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

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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).

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Notes

  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.

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Acknowledgements

The authors gratefully acknowledge the SSHRC (Canada) for financial support. The authors also thank Clayton Mitchell for research assistance and Tom Lawrence and three anonymous referees for helpful comments.

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  1. Segal Graduate School of Business, Simon Fraser University, Vancouver, BC, Canada, V6B 5K3

    Elicia M. Maine, Daniel M. Shapiro & Aidan R. Vining

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  1. Elicia M. Maine
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Correspondence to Daniel M. Shapiro.

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Maine, E.M., Shapiro, D.M. & Vining, A.R. The role of clustering in the growth of new technology-based firms. Small Bus Econ 34, 127–146 (2010). https://doi.org/10.1007/s11187-008-9104-3

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