Abstract
A large number of studies have demonstrated that proximity effects from knowledge spillovers, network externalities and other forms of knowledge transfers among like firms are geographically bounded. However, only a few studies have measured the strength and geographic scope of such externalities and even fewer have done so for firms in very close proximity. In this study, we examine the size and geographic scope of proximity effects among all life science firms that have received Small Business Innovation Research (SBIR) grants in the US over a 23-year period while controlling for relevant regional and firm characteristics. From our empirical analysis, we conclude that proximity effects among nearby small life science firms are strong within one-tenth of a mile distance and are exhausted within a radius of 1.5 miles. By examining the location of all firms in the sample, we offer possible explanations for the narrow geographic scope of the measured proximity effects. We also explain the significance of such findings for academic research that seeks to understand the nature of spatial externalities and for public policy.
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Notes
There is also a Phase 3 but federal agencies do not provide funds during that phase.
Aharonson et al. (2007) studied the firm location choice of biotechnology firms conditional on the existence of knowledge spillovers, which are approximated as the level of R&D spending; Rosenthal and Strange (2003) also studied firm location choice and operationalize knowledge spillovers as the level of employment in a given industry. Hence, empirical evidence from these studies does not specifically pertain to SBIR firms.
The benefits from the agglomeration of firms and industries discussed in the literature are, generally, of two kinds. The first involves efficiency gains from reductions in firm production costs through access to local labor pools and services as well as reductions in transaction costs through improved access to local suppliers and buyers. The second involves gains in firm knowledge and innovation that typically result from knowledge spillovers, network effects, increased collaboration among proximate firms and other forms of knowledge transfer. Because these latter effects rely heavily on human capital (e.g., labor mobility, face-to-face interactions), the associated benefits are generally assumed to be more geographically limited (termed proximity effects here).
Black (2005) also studied the probability for a given firm to win Phase 2 SBIR awards but his analysis did not include proximity effects across similar firms.
The life sciences industry is also characterized by a large number of university and other life science firm spinoffs created in close proximity to the original sources of knowledge. This is a firm creation mechanism described in the knowledge spillover theory of entrepreneurship and refers to the process where firms are founded in order to exploit knowledge that is not fully developed at the institution that first produced it (Audretsch and Keilbach 2007). Accordingly, our analysis could also provide insights into the proximity effects and the geographic scope of knowledge spillover entrepreneurship.
Relatedly, Funderburg and Boarnet (2008) report that the majority of the labor force of a given cluster is located within 5–7.5 miles from the cluster.
It is possible that knowledge specific to the SBIR program itself but not related to the life sciences could also result in an increase in the level of SBIR funding of LSFs. In such a case, proximity to SBIR winners from other industries could also contribute to the funding performance of LSFs receiving SBIR grants. We consider this possibility in our empirical analysis and report relevant results in Appendix Table 4.
We use a 5 year lag because we expect the effects of the knowledge transfer mechanisms to be stronger during that period. However, given the relative lack of theoretical guidance from the extant literature (albeit this lack Acs et al. 2009; and Baum et al. 2000 have, among others, also used 5 year windows) we performed a robustness check and tried shorter and longer lag periods. The results of this robustness check revealed that our estimates are not sensitive to the choice of the lag structure and are not reported here for parsimony.
It should be noted that, in addition to measuring the association of the sum of SBIR funds raised by a given LSF with the density of SBIR winners located in different spatial rings, we also considered its association with the sum of funds raised by the proximate SBIR winners. Initial empirical results from the two alternative approaches were qualitatively similar, and we opted for using the density of SBIR firms for two reasons. First, it allows for a direct comparison with Wallsten (2001) who also considered firm density in his study. Second, Phase 1 grants are typically of similar magnitude across firms and as a result the amount of funds from SBIR grants accumulated from LSFs in a given radius was found to be highly correlated with the corresponding measure of SBIR firm density in the relevant spatial rings.
