Skip to main content
SpringerLink
Log in

Innovation In an Integrated Framework: A Europe-United States Comparative Analysis

  • Chapter
  • First Online:
Innovation and Regional Growth in the European Union

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

  • 911 Accesses

Abstract

In the first part of this book we have shown how different streams of literature – the linear model, the systems of innovation approach and the geographical analysis of the diffusion of knowledge spillovers – can be effectively combined into an “integrated” analytical framework, providing us with a more complex and perhaps realistic view on the territorial determinants of innovation and economic growth. This chapter is aimed, on the one hand, at further developing the “integrated framework” discussed so far by explicitly including into the picture specialisation and agglomeration processes and, on the other hand, at using this framework as a “common ground” to compare the drivers of innovation (and their geography) in Europe and in the United States.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    “A patent is a member of the triadic patent families if and only if it has been applied for and filed at the European Patent Office (EPO), at the Japanese Patent Office (JPO) and if it has been granted by the US Patent and Trademark Office (USPTO)” (Eurostat 2006: 6). Patent families are supposed to improve international comparability by suppressing the home advantage.

  2. 2.

    € 8,049.5 in the EU-25 and € 8,422.6 in the Euro Zone vs. € 20,487 in the US, measured in PPS, based on full-time equivalents.

  3. 3.

    When the ranking is extended to the top 100 universities we find that 57 are in the USA and 35 in the EU (of which 11 in the UK). The ranking of the top 500 universities in the world is based upon a variety of performance indicators (see http://ed.sjtu.edu.cn/rank/2005/ARWU%202005.pdf for further details).

  4. 4.

    Though the effects of the 1982 patenting system reform are debated (see Jaffe and Lerner 2004).

  5. 5.

    Duranton and Puga (2003) use as an example a model “in which agglomeration facilitates the matching between firms and inputs. These inputs may be labelled workers, intermediates, or ideas. Depending on the label chosen, a matching model of urban agglomeration economies could be presented as a formalisation of either one of Marshall’s three basic sources of agglomeration economies even though it only captures a single mechanism” (p. 2).

  6. 6.

    Zimmermann (2005) points out that the EU shows “a split labour market that is characterized by high levels of unemployment for low-skilled people and a simultaneous shortage of skilled workers. This lack of flexible high-skilled workers and the aging process has created the image of an immobile labour force and the eurosclerosis phenomenon (thus preventing) the best allocation of resources and hence economic efficiency” (p. 448).

  7. 7.

    For a review of the theoretical and empirical works based on this approach and for a discussion of its limitations see Wieser 2005.

  8. 8.

    The majority of patents issued by the USPTO are utility (i.e., invention) patents. Other types of patents and patent documents issued by USPTO, but not included in this report, are plant patents, design patents, statutory invention registration documents, and defensive publications. While in 1999 the number of utility patents granted reached 153,493, just 14,732 design patents, 448 reissue, and 421 plant patents were awarded. Our data do not include these other categories.

  9. 9.

    The USPTO provides data at the sub-state level on utility patents granted from 1990 to 1999 with a first-named inventor who resided in the United States. For the EU, instead, patents are organized by EUROSTAT according to the application years rather than the grant years. However, the US patent data at the national level show that the numbers of patent applications and patents granted are highly correlated over time (0.94 for the period 1989–2002) and across geographical units (0.98 for 1990).

  10. 10.

    As the time distance-matrix is calculated either at the NUTS1 or at the NUTS2 level, in order to make it coherent with our data which combine different NUTS levels we relied on the NUTS distance matrix using the NUTS 2 regions with the highest population density in order to represent the corresponding NUTS1 level for Belgium, Germany, and the UK.

  11. 11.

    The distance matrix does not take into account the impact of railway and/or air connections on the average trip length. Only road travel-time is available for the EU regions.

  12. 12.

    Data on distances between MSAs are calculated on the assumption that a 1 degree difference in latitude is constant regardless of the latitude being examined. This assumption is not problematic for smaller countries, but for a large country like the US, it may result in an underestimation of the distance between Southern cities and an overestimation of that between Northern cities.

  13. 13.

    The 1990 census classification was developed from the 1987 Standard Industrial Classification (SIC) Manual published by the Office of Management and Budget Executive Office of the President.

  14. 14.

    The first category includes people whose highest level of schooling is an associate degree (for example: AA, AS) or some college credit, but no degree. The second group includes those whose highest level of schooling is a bachelor’s degree (for example: BA, AB, BS), a master’s degree (for example: MA, MS, MEng, MEd, MSW, MBA); or a professional degree (for example: MD, DDS, DVM, LLB, JD) (US Census Bureau).

  15. 15.

    Unemployment is “hidden” in the fabric of very small farm holdings in many EU peripheral areas and in many Southern states of the US (Demissie 1990; Caselli and Coleman 2001). In both these contexts agricultural workers show low levels of formal education, scarce mobility, and tend to be aged.

  16. 16.

    We are aware of the potential endogeneity arising from the introduction of the social filter variable into the Knowledge Production Function. An effective strategy in order to address this issue would imply the use of several time lags of the social filter variables as instruments in an instrumental variables framework. However, due to the constraints on data availability discussed before, we are forced to limit ourselves to considering the value of this indicator at the beginning of the period of analysis, while assessing the patent growth rate over subsequent years.

  17. 17.

    Migration data are provided by Eurostat in the “Migration Statistics” collection. However there are no data for Spain and Greece. Consequently, in order to obtain a consistent measure across the various countries included in the analysis, we calculate this variable from demographic statistics. “Data on net migration can be retrieved as the population change plus deaths minus births. The net migration data retrieved in this way also includes external migration” (Puhani 2001: 9). The net migration was standardised by the average population, obtaining the net migration rate. Consequently, while for the EU it is impossible to distinguish between national, intra-EU, and extra-EU migration flows, for the US domestic in-migration and out-migration data consist of moves where both the origins and destinations are within the United States.

  18. 18.

    Different estimation techniques have been considered in order to minimize potential bias due to omitted variables (panel data) and/or modifiable aerial unit problem (e.g., Hierarchical Linear Models, HLM). However, severe constraints in terms of both the spatial scale and the time-series dimension of the existing regional data prevent us from implementing these alternative methodologies. Further research in this direction remains in our agenda, given the continuous progress in the availability of regional statistics.

  19. 19.

    The MSA/CMSA list is based on Metropolitan Areas and Components, 1993, with FIPS Codes, published by the Office of Management and Budget (1993).

  20. 20.

    Standard & Poor’s Compustat North America is a database of financial, statistical, and market information covering publicly traded companies in the U.S. and Canada. It provides more than 340 annual and 120 quarterly income statements, balance sheets, flows of funds, and supplemental data items on more than 10,000 active and 9,700 inactive companies.

  21. 21.

    The concept of FURs has been defined as a means to minimize the bias introduced by commuting patterns. A FUR includes a core city, where employment is concentrated, and its hinterland, from which people commute to the center. For a detailed analysis of this concept see Cheshire and Hay (1989).

  22. 22.

    The 145 MSAs for which R&D data are available account for 89,9% of the GDP generated in all 266 MSAs and show an average of 225.19 patents per million inhabitant against 176.83 for the whole sample.

  23. 23.

    Beeson et al. (2001) discuss the “sample selection bias” introduced when choosing cities as unit of analysis rather then county-level data: only places that experienced successful growth in the past are considered in this way. The use of standard metropolitan statistical areas minimises this first bias. However, in order to keep the bias at a minimum, we not only report the results for the most innovative subsample of MSAs, but also for all MSAs in the continental US.

  24. 24.

