 08*** -7.98e- 09*** -7.45e- 09*** -7.84e- 09*** (3.85e-09) (2.56e-09) (2.28e-09) (2.59e-09) Spatially Weighted S&T (First Order Contiguity) But the key variaibles of interest (R&D and social filter) are insignificant Social Filter 2.76e ( ) ( ) ( ) ( ) Spatially Weighted Social Filter (Inverse Dist) Spatially Weighted Social Filter (First Order Contiguity) * *** *** ( ) ( ) ( ) ( ) Krugman Index *** *** *** ( ) ( ) ( ) Railway Density 0.134** 0.141*** 0.134** (0.0604) (0.0496) (0.0615) Population Density *** *** *** *** (5.52e-05) (4.91e-05) (5.86e-05) (5.19e-05) Net Migration -1.81e e-05*** 4.57e-05*** 2.79e-05*** (2.24e-05) (1.04e-05) (1.06e-05) (1.07e-05) GDP Per Capita 8.27e-07*** (2.63e-07) Once these factors are controlled for, migration is also a force for innovation

Results: China (III) VARIABLES (9) (10) (11) (12) PCT_app_ PCT_app_ pc pc PCT_app_ pc PCT_app_ pc Regional R&D/S&T Expenditure -0.0533-0.0963 0.492* -0.0961 (0.0601) (0.0701) (0.267) (0.

24 Results: China (III) VARIABLES (9) (10) (11) (12) PCT_app_ PCT_app_ pc pc PCT_app_ pc PCT_app_ pc Regional R&D/S&T Expenditure * (0.0601) (0.0701) (0.267) (0.0707) Spatially Weighted S&T (Inverse Dist) -1.63e- 08*** -7.98e- 09*** -7.45e- 09*** -7.84e- 09*** (3.85e-09) (2.56e-09) (2.28e-09) (2.59e-09) Spatially Weighted S&T (First Order Contiguity) There are no spillovers. Core innovative regions are sucking resources from neighbouring ones Social Filter 2.76e ( ) ( ) ( ) ( ) Spatially Weighted Social Filter (Inverse Dist) Spatially Weighted Social Filter (First Order Contiguity) * *** *** ( ) ( ) ( ) ( ) Krugman Index *** *** *** ( ) ( ) ( ) Railway Density 0.134** 0.141*** 0.134** (0.0604) (0.0496) (0.0615) Population Density *** *** *** *** (5.52e-05) (4.91e-05) (5.86e-05) (5.19e-05) Net Migration -1.81e e-05*** 4.57e-05*** 2.79e-05*** (2.24e-05) (1.04e-05) (1.06e-05) (1.07e-05) GDP Per Capita 8.27e-07*** (2.63e-07)

Results: India A more traditional story (9) (10) (11) VARIABLES PCT_app_pc PCT_app_pc PCT_app_pc Regional R&D Expenditure 1.314* 1.545* 0.194 (0.774) (0.810) (0.

19e-10) Social Filter 0.000253** 0.000210* 0.000194** (0.000108) (0.000110) (9.

25 Results: India A more traditional story (9) (10) (11) VARIABLES PCT_app_pc PCT_app_pc PCT_app_pc Regional R&D Expenditure 1.314* 1.545* (0.774) (0.810) (0.321) Spatially Weighted R&D (Inverse Dist) Innovation is driven by investment in R&D Spatially Weighted R&D (First Order Contiguity) 1.04e e e-09 (8.96e-10) (9.54e-10) (8.19e-10) Social Filter ** * ** ( ) ( ) (9.07e-05) Spatially Weighted Social Filter (Inverse Dist) In states with adequate social filters Spatially Weighted Social Filter (First Order Contiguity) ( ) ( ) ( ) Krugman Index -8.15e ( ) ( ) Road Density -4.53e-05** -3.69e-05** (2.12e-05) (1.75e-05) Population Density -3.56e e e-08 GDP Per Capita (2.87e-06) (1.26e-06) (1.07e-06) -6.04e-08 (3.80e-08) Gross Migration (Inter-State) 1.75e-05*** 1.74e-05** 1.30e-05** (6.53e-06) (7.55e-06) (6.07e-06) Int.Term Exp.S&T*Pop.Density *** ( )

Results: India (II) Social filter is a real social filter (9) (10) (11) VARIABLES PCT_app_pc PCT_app_pc PCT_app_pc Regional R&D Expenditure 1.314* 1.545* 0.194 (0.774) (0.810) (0.

000194** (0.000108) (0.000110) (9.07e-05) Spatially Weighted Social Filter (Inverse Dist) Spatially Weighted Social Filter (First Order Contiguity) 0.000848 0.000694 0.000357 (0.000517) (0.