As with SBIR LSFs, we initially specified variables that measured potential spatial externalities with VCFs at larger distances from the origin firm, but we pared down the specification of our variables to those presented above as we could not find statistically significant impacts in larger distances. We also examined proximity effects with VCFs and SBIR LSFs through an alternative specification. Because many locations do not host a large number of VCFs and because the potential impact of VCFs may go beyond immediate proximity, following previous literature (i.e. Samila and Sorenson 2011), we also measured the density of VCFs at the MSA level. We discuss the empirical results of these alternative specifications in the next section.
As we explain in the next section, the empirical assessment of such potential proximity effects is hampered by the fact that, while we can locate the non-SBIR LSFs in space, we cannot assess whether they never applied for an SBIR grant or they were unsuccessful in sourcing SBIR grants. This is important, as underperforming firms may have a negative proximity effect on the origin SBIR LSF. Beaudry and Breschi (2003) document the potential of negative effects arising from collocation with underperforming firms. As a result, we include the particular variables in the analysis to account for the potential knowledge transfer but, as we explain below, we interpret the results with caution.
Examples of private organizations that offer consulting services on securing SBIR grants include Foresight S&T in Rhode Island and the Larta Institute in California and the District of Columbia.
Note that, in addition to experience, the PreviousSBIR variable may capture unobserved qualities and characteristics of the LSF that make it successful in acquiring SBIR funds and as such the relevant empirical results should be interpreted carefully.
It should be noted that there is potential for simultaneity of the patent variable with the dependent variable because we report the total number of patents over a range of years, and some of the patents may have resulted from SBIR grants. Unfortunately, differential and often unobserved lags in the dates of discovery, patent submission, and patent issuance make proper allocation of the patents by year exceedingly difficult, and as a result we opt for including a total patent count as an indicator of the innovative character of each firm. For this reason, the relevant empirical results should be treated with caution.
We, however, followed the codification scheme described in Table 2 because the number of employees is typically reported by firms in discrete categories.
The biopharmaceutical keywords list was constructed after consulting with biotechnology researchers employed at the authors’ institution. The list included the following terms: Allergen, Antibodies, Antigen, Ascites, Biomedicine, Cancer, Cardiovascular, Cartilage, Central Nervous System, Chinese Hamster Ovary, Cho cells, Collagen, Dermal, Endocrine, Gene therapy, Genetic disorders, Growth hormone, Immune suppression, Immunodeficiency, Infectious disease, Insulin, Ligament, Lymphoma, Magnetic resonance imaging (MRI), Monoclonal antibodies, Myocardial infarction, Oncogene, Pharmacokinetics, Polyclonal antibodies, Polyvalent vaccine, Renal, Respiratory.
One issue that has plagued previous research is how to correctly identify the location of relevant organizations. For example, often only the address of the corporate headquarters or main university campus may be recorded in datasets, while the locations of other facilities are not reported. We cope with such potential issues in our dataset in the following ways. For SBIR LSFs and non-SBIR LSFs, proper identification of location is relatively straightforward because these firms are typically small and they only have one location. For the small number of firms with a relatively large number of employees, we visited the website, and for a handful of firms with multiple locations, we recorded all such locations. For the venture capital firms active in the life sciences sector, we visited their websites in all occasions, and we used their multiple locations in the very few cases that multiple locations existed. In the case of the universities used in our analysis, we received data from AUTM on campus locations with active research life sciences programs, which we complemented with data available from NIH on all university locations that received NIH funding over the period of analysis. We further used Google Earth® and visited the website of each institution to ensure that we could identify the proper locations of the universities and associated medical schools in our sample. While it is still possible that some locations of relevant organizations might have been overlooked, our focus on the life sciences has allowed us to minimize such potential shortcoming.
Walcott (2002) provides a detailed case study of the growth of the San Diego bioscience cluster with insightful references on geographical patterns.
We used the zip code instead of the units used in the empirical analysis as the unit of presentation in the map, because it is the smallest spatial unit for which symbols do not overlap to the degree of making the map prohibitively difficult to assess visually.
