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Small Business Economics
Erschienen in: Small Business Economics 2/2010

01.02.2010

The role of clustering in the growth of new technology-based firms

verfasst von: Elicia M. Maine, Daniel M. Shapiro, Aidan R. Vining

Erschienen in: Small Business Economics | Ausgabe 2/2010

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Abstract

This study analyses the relationship between clusters and the growth performance of new U.S. technology-based firms. It is argued that firms benefit because clustering provides access to specialized resources that cannot be developed internally. The empirical results indicate that distance from a cluster is negatively related to growth, but clustering has a greater positive impact on biotech firms. Proximity to a cluster within a diverse metropolitan area is associated with superior growth performance only for firms that rely heavily on broad, downstream supply chain effects (that is, for information and communications technology firms).
Vorheriger Artikel The missing link: knowledge diffusion and entrepreneurship in endogenous growth
Nächster Artikel Process innovations and firm productivity growth

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Fußnoten
1
We define NTBFs as young and initially small firms operating in research and development (R&D)-intensive sectors.
 
2
Similarly, Owen-Smith and Powell (2004) have recently distinguished between “conduits” (market-based) and “channels” (spillovers).
 
3
Geroski (2005, p. 136) argues that Gibrat-type randomness is logically inconsistent with the existence of the firm-specific competencies associated with the resource-based view of the firm. By inference, results that reject Gibrat’s Law are at least logically consistent with the RBV.
 
4
As described more fully below, cluster effects are measured by the firm’s location in, or distance from, a relevant cluster. The economic diversity associated with that cluster is measured using two different metrics.
 
5
More precisely, the sign of the coefficient will depend on how we measure the cluster effect. As discussed below, we use two measures: proximity to a cluster (with an expected negative sign, as indicated in the text), and a dummy variable indicating location within a relevant cluster. In the latter case the coefficient is expected to be positive.
 
6
In fact several other distance measures were also calculated, but are not reported in this study because they do not alter the basic results. These include the average distance to all relevant top-10 clusters, and the average distance to various subsets of the top-10 (for example, average distance to the largest two clusters).
 
7
The data in Table 2 is recorded in natural logarithms, the form in which it used in estimation. The Great Circle Calculator program measures distances from the municipal hall of a chosen cluster or metropolitan area.
 
8
The Hachman Index is computed as Hi = 1/Σ(Eij/EUSj) × Eij, where Eij represents the share of employment in industry j in region i, and EUSj is the share of employment in industry j in the U.S.
 
9
For reasons of space we do not include all of the diversity terms. There are two diversity measures, and each applies to a corresponding measure of cluster distance (distance to largest, distance to nearest etc.). We present only the diversification measures relevant to the nearest cluster.
 
10
When we estimated a specification that included interactive terms for the two ICT sectors (but not the “other” category), there was significant multicollinearity and none of these terms was statistically significant, including those for biotech. When the medical devices dummy variable was omitted, leaving only the biopharmaceutical dummy, the effect on the latter is minimal.
 
11
We note as well that direct cluster effects are not observed when distance is measured from the nearest top-10 cluster and biotech interactive terms are included (Model 3), but the relevant coefficient exceeds unity. In addition, direct effects are found when the interactive terms are deleted (results not shown, but are available on request).
 
12
The HI is not corrected for size, but it is correlated with population (r = 0.69). The estimated coefficient for an interactive term (HACHMAN*POPULATION) was not statistically significant.
 
13
If the cluster distance is endogenous, then the interaction terms that involve it will also be endogenous. There are two interactive terms involving cluster distance, and we therefore require two additional exogenous instruments. For this purpose we used the interaction of the cluster distance rank index with both firm age and firm size.
 
