Skip to main content

Advertisement

SpringerLink
Log in

The role of knowledge variety and intensity for regional innovation

  • Published:
Small Business Economics Aims and scope Submit manuscript

Abstract

This paper analyses the effect of variety and intensity of knowledge on the innovation of regions. Employing data for Swedish functional regions, the paper tests the role of the variety (related and unrelated) and intensity of (1) internal knowledge generated within the region and also (2) external knowledge networks flowing into the region in explaining regional innovation, as measured by patent applications. The empirical analysis provides robust evidence that both the variety and intensity of internal and external knowledge matter for regions’ innovation. When it comes to variety, related variety of knowledge plays a superior role.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. In this paper, “knowledge” refers to all three types of knowledge classified by Karlsson and Johansson (2006): scientific knowledge, technological knowledge, and entrepreneurial knowledge. Scientific knowledge has the character of a pure public good, although it is generally only available to those with the relevant scientific training. Technological and entrepreneurial knowledge are non-rivalrous and partially excludable goods, where the latter is often the result of learning-by-doing. All three types are argued to be patentable and there is indeed evidence on increasing propensity to patent all three types. Since the phenomenon under investigation in this paper is actually patent application, it seems plausible to include all of them as the conceptualization of knowledge.

  2. One could ask even a further question: given the importance of knowledge variety (diversity), how to enhance it in the regions/cities (Audretsch and Belitski 2013)?

  3. There are previous studies showing the positive effect of the concept of related industries on regional innovation measures (Feldman 1994; Feldman and Audretsch 1999; Ejermo 2005). Yet, this paper employs the entropy measure, which turns out to be an attractive measure, since it distinguishes between related and unrelated variety.

  4. One way to enhance the variety (heterogeneity) of knowledge in the region/city is recently suggested to be the existence of creative class and ethnical diversity, which may enhance knowledge spillover and entrepreneurial opportunity in the region (Audretsch and Belitski 2013).

  5. This has been recognized as one of the stylized fact in the geography of innovation (Feldman and Kogler 2010).

  6. It is preferable to use the EPO data rather than data from Swedish Patent Office, since in recent years the number of Swedish patent applications in EPO is increasing relative to Swedish Patent Office. Hence, it is assumed that EPO data can provide richer dataset in the study period of this paper.

  7. The unit of analysis in RKPF has been either region-technology (Jaffe 1989; Feldman and Florida 1994; Ponds et al. 2010) or region (ACS et al. 2002; Ejermo 2005; Fritsch and Slavtchev 2007). This paper chooses the later alternative, while controlling for industry heterogeneity across regions.

  8. It is worthy to note that, however, the patent data is a non-integer data, since the patent data is fractionalized (based on the number of inventors belonging to different functional regions). This could violate the usage of negative binomial regression, since this technique is designed for count data (integer data). To avoid this possible violence, the rounded value of fractionalized patent data is used in the regression. There are two groups of data that are in the risk of being under/overvalued after rounding: (1) the observations with the patent value between 0 and 0.5 and (2) with the value between 0.5 and 1. Nonetheless, the numbers of observation in former group is only ten and in the latter one is only eleven. More importantly, the result of binomial regression before and after rounding is quite similar.

  9. The mean value for patent application is 24 and the variance is 6,560.

  10. Since the mean and variance are not equal, therefore the equidispersion assumption is violated, which implies that the estimations based on Poisson and Zero-inflated Poisson models are not the preferred options.

  11. Even if there would be many zero value in the data, it does not necessarily mean that zero-inflated models can be the best option (Cameron and Trivedi 2008, p. 605), since it must be possible to distinguish between ‘true zeros’ and ‘excess zeros’ in order to be reasonable to use zero-inflated models. The mechanism for distinguishing these two types of zero is not clear in the patent application data, hence the use of zero-inflated models seems to be implausible. An example of the a situation where it is possible to distinguish between true zeros and excess zero is when a researcher wants to explain the amount of cigarettes smoked per day, while s/he has a survey containing both smokers (can cause true zeros) and non-smokers (causing excess zeros) (Cameron and Trivedi 2008, p. 584).

  12. Here ‘random effects’ apply to the distribution of the over-dispersion parameter (alpha), which is the same for all observations in the same group (here functional region) but varies randomly from group to group.

  13. Acs et al. (2002) compared the number of new products and patents across US regions and conclude; “the empirical evidence suggests that patents provide a fairly reliable measure of innovative activity” (p. 1080).

  14. It must be acknowledge that the use of patents as indicators for innovation is not undisputed. It is argued that not all patents are innovations and not all innovations are patented (Griliches 1990).

  15. The R&D investment refers to corporate R&D investment. In Swedish case it is documented that the corporate R&D (not university R&D) is the one that has the significant impact on innovation of the regions (Gråsjö 2006). Gråsjö (2006) used patent application as the measure of innovation of Swedish regions. He found significant impact of corporate R&D (but not university R&D) on innovation. The lack of significant impact of university R&D has been also found in explaining the Swedish export (Gråsjö 2006) as well as Swedish firm formation (Karlsson and Nyström 2011).

  16. In addition to Unrelated Variety, this paper initially considered the measured of the pure variety, which is a disentangled measure of variety in five-digit sectors within the regions (Boschma et al. 2012). Substituting the unrelated variety with pure variety measures reveals very similar results.

  17. High-Tech manufacturing sector is defined based on OECD classifications. It consists of following NACE codes: 2433, 30, 32, 33 and 353. Similar classification is used in other patent studies, for instance in Andersson and Lööf (2011).

  18. The value of LQ_HT is normalized by \({\text{LQ}}\_{\text{HT}} ^{\text{norm}} = \left( {{\text{LQ}}_{\text{HT}} - 1} \right)/\left( {{\text{LQ}}_{\text{HT}} + 1} \right)\). \(LQ\_HT ^{norm}\) is systematically distributed between −1 and +1 (Fritsch and Slavtchev 2007; Paci and Usai 1999). The same normalization is done for LQ_MAN.

  19. It should be acknowledge that one year may not be the best lagging option, as other studies uses 2 or 3 years lag (Fritsch and Slavtchev 2007; Ponds et al. 2010), although there are indeed one-year-lag studies, too (Crescenzi et al. 2012). However, the sample would have been substantially small if the lag was increased.

  20. The VIF test is performed after the conventional OLS regressions. There is no formal threshold for variance inflation factor test, but as a rule of thumb the VIF score below 10 (or sometimes 5) is said to be the evidence of quite mild multicollinearity.

  21. Using the Location Quotient instead of absolute number of High-Tech large manufacturing firms revealed the same result.

  22. LR test of restricted versus unrestricted models is not reported in Table 2 and is available upon request.

References

  • Acs, Z. J., Anselin, L., & Varga, A. (2002). Patents and innovation counts as measures of regional production of knowledge. Research Policy, 31(7), 1069–1083.

    Article  Google Scholar 

  • Acs, Z. J., Audretsch, D. B., & Feldman, M. P. (1994). R&D spillovers and recipient firm size. The Review of Economics and Statistics, 76(2), 336–340.

    Article  Google Scholar 

  • Andersson, M., & Ejermo, O. (2005). How does accessibility to knowledge sources affect the innovativeness of corporations? Evidence from Sweden. The Annals of Regional Science, 39, 741–765.

    Article  Google Scholar 

  • Andersson, M., & Karlsson, C. (2007). Knowledge in regional economic growth-the role of knowledge accessibility. Industry & Innovation, 14(2), 129–149.

    Article  Google Scholar 

  • Andersson, M., & Lööf, H. (2009). Learning‐by‐exporting revisited: The role of intensity and persistence. The Scandinavian Journal of Economics, 111(4), 893–916.

    Article  Google Scholar 

  • Andersson, M., & Lööf, H. (2011). Small business innovation: Firm level evidence from Sweden. Journal of Technology Transfer. doi:10.1007/s10961-011-9216-9.

  • Antonietti, R., & Cainelli, G. (2011). The role of spatial agglomeration in a structural model of innovation, productivity and export. Annals of Regional Science, 46(3), 577–600.

    Article  Google Scholar 

  • Asheim, B. T., Boschma, R., & Cooke, P. (2011). Constructing regional advantage: Platform policies based on related variety and differentiated knowledge bases. Regional Studies, 45(7), 893–904.