Approximately one-third of the LSFs in the sample are repeated winners and are mostly larger firms.
GEE is a method to estimate the standard errors which first estimates the variability within the defined cluster and then sums across all clusters (Zorn 2006).
The size and reach of research collaborations and networks has increased over time as communication costs have declined. These types of effects have also been mentioned in the literature (Johnson and Lybecker 2012) and such changes could have changed the geographic scope of the proximity effects. As a robustness check to the sensitivity of our results against such considerations, we constructed two alternative empirical specifications: (1) one where a time trend was added to our base model (to represent ongoing reductions in communication costs) and where the trend was interacted with the independent variables that measure the density of SBIR winners in close proximity to the origin firm (to test for changes in the geographic scope of proximity effects); (2) another where our base model was modified to allow the above-mentioned density coefficients to differ over two selected sub-period spanning the 1983–2006 period of analysis. The results were generally similar but, because of the limited number of observations in the early years of the sample, and the increase in the size of the SBIR program in 1994, the inclusion of a time trend in our model conflicted with the 1994 dummy variable and generally caused significant multicollinearity that raised the condition index well above the generally accepted threshold of 30 and rendered inference from such results problematic. The empirical results from the second specification were generally invariant to the choice of sub-periods. We have reported the empirical results for one of such models in the Appendix Table 5. All the empirical models we estimated, including the one reported here, did not support any shift over time in the geographic scope of the proximity effects in our sample.
Note, for instance, that clustering of two firm cohorts in side by side office parks or business incubators of typical size would tend to imply that firms within the cohabitating cohorts would, typically, be less than 0.2 miles apart from each other while firms between cohorts would be located 1–1.5 miles apart. These types of patterns will tend to influence all empirical measures of proximity effects and must be explicitly taken into account.
It is possible that there may be additional proximity effects between the origin LSF and firms from industries other than the life sciences which have received SBIR grants. The performance of the origin LSF may improve from proximity with non-LSF SBIR winners if knowledge specific to acquiring SBIR grants is useful, yielding positive coefficients. To capture any such potential proximity effect, we include in X tt of Eq. (1) a variable that measures the number of non-LSF SBIR winners (NON_LSF_SBIR) that are located in the same zip code as the origin LSF. We examine proximity effects with non-LSF SBIR winners at the zip code rather than through a sequential ring specification due to the very large number of non-LSF SBIR winners which makes the ring specification practically intractable. For instance, in our empirical analysis, construction of a variable that would measure the density of non-LSF SBIRs in a single ring over a 23-year period would require 556,021,550 calculations (14,450 non-LSF-SBIR firms × 1,673 LSFs × 23 years). We present the empirical results of this specification in Appendix Table 4 and we find that while the remaining of the parameter estimates are largely unchanged, the estimated coefficient of NON_LSF_SBIR is negative. As a result, we do not find empirical support that proximity of LSFs to firms from industries other than the life sciences which have won SBIR grants could improve their funding performance.
As we explain in footnote 13, we also built models where the density of VCFs is measured at the MSA level. Because the density of VCFs at the MSA level and the count of research universities at the same level were highly correlated (correlation coefficient 0.78) we could only use one of the two variables at a time as the multicollinearity index of the model where both variables were included raised significant inference concerns. When the density of VCFs in the MSA replaces the density of research universities in the MSA, the empirical estimates suggest that the presence of variable of interest is statistically significant and positive, but it has a very small effect on the amount of SBIR funds raised by a given LSF. The rest of the empirical results remain robust as discussed in the base model in Table 3.
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Research funding provided by the Ewing Marion Kauffman Foundation Strategic Grant #20050176 is gratefully acknowledged.
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Kolympiris, C., Kalaitzandonakes, N. Geographic scope of proximity effects among small life sciences firms. Small Bus Econ 40, 1059–1086 (2013). https://doi.org/10.1007/s11187-012-9441-0
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DOI: https://doi.org/10.1007/s11187-012-9441-0