Literatur
Acs, Z., Audretsch, D., & Feldman, M. (1992). Real effects of academic research. American Economic Review, 82(1), 363–367.
Acs, Z., Fitzroy, F., & Smith, I. (2002). High-technology employment and R&D in cities: Heterogeneity vs. specialization. Annals of Regional Science, 36(3), 373–386.CrossRef
Audretsch, D., & Feldman, M. (1996). R&D spillovers and the geography of innovation and production. American Economic Review, 86(3), 630–640.
Audretsch, D., & Feldman, M. (2003). Small-firm strategic research partnerships: The case of biotechnology. Technology Analysis and Strategic Management, 15(2), 273–288.CrossRef
Audretsch, D., & Lehman, E. E. (2005). Mansfield’s missing link: The impact of knowledge spillovers on firm growth. Journal of Technology Transfer, 30(1/2), 207–210.
Audretsch, D., & Stephan, P. (1999). Knowledge spillovers in biotechnology: Sources and incentives. Journal of Evolutionary Economics, 9(1), 97–107.CrossRef
Audretsch, D., Klomp, L., Santarelli, E., & Thurik, A. R. (2004). Gibrat’s law: Are services different? Review of Industrial Organization, 24, 301–324.CrossRef
Autant-Bernard, C., Mangematin, V., & Massard, N. (2006). Creation of biotech SMEs in France. Small Business Economics, 26(2), 173–187.CrossRef
Baptista, R., & Swann, G. M. (1999). A comparison of clustering dynamics in the US and UK computer industries. Journal of Evolutionary Economics, 9(3), 373–399.CrossRef
Bell, G. G. (2005). Clusters, networks, and firm innovativeness. Strategic Management Journal, 26(3), 287–295.CrossRef
Braunerhjelm, P., Carlsson, B., Cetindamar, D., & Johansson, D. (2000). The old and the new: The evolution of polymer and biomedical clusters in Ohio and Sweden. Journal of Evolutionary Economics, 10(5), 471–488.CrossRef
Breschi, S., & Lissoni, F. (2001). Knowledge spillovers and local innovations systems: A critical survey. Industrial and Corporate Change, 10(4), 975–1005.CrossRef
Chung, W., & Alcacer, J. (2002). Knowledge seeking and location choice of foreign direct investment in the United States. Management Science, 48(12), 1534–1554.CrossRef
Cohen, W. M., & Levinthal, D. A. (1990). Absorbtive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.CrossRef
Cubbin, J., & Stern, J. (2006). The impact of regulatory governance and privatization on electricity industry generation capacity in developing economies. World Bank Economic Review, 20(1), 115–141.CrossRef
Davidsson, P., Kirchhoff, B., Hatemi-J, A., & Gustavsson, H. (2002). Empirical analysis of business growth factors using Swedish data. Journal of Small Business Management, 40(4), 332–349.
Edwards, G. E., & Waverman, L. (2006). The effects of public ownership and regulatory independence on regulatory outcomes. Journal of Regulatory Economics, 29(1), 23–67.CrossRef
Eisenhardt, K., & Schoonhoven, C. (1990). Organizational growth: Linking founding team, strategy, environment, and growth among U.S. semiconductor ventures, 1978–1988. Administrative Science Quarterly, 35(3), 504–529.CrossRef
Evans, W., & Kessides, I. (1993). Localized market power in the US airline industry. Review of Economics and Statistics, 75(1), 66–73.CrossRef
Feldman, M. (2003). The Locational dynamics of the US biotech industry: Knowledge externalities and the anchor hypothesis. Industry and Innovation, 10(3), 311–328.CrossRef
Feldman, Francis, M. J., & Bercovitz, J. (2005). Creating a cluster while building a firm: Entrepreneurs and the formation of industrial Clusters. Regional Studies, 39(1), 129–141.
Geroski, P. A. (2005). Understanding the implications of empirical work on corporate growth rates. Managerial and Decision Economics, 26(2), 129–138.CrossRef
Gimeno, J., Hoskinsson, R. E., Beal, B. D., & Wan, W. P. (2005). Explaining the clustering of international expansion moves: A critical test in the U.S. telecommunications industry. Academy of Management Journal, 48(2), 297–319.
Glaeser, E., Kallal, H., Scheinkman, J., & Shleifer, A. (1992). Growth in cities. Journal of Political Economy, 100(6), 1126–1152.CrossRef
Globerman, S., Shapiro, D., & Vining, A. (2005). Clusters and intercluster spillovers: Their influence on the growth and survival of Canadian information technology firms. Industrial and Corporate Change, 14(1), 27–60.CrossRef
Hamilton, O., Shapiro, D., & Vining, A. (2002). The growth patterns of Canadian high-tech firms. International Journal of Technology Management, 24(4), 458–472.CrossRef