    When assessing this phenomenon it must, however, be borne in mind that the unit of analysis in the case of the EU are NUTS regions i.e., territorial units for the production of regional statistics for the European Union whose definition mainly serves administrative purposes. As a consequence, NUTS regions might not always approximate the functional borders of the regional economy. Conversely, US MSAs are closer to the concept of ‘functional urban regions’ (Cheshire and Hay, 1989) and likely to be more “self-contained” in terms of economic interactions. Consequently, part of the difference between the empirical evidence recorded in the two cases may be due to the different nature of the spatial unit of analysis. However, since we rely on inverse linear distance (and not on contiguity) for the specification of our spillovers variable, the impact of heterogeneous spatial units in Europe and the US should be minimized supporting our results that are, in any case, largely in line with the existing literature. As a robustness check we have re-estimated our empirical model for the US, using state-level data (contiguous) with very similar results for the spillover variables. These results are available from the authors on request.

  25. 25.

    This is consistent with the notion that because mobility is higher in the US, innovation systems have more local matching and learning and hence are more “local” than in Europe, where long distance communication is necessary in order to match relatively immobile agents.

  26. 26.

    The impact of the sectoral structure upon regional innovative performance cannot be limited to the overall degree of specialisation. On the contrary, it would be necessary to fully control for the specific regional patterns of specialisation: given the aggregate degree of regional specialisation, the true differential factor could stem from a region being specialised in high tech R&D-intensive vs traditional sectors (Smith 2007). Further investigation, in an EU-US comparative perspective, of the sectoral level territorial processes remains in our agenda for future research.

References

  • Acs ZJ (2002) Innovation and growth in cities. Edward Elgar, Northampton, MA

    Google Scholar 

  • Ács Z, Audretsch DB (1989) Patents as a measure of innovative activities. Kyklos 42:171–181

    Article  Google Scholar 

  • Andersson R, Quigley JM, Wilhehnsson M (2005) Agglomeration and the spatial distribution of creativity. Papers in Regional Science, 84: 445–464

    Google Scholar 

  • Anselin L, Varga A, Acs Z (1997) Local geographic spillovers between University research and high technology innovations. J Urban Econ 42:422–448

    Article  Google Scholar 

  • Audretsch DB (2003) Innovation and spatial externalities. Int Reg Sci Rev 26(2):167–174

    Article  Google Scholar 

  • Audretsch DB, Feldman MP (1996a) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640

    Google Scholar 

  • Batty M (2003) The geography of scientific citation. Environ Plann 35(5):761–765

    Article  Google Scholar 

  • Beeson PE, DeJong DN, Troesken W (2001) Population growth in U.S. counties, 1840–1990. Reg Sci Urban Econ 31:669–699

    Article  Google Scholar 

  • Borrás S (2004) System of innovation theory and the European Union. Sci Public Pol 31(6):425–433

    Article  Google Scholar 

  • Bottazzi L, Peri G (2003) Innovation and spillovers in regions: evidence from European patent data. Eur Econ Rev 47:687–710

    Article  Google Scholar 

  • Carlino G, Chatterjee S, Hunt R (2001) Knowledge spillovers and the new economy of cities, Working Paper n. 01–14. Federal Reserve Bank of Philadelphia, Mimeo

    Google Scholar 

  • Caselli C, Coleman J (2001) The US structural transformation and regional convergence: a reinterpretation. J Pol Econ 109(3):584–616

    Article  Google Scholar 

  • Charlot S, Duranton G (2006) Cities and workplace communication: some quantitative French evidence. Urban Stud 43:1365–1394

    Article  Google Scholar 

  • Cheshire PC, Hay DG (1989) Urban problems in Western Europe: an economic analysis. Unwin Hyman, London

    Google Scholar 

  • Ciccone A (2000) Agglomeration effects in Europe. Eur Econ Rev 46:213–227

    Article  Google Scholar 

  • Ciccone A, Hall RE (1996) Productivity and the density of economic activity. Am Econ Rev 86(1):54–70

    Google Scholar 

  • Ciccone A (2002) Agglomeration effects in Europe. European Economic Review, 46: 213–227

    Google Scholar 

  • Cliff A, Ord JK (1972) Testing for spatial autocorrelation among regression residuals. Geogr Anal 4:267–284

    Article  Google Scholar 

  • Crescenzi R (2005) Innovation and regional growth in the enlarged Europe: the role of local innovative capabilities, peripherality and education. Growth Change 36:471–507

    Article  Google Scholar 

  • Criscuolo P, Verspagen B (2006) Does it matter where patent citations come from? Inventor versus examiner citations in European patents, ECIS Working Papers 05.06

    Google Scholar 

  • De Blasio G (2006) Production or consumption? Disentangling the skill-agglomeration connection, Bank of Italy. Tema di discussione n. 571

    Google Scholar 

  • Delmas MA (2002) Innovating against European rigidities. Institutional environment and dynamic capabilities. J High Technol Manage Res 13:19–43

    Article  Google Scholar 

  • Demissie E (1990) Small-scale agriculture in America. Westview, San Francisco

    Google Scholar 

  • Desmet K, Fafchmps M (2005) Changes in the spatial concentration of employment across US counties: a sectoral analysis 1972–2000. J Econ Geogr 5(3):261–284

    Article  Google Scholar 

  • Dosi G, Llerena P, Sylos LM (2006) The relationships between science, technologies and their industrial exploitation: an illustration through the myths and realities of the so-called ‘European Paradox’. Res Policy 35(10):1450–1464

    Article  Google Scholar 

  • Drennan M, Lobo J (2007) Specialization matters: the knowledge economy and United States cities. Los Angeles: UCLA School of Public Affairs, unpublished manuscript

    Google Scholar 

  • Duranton J, Puga D (2003) Micro-foundation of urban agglomeration economies. In: Henderson VJ, Thisse JF (eds) Handbook of regional and urban economics Vol. 4 cities and geography. Elsevier, Amsterdam

    Google Scholar 

  • Duranton G and Puga D (2001) Nursery cities: Urban diversity, process innovation, and the life cycle of products. American Economic Review, 91: 1454–1477

    Google Scholar 

  • Ergas H (1987) Does technology policy matter? In: Guile B, Brooks H (eds) Technology and global industry. National Academy Press, Washington, pp 191–245

    Google Scholar 

  • European Commission (2005a) Integrated guidelines for growth and jobs 2005–2008. COM(2005) 141 final2005/0057 (CNS)

    Google Scholar 

  • European Commission (2005b) Towards a European research area: science, technology and innovation, key figures 2005. Office for Official Publications of the European Communities, Luxembourg

    Google Scholar 

  • European Commission (2007a) Commission staff working document accompanying the green paper “The European Research Area: New Perspectives” COM(2007)161, Brussels

    Google Scholar 

  • EUROSTAT (2006a) Population statistics. Official Publication of EC, Luxemburg

    Google Scholar 

  • EUROSTAT (2006b) Patent applications to the EPO at national level. Statistics in focus n.3

    Google Scholar 

  • Fagerberg J, Verspagen B, Caniels M (1997) Technology, growth and unemployment across European regions. Reg Stud 31(5):457–466

    Article  Google Scholar 

  • Feldman M (1994) The geography of innovation. Kluwer, Boston

    Google Scholar 

  • Feldman M, Audretsch DB (1999) Innovation in cities: science-based diversity, specialisation and localised competition. Eur Econ Rev 43(2):409–429

    Article  Google Scholar 

  • Fischer MM, Varga A (2003) Spatial knowledge spillovers and university research. Ann Reg Sci 37:303–322

    Article  Google Scholar 

  • Fritsch M (2002) Measuring the quality of regional innovation systems: a knowledge production function approach. Int Reg Sci Rev 25(1):86–101

    Article  Google Scholar 

  • Fujita M, Thisse J-F (2002) Economics of agglomeration. University Press, Cambridge

    Google Scholar 

  • Gambardella A, Malerba F (1999) The organization of innovative activity in Europe: toward a conceptual framework. In: Gambardella A, Malerba F (eds) The organization of economic innovation in Europe. Cambridge University Press, Cambridge, pp 1–2

    Google Scholar 

  • Glaeser E, Kallal H, Scheinkman J, Schleifer A (1992) Growth in cities. J Polit Econ 100(6):1126–1152

    Article  Google Scholar 

  • Gordon IR (2001) Unemployment and spatial labour markets: strong adjustment and persistent concentration. In: Martin R, Morrison P (eds) Geographies of labour market inequality. Routledge, London

    Google Scholar 

  • Gregersen B, Johnson B (1996) Learning economies, innovation systems and European integration. Reg Stud 31:479–490

    Article  Google Scholar 

  • Greunz L (2003) Geographically and technologically mediated knowledge spillovers between European regions. Ann Reg Sci 37:657–680

    Article  Google Scholar 

  • Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10(1):92–116

    Article  Google Scholar 

  • Griliches Z (1986) Productivity, R&D, and basic research at the firm level in the 1970s. Am Econ Rev 76:141–154

    Google Scholar 

  • Hart DM (2001) Antitrust and technological innovation in the US: ideas, institutions, decisions, and impacts, 1890–2000. Res Policy 30:923–936

    Article  Google Scholar 

  • Henderson JV (1999) Marshall’s economies National Bureau of Economic Research. Working Paper 7358

    Google Scholar 

  • Henderson JV (2003) Marshall’s scale economies. Journal of Urban Economics, 53: 1–28

    Google Scholar 

  • Institute for Higher Education (2006) Academic Ranking of World Universities – 2006, Shanghai Jiao Tong University (http://ed.sjtu.edu.cn/rank/2005/ARWU%202005.pdf)

  • IRPUD (2000) European peripherality indicators (E.P.I). IRPUD GIS database. Institute of Spatial Planning, Dortmund

    Google Scholar 

  • Jaffe AB (1986) Technological opportunity and spillovers of R&D: Evidence from firms’ patents, profits and market share. Am Econ Rev 76:984–1001

    Google Scholar 

  • Jaffe AB, Lerner J (2004) Innovation and its discontents. Princeton University Press, Princeton

    Google Scholar 

  • Lundvall BÅ (1992) National systems of innovation: towards a theory of innovation and interactive learning. Pinter, London

    Google Scholar 

  • Maggioni MA, Nosvelli M, Uberti E (2006) Space vs networks in the goegraphy of innovation: a European analysis, Working paper 2006.153, Fondazione Eni Enrico Mattei

    Google Scholar 

  • Malecki E (1997) Technology and economic development: the dynamics of local, regional and national competitiveness, 2nd edn. Addison Wesley Longman, London

    Google Scholar 

  • Mariani M (2002) Next to production or to technological clusters? The economics and management of R&D location. J Manage Governance 6:131–152

    Article  Google Scholar 

  • Midelfart-Knarvik H, Overman HG (2002) Delocation and European integration: is structural spending justified? Econ Pol 17(35):322–359

    Google Scholar 

  • Moreno R, Paci R, Usai S (2005a) Spatial spillovers and innovation activity in European regions. Environ Plann 37:1793–1812

    Article  Google Scholar 

  • Moreno R, Paci R, Usai S (2005b) Geographical and sectoral clusters of innovation in Europe. Ann Reg Sci 39(4):715–739

    Article  Google Scholar 

  • Morgan K (1997) The learning region: institutions, innovation and regional renewal. Reg Stud 31:491–503

    Article  Google Scholar 

  • Mowery DC (1998) The changing structure of the US National Innovation System: implications for International Conflict and Cooperation in R&D Policy. Res Pol 27(6):639–654

    Article  Google Scholar 

  • NTSC National Science and Technology Council (1999) Annual report 1998. The White House, Washington

    Google Scholar 

  • OECD (2001) Using patent counts for cross-country comparisons of technology output. STI Rev 27:129–146