68e-08 GDP Per Capita (2.87e-06) (1.26e-06) (1.07e-06) -6.04e-08 (3.80e-08) Gross Migration (Inter-State) 1.75e-05*** 1.74e-05** 1.30e-05** (6.53e-06) (7.55e-06) (6.07e-06) Int.Term Exp.S&T*Pop.

26 Results: India (II) Social filter is a real social filter (9) (10) (11) VARIABLES PCT_app_pc PCT_app_pc PCT_app_pc Regional R&D Expenditure 1.314* 1.545* (0.774) (0.810) (0.321) Spatially Weighted R&D (Inverse Dist) Spatially Weighted R&D (First Order Contiguity) 1.04e e e-09 (8.96e-10) (9.54e-10) (8.19e-10) Social Filter ** * ** ( ) ( ) (9.07e-05) Spatially Weighted Social Filter (Inverse Dist) Spatially Weighted Social Filter (First Order Contiguity) ( ) ( ) ( ) Krugman Index -8.15e ( ) ( ) Road Density -4.53e-05** -3.69e-05** (2.12e-05) (1.75e-05) Population Density -3.56e e e-08 GDP Per Capita (2.87e-06) (1.26e-06) (1.07e-06) -6.04e-08 (3.80e-08) Gross Migration (Inter-State) 1.75e-05*** 1.74e-05** 1.30e-05** (6.53e-06) (7.55e-06) (6.07e-06) Int.Term Exp.S&T*Pop.Density *** ( ) R&D spillovers are positive and significant, especially when the social filter is decomposed into its key elements With migration reinforcing R&D and the social filter Interaction between R&D and density highly significant

Results: US Consistently strong association between R&D and patenting VARIABLES Dependent Variable: Patent Intensity (1) (2) (3) (4) (5) Regional R&D Expenditure 2769*** 2792*** 2757*** 2782***

27 Results: US Consistently strong association between R&D and patenting VARIABLES Dependent Variable: Patent Intensity (1) (2) (3) (4) (5) Regional R&D Expenditure 2769*** 2792*** 2757*** 2782*** 2745*** (668.4) (672.0) (666.3) (671.5) (665.9) Spatially Weighted R&D (Inverse Dist) (1580) (1584) Spatially Weighted R&D (First Order Contiguity) (454.8) (449.1) But no evidence of spillovers at play, regardless of the method used

Results: US (II) Social filter fundamental for innovation VARIABLES Dependent Variable: Patent Intensity (4) (5) (6) (7) (8) (9) (10) (11) Social Filter 0.0058 0.0056 0.0062 0.0082 0.0062 0.0073 0.

28 Results: US (II) Social filter fundamental for innovation VARIABLES Dependent Variable: Patent Intensity (4) (5) (6) (7) (8) (9) (10) (11) Social Filter *** 6*** 4*** 2*** 7*** 8*** 2*** 8*** Spatially Weighted Social Filter (Inverse Dist) Spatially Weighted Social Filter (First Order Contiguity) ( ) ( ) ( ) *** ( ) But contradictory evidence on spillovers. No possibility of free-riding ( ) *** ( ) ( ) *** ( ) ( ) * ( ) ( ) ( ) ( ) ( )

29 Other factors: US Specialisation (Krugman index): Weakly significant or insignificant Migration: Insignificant Density and wealth are positively and significantly associated with innovation Whereas the impact of mobility is somewhat limited

30 Conclusions No single model of geography of innovation 1. Knowledge production in the developed world is governed by different factors than in the emerging world 2. But there are significant differences between the EU and the US 3. As is also the case between China and India In China, regional innovation relies in agglomeration externalities that facilitate: 1. The generation of innovation 2. The absorption of innovation potential from other regions In India, innovation more linked to: 1. Adequate socio-economic context 2. Proximity to other innovative areas (capacity to assimilate and transform knowledge spillovers into innovation) In the US: 1. Strong reliance on R&D, social filters, and agglomeration economies 2. But very limited role for spillovers

31 Conclusions (II) Territorial innovation patterns are strongly place dependent Future innovation in any part of the world will depend on their geographical models of innovation The question is whether each model represents a sustainable model that will foster future evolutionary trajectories and economic outcomes 1. Both in aggregate terms and in terms of regional innovation 2. And consequently regional disparities and tensions

32 The territorial dynamics of innovation in a global world EU, US, China and India in comparison Andrés London School of Economics This and more working papers in

Do clusters generate greater innovation and growth?

Do clusters generate greater innovation and growth? Andrés Rodríguez-Pose Department of Geography and Environment London School of Economics and IMDEA, Social Sciences, Madrid IRIS Stavanger, 14 September