Helsely, R. W., & Strange, W. C. (2002). Innovation and input sharing. Journal of Urban Economics, 51(1), 22–45.
Jacobs, J. (1969). The economy of cities. New York: Vintage.
Kalnins, A., & Chung, W. (2004). Resource-seeking agglomeration: A study of market entry in the lodging industry. Strategic Management Journal, 25(7), 689–699.CrossRef
Keller, W. (2002). Geographic localization of international technology diffusion. American Economic Review, 92(1), 120–142.CrossRef
Klepper, S. (1996). Entry, exit, growth, and innovation over the product life cycle. American Economic Review, 86(3), 562–583.
Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499.CrossRef
Lemarie, S., Mangematin, V., & Torre, A. (2001). Is the creation and development of biotech smes localised? Conclusions drawn from the French case. Small Business Economics, 17(1–2), 61–76.CrossRef
Lotti, F., Santarelli, E., & Vivarelli, M. (2003). Does Gibrat’s law hold among Young, Small Firms? Journal of Evolutionary Economics, 13(3), 213–235.CrossRef
Marshall, A. (1920). Principles of economics. London: Macmillan.
Martin, R. L., & Sunley, P. (2003). Deconstructing clusters: Chaotic concept or policy Panacea? Journal of Economic Geography, 3(1), 5–35.CrossRef
Oerlemans, L. A. G., & Meeus, M. T. H. (2005). Do organizational and spatial proximity impact on firm performance? Regional Studies, 39(1), 89–104.CrossRef
Owen-Smith, J., & Powell, W. (2004). Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15(1), 5–21.CrossRef
Porter, M. (2000). Location, competition and economic development: Local clusters in a global economy. Economic Development Quarterly, 14(1), 15–34.CrossRef
Porter, M., & Stern, S. (2001). Innovation: Location matters. MIT Sloan Management Review, 42(4), 28–39.
Rosenthal, S., & Strange, W. (2005). The geography of entrepreneurship in the New York metropolitan area. FRBNY Economic Policy Review, December, 29–53.
Sadler-Smith, E., Hampson, Y., Chaton, I., & Badger, B. (2003). Managerial behavior, entrepreneurial style, and small business performance. Journal of Small Business Management, 41(1), 47–67.CrossRef
Shaver, J. M. (2006). Interpreting empirical findings. Journal of International Business Studies, 37, 451–452.CrossRef
Shaver, J. M., & Flyer, F. (2000). Agglomeration economies, firm heterogeneity, and foreign direct investment in the United States. Strategic Management Journal, 21(12), 1175–1193.CrossRef
Steinle, C., & Schiele, H. (2002). When do industries cluster? A proposal on how to assess an industry’s propensity to concentrate at a single region or nation. Research Policy, 31(6), 849–858.CrossRef
Storey, D. (1994). New firm growth and bank financing. Small Business Economics, 6(2), 139–151.CrossRef
Stuart, T., & Sorenson, O. (2003). The geography of opportunity: Spatial heterogeneity in founding rates and the performance of biotechnology firms. Research Policy, 32(2), 229–253.CrossRef
Sutton, J. (1997). Gibrat’s legacy. Journal of Economic Literature, 35(1), 40–59.
Swann, P., & Prevezer, M. (1996). A comparison of the dynamics of industrial clustering in computing and biotechnology. Research Policy, 25(7), 1139–1157.CrossRef
Tallman, S., Jenkins, M., Henry, N., & Pinch, S. (2004). Knowledge, clusters, and competitive advantage. Academy of Management Review, 29(2), 258–271.CrossRef
Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180.CrossRef
Zaheer, A., & George, V. (2004). Reach out or reach within? performance implications of alliances and location in biotechnology. Managerial and Decision Economics, 25(6–7), 437–452.CrossRef
Zucker, L. G., Darby, M. R., & Armstrong, J. (2002). Commercializing knowledge: University science, knowledge capture, and the performance of the US biotechnology firms. Management Science, 48(1), 138–153.CrossRef
Zucker, L. G., Darby, M. R., & Brewer, M. (1998). Intellectual human capital and the birth of U.S. biotechnology enterprises. American Economic Review, 88(1), 290–307.
Metadaten
Titel
The role of clustering in the growth of new technology-based firms
verfasst von
Elicia M. Maine
Daniel M. Shapiro
Aidan R. Vining
Publikationsdatum
01.02.2010
Verlag
Springer US
Erschienen in
Small Business Economics / Ausgabe 2/2010
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI
https://doi.org/10.1007/s11187-008-9104-3

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