    Google Scholar 

  • OECD (2006) Compendium of patent statistics. OECD, Paris

    Google Scholar 

  • Oõhuallachain B, Leslie TF (2007) Rethinking the regional knowledge production function. J Econ Geogr 7:737–752

    Article  Google Scholar 

  • Ottaviano G, Peri G (2006) The economic value of cultural diversity: evidence from US cities. J Econ Geogr 6(1):9–44

    Article  Google Scholar 

  • Peri G (2005) Skills and talent of immigrants: a comparison between the European Union and the United States. Institute of European Studies, UC, Berkeley Mimeo

    Google Scholar 

  • Puhani AP (2001) Labour mobility – an adjustment mechanism in Euroland? Empirical evidence for Western Germany, France, and Italy. Ger Econ Rev 2(2):127–140

    Article  Google Scholar 

  • Rodríguez-Pose A (1999) Innovation prone and innovation averse societies. Economic performance in Europe. Growth Change 30:75–105

    Article  Google Scholar 

  • Rodríguez-Pose A, Crescenzi R (2008) R&D, spillovers, innovation systems and the genesis of regional growth in Europe. Reg Stud 42(1):51–67

    Article  Google Scholar 

  • Sedgley N, Elmslie B (2004) The geographic concentration of knowledge: scale, agglomeration and congestion in innovation acress US states. Int Reg Sci Rev 27(2):111–137

    Article  Google Scholar 

  • Smith K (2007) Does Europe perform too little corporate R&D? In: Paper presented at the DRUID Summer Conference 2007. Copenhagen CBS, Denmark

    Google Scholar 

  • Sonn JW, Storper M (2008) The increasing importance of geographical proximity in technological innovation: an analysis of US patent citations, 1975–1997. Environ Plann A 40(5):1020–1039

    Article  Google Scholar 

  • Stein JA (2004) Is there a European knowledge system? Sci Public Policy 31(6):435–447

    Article  Google Scholar 

  • Storper M, Venables AJ (2004) Buzz: face-to-face contact and the urban economy. J Econ Geogr 4:351–370

    Google Scholar 

  • Vandamme F (2000) Labour mobility within the European union: findings, stakes and prospects. Int Labour Rev 139(4):437–455

    Article  Google Scholar 

  • Varga A (1998) University research and regional innovation. Kluwer, Boston

    Google Scholar 

  • Varga A (2000) Local academic knowledge spillovers and the concentration of economic activity. J Reg Sci 40:289–309

    Article  Google Scholar 

  • Wieser R (2005) Research and development productivity and spillovers: empirical evidence at the firm level. J Econ Surv 19(4):587–621

    Article  Google Scholar 

  • Wooldridge JM (2002) Econometric analysis of cross section and panel data. MIT, Cambridge (MA), USA

    Google Scholar 

  • Wooldridge JM (2003) Cluster-sample methods in applied econometrics. American Economic Review, 93: 133–138

    Google Scholar 

  • Zimmermann K (1995) Tackling the European migration problem. J Econ Perspect 9:45–62

    Google Scholar 

  • Zimmermann K (2005) European labour mobility: challenges and potentials. De Economist 127(4):425–450

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Geography and Environment, London School of Economics, Houghton Street, WC2A 2AE, London, UK

    Riccardo Crescenzi & Andrés Rodríguez-Pose

  2. IMDEA Social Sciences, Madrid, Spain

    Andrés Rodríguez-Pose

Authors
  1. Riccardo Crescenzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrés Rodríguez-Pose
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Riccardo Crescenzi .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Crescenzi, R., Rodríguez-Pose, A. (2011). Innovation In an Integrated Framework: A Europe-United States Comparative Analysis. In: Innovation and Regional Growth in the European Union. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17761-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17761-3_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17760-6

  • Online ISBN: 978-3-642-17761-3

  • eBook Packages: Business and EconomicsEconomics and Finance (R0)

Publish with us

Policies and ethics

Search

Navigation