    Article  Google Scholar 

  • Audretsch, D. B., & Belitski, M. (2013). The missing pillar: The creativity theory of knowledge spillover entrepreneurship. Small Business Economics, 41, 819–836.

    Article  Google Scholar 

  • Audretsch, D. B., & Feldman, M. P. (1996). R&D spillovers and the geography of innovation and production. The American Economic Review, 86(3), 630–640.

    Google Scholar 

  • Audretsch, D. B., & Feldman, M. P. (2004). Knowledge spillovers and the geography of innovation. In J. V. Henderson & J. Thisse (Eds.), Handbook of urban and regional economics: Cities and geography (Vol. 4, pp. 2713–2739). Amsterdam: North Holland Publishing.

    Google Scholar 

  • Audretsch, D. B., & Vivarelli, M. (1996). Small firms and spillovers: Evidence from Italy. Small Business Economics, 8(3), 249–258.

    Article  Google Scholar 

  • Bathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: Local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28(1), 31–56.

    Article  Google Scholar 

  • Beaudry, C., & Schiffauerova, A. (2009). Who’s right, Marshall or Jacobs? The localization versus urbanization debate. Research Policy, 38(2), 318–337.

    Article  Google Scholar 

  • Berliant, M., & Fujita, M. (2011). The dynamics of knowledge diversity and economic growth. Southern Economic Journal, 77(4), 856–884.

    Article  Google Scholar 

  • Bettencourt, L. M., Lobo, J., & Strumsky, D. (2007). Invention in the city: Increasing returns to patenting as a scaling function of metropolitan size. Research Policy, 36, 107–120.

    Article  Google Scholar 

  • Boschma, R. A., & Fritsch, M. (2009). Creative class and regional growth: Empirical evidence from seven European countries. Economic Geography, 85(4), 391–423.

    Article  Google Scholar 

  • Boschma, R., & Iammarino, S. (2009). Related variety, trade linkages, and regional growth in Italy. Economic Geography, 85(3), 289–311.

    Article  Google Scholar 

  • Boschma, R., Minondo, A., & Navarro, M. (2012). Related variety and regional growth in Spain. Papers in Regional Science, 91(2), 241–256.

    Google Scholar 

  • Breschi, S. (1999). Spatial patterns of innovation. In A. Gambardella & F. Malerba (Eds.), The organisation of economic innovation in Europe (pp. 71–102). Cambridge: Cambridge University Press.

    Google Scholar 

  • Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69–87.

    Article  Google Scholar 

  • Cameron, C., & Trivedi, P. (2008). Applied Microeconometrics using STATA. New York: STATA Press.

    Google Scholar 

  • Castellani, D. (2002). Export behaviour and productivity growth: Evidence from Italian manufacturing firms. Review of World Economics, 138(4), 605–628.

    Google Scholar 

  • Clerides, S., Lach, S., & Tybout, J. (1998). Is learning by exporting important? Micro-dynamic evidence from Colombia, Mexico, and Morocco. The Quarterly Journal of Economics, 113(3), 903–947.

    Article  Google Scholar 

  • Crescenzi, R., Rodriguez-Pose, A., & Storper, M. (2012). The territorial dynamics of innovation in China and India. Journal of Economic Geography, 12, 1055–1085.

    Article  Google Scholar 

  • De Groot, H. L., Poot, J., & Smit, M. J. (2009). Agglomeration externalities, innovation and regional growth: Theoretical perspectives and meta-analysis. In R. Capello & P. Nijkamp (Eds.), Handbook of regional growth and development theories (pp. 256–281). Northampton MA: Edward Elgar Publishing.

    Google Scholar 

  • Duranton, G., & Puga, D. (2001). Nursery cities: Urban diversity, process innovation, and the life cycle of products. The American Economic Review, 9(5), 1454–1477.

    Article  Google Scholar 

  • Ejermo, O. (2005). Technological diversity and Jacobs’ externality hypothesis revisited. Growth and Change, 36(2), 167–195.

    Article  Google Scholar 

  • Essletzbichler, J. (2007). Diversity, stability and regional growth in the United States 1975–2002. In K. Frenken (Ed.), Applied evolutionary economics and economic geography (pp. 203–229). Cheltenham: Edward Edgar Publishing.

    Google Scholar 

  • Feldman, M. P. (1994). The geography of innovation. Dordrecht: Kluwer Academic.

    Book  Google Scholar 

  • Feldman, M. P. (2003). Location and innovation: The new economic geography of innovation, spillovers, and agglomeration. In G. L. Clark & M. P. Feldman (Eds.), The Oxford handbook of economic geography (pp. 373–394). Oxford: Oxford University Press.

    Google Scholar 

  • Feldman, M. P., & Audretsch, D. B. (1999). Innovation in cities: Science-based diversity, specialization and localized competition. European Economic Review, 43, 409–429.

    Article  Google Scholar 

  • Feldman, M. P., & Florida, R. (1994). The geographic sources of innovation: Technological infrastructure and product innovation in the United States. Annals of the Association of American Geographers, 84, 210–229.

    Article  Google Scholar 

  • Feldman, M. P., & Kogler, D. F. (2010). Stylized facts in the geography of innovation. In B. Hall & N. Rosenberg (Eds.), Handbook of economics of technical change (pp. 381–410). Oxford: Elsevier.

    Google Scholar 

  • Frenken, K., Van Oort, F., & Verburg, T. (2007). Related variety, unrelated variety and regional economic growth. Regional Studies, 41(5), 685–697.

    Article  Google Scholar 

  • Fritsch, M., & Slavtchev, V. (2007). Universities and innovation in space. Industry and Innovation, 14(2), 201–218.

    Article  Google Scholar 

  • Glaeser, E., Kallal, H., Scheinkam, J., & Shleifer, A. (1992). Growth in cities. The Journal of Political Economy, 100, 1126–1152.

    Article  Google Scholar 

  • Gordon, I. R., & Mccann, P. (2000). Industrial clusters, complexes, agglomeration and/or social networks? Urban Studies, 37, 513–532.

    Article  Google Scholar 

  • Grant, R. M. (1996). Prospering in dynamically-competitive environments: Organizational capability as knowledge integration. Organization Science, 7(4), 375–387.

    Article  Google Scholar 

  • Gråsjö U. (2006) Spatial spillovers of knowledge production—An accessibility approach. JIBS Dissertation series No. 034, Jönköping.

  • Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics, 10(1), 92–116.

    Article  Google Scholar 

  • Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.

    Google Scholar 

  • Hipp, C., & Grupp, H. (2005). Innovation in the service sector: The demand for service-specific innovation measurement concepts and typologies. Research Policy, 34, 517–535.

    Article  Google Scholar 

  • Jacobs, J. (1969). The economy of cities. New York: Vintage.

    Google Scholar 

  • Jaffe, A. B. (1989). Real effects of academic research. American Economic Review, 79, 957–970.

    Google Scholar 

  • Jaffe, A. B., & Trajtenberg, M. (2005). Patents, citations, and innovations: A window on the knowledge economy. Massachusetts: MIT Press Books.

    Google Scholar 

  • Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics, 108(3), 577–598.

    Article  Google Scholar 

  • Karlsson, C., & Johansson, B. (2006). Dynamic and entrepreneurship in a knowledge-based economy. In C. Karlsson, B. Johansson, & R. Stough (Eds.), Entrepreneurship and dynamics in the knowledge economy (pp. 12–46). New York: Routledge.

    Google Scholar 

  • Karlsson, C., & Nyström, K. (2011). Knowledge accessibility and new firm formation. In S. Desai, P. Nijkamp, & R. Stough (Eds.), New directions in regional economic development (pp. 174–198). Cheltenham: Edward Elgar Publishing.

    Google Scholar 

  • Keller, W. (2004). International technology diffusion. Journal of Economic Literature, 42(3), 752–782.

    Article  Google Scholar 

  • Krugman, P. R. (1991). Geography and trade. Cambridge: MIT Press.

    Google Scholar 

  • Lee, S. Y., Florida, R., & Gates, G. (2010). Innovation, human capital, and diversity. International Review of Public Administration, 14(3), 13–24.

    Article  Google Scholar 

  • Lucas, R. (1988). On the mechanics of economic growth. Journal of Monetary Economics, 22, 3–42.

    Article  Google Scholar 

  • Malerba, F., & Orsenigo, L. (1997). Technological regimes and sectoral patterns of innovative activities. Industrial and Corporate Change, 6(1), 83–117.

    Article  Google Scholar 

  • Maskell, P. (2001). Towards a knowledge-based theory of the geographical cluster. Industrial and Corporate Change, 10, 921–943.

    Article  Google Scholar 

  • Maskell, P., & Malmberg, A. (1999). Localised learning and industrial competitiveness. Cambridge Journal of Economics, 23(2), 167–186.

    Article  Google Scholar 

  • Maskell, P., & Malmberg, A. (2002). The elusive concept of localization economies: Towards a knowledge-based theory of spatial clustering. Environment and Planning A, 34, 429–449.

    Article  Google Scholar 

  • Mesquita, L. F. (2007). Starting over when the bickering never ends: Rebuilding aggregate trust among clustered firms through trust facilitators. Academy of Management Review, 32, 72–91.

    Article  Google Scholar 

  • Neffke, F., Henning, M., & Boschma, R. (2011). How do regions diversify over time? Industry relatedness and the development industry relatedness and the development. Economic Geography, 87(3), 237–265.

    Article  Google Scholar 

  • NUTEK. (2005). Årsbok. Stockholm: The Swedish Agency for Economic and Regional Growth.

    Google Scholar 

  • Nooteboom, B. (2000). Learning and innovation in organizations and economies. Oxford: Oxford University Press.

  • Paci, R., & Usai, S. (1999). Externalities, knowledge spillovers and the spatial distribution of innovation. GeoJournal, 49, 381–390.

    Article  Google Scholar 

  • Ponds, R., Van Oort, F., & Frenken, K. (2010). Innovation, spillovers and university–industry collaboration: An extended knowledge production function approach. Journal of Economic Geography, 10, 231–255.

    Article  Google Scholar 

  • Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge heterogeneity influences managerial performance and innovativeness. Strategic Management Journal, 25, 541–562.

    Article  Google Scholar 

  • Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94, 1002–1037.

    Article  Google Scholar 

  • Saviotti, P. P. (1996). Technological evolution, variety and the economy. Cheltenham: Edward Elgar Publishing.

    Google Scholar 

  • Saxenian, A. (1996). Regional advantage: Culture and competition in silicon valley and route 128. Cambridge: Harvard University Press.

  • Scherer, F. M. (1983). The propensity to patent. International Journal of Industrial Organization, 1, 107–128.

    Article  Google Scholar 

  • Schumpeter, J. (1934). The theory of economic development. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Tallman, S., Jenkins, M., Henry, N., & Pinch, S. (2004). Knowledge, clusters and competitive advantage. Academy of Management Review, 29, 258–271.

    Google Scholar 

  • Utterback, J. M., & Abernathy, W. J. (1975). A dynamic model of process and product innovation. Omega, 3(6), 639–656.

    Article  Google Scholar 

  • Vernon, R. (1966). International investment and international trade in the product cycle. The Quarterly Journal of Economics, 2, 190–207.

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to Olof Ejermo who provides the patent data. Valuable comments by Koen Frenken, Martin Andersson, Charlie Karlsson, Maryann Feldman, Viroj Jienwatcharamongkhol, three anonymous referees, and journal editor are greatly appreciated. This project is performed in collaboration with Centre for Innovation, Research and Competence in the Learning Economy (CIRCLE), Lund University.

Author information

Authors and Affiliations

  1. Department of Industrial Economics, Blekinge Institute of Technology (BTH), 371 79, Karlskrona, Sweden

    Sam Tavassoli

  2. Department of Mechanical and Management Engineering, Politecnico di Bari, Viale Japigia, Bari, Italy

    Nunzia Carbonara

Authors
  1. Sam Tavassoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nunzia Carbonara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sam Tavassoli.

Appendices

Appendix 1

See Fig. 2.

Fig. 2
figure 2

Map of Sweden divided to 81 functional regions (local labour market). Source: NUTEK (2005)

Full size image

Appendix 2

See Table 3.

Table 3 Variable definitions and measures
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tavassoli, S., Carbonara, N. The role of knowledge variety and intensity for regional innovation. Small Bus Econ 43, 493–509 (2014). https://doi.org/10.1007/s11187-014-9547-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11187-014-9547-7

Keywords

JEL Classifications

Search

Navigation