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Regional public research, higher education, and innovative start-ups: an empirical investigation

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Abstract

Based on detailed information about the regional knowledge base, particularly about universities, we find that regional public research and education have a strong positive impact on new business formation in innovative industries but not in industries classified as non-innovative. Measures for the presence and size of public academic institutions have more of an effect on the formation of innovative new businesses than indicators that reflect the quality of these institutions. We find relatively weak evidence for interregional spillovers of these effects. Our results clearly demonstrate the importance of localized knowledge and, especially, of public research for the emergence of innovative new businesses.

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Notes

  1. Earlier studies for Germany are Harhoff (1999), Bade and Nerlinger (2000), Audretsch and Lehmann (2005), Audretsch et al. (2005), and Hülsbeck and Pickavé (2012).

  2. Harhoff’s (1999) analysis is limited to start-ups in electrical machinery and the mechanical engineering industry. Audretsch and Lehmann (2005) and Audretsch et al. (2005) focus on 281 firms that made an initial public offering (IPO) in Germany between March 1997 and March 2002. Since these firms may have been set up considerably in advance of making an IPO, their founding date is only vaguely defined.

  3. We also include information about the non-university research institutions in the region, which are neglected in Audretsch and Lehmann (2005), Audretsch et al. (2005), and Hülsbeck and Pickavé (2012).

  4. For the researcher, starting an own business is often the only way to have an idea realized. For a more detailed discussion of this point, see Audretsch et al. (2006) and Acs et al. (2009).

  5. This corresponds to the observation that founders are likely to set up their business in the industry in which they previously worked (Fritsch and Falck 2007).

  6. Since many service firms do not have a standardized product program but provide customer-specific services, they are not innovative in the same sense as manufacturing firms. Hence, service industries that may be relevant for innovation are defined as such based on the knowledge intensity of their inputs. These knowledge-intensive service industries include, for example, “computer services,” “research and development in natural sciences and engineering,” and “business consultancy.” For definitions of these groups of industries, see Grupp and Legler (2000) and OECD (2005). For a review of different methods of identifying innovative businesses, see Fritsch (2011).

  7. Audretsch and Lehmann (2005), Bade and Nerlinger (2000), Harhoff (1999), Hülsbeck and Pickavé (2012), Lasch et al. (2013).

  8. The reason for these mixed results may be high correlation among the different indicators (see Sect. 4).

  9. A study of the USA by Bania et al. (1993) shows that there may also be considerable differences in the effect of different regional knowledge sources among four-digit industries that are classified as highly innovative.

  10. An indication for different effects of size and quality-related university indicators is provided by Fritsch and Slavtchev (2007), who find that only the volume of external funds has a positive effect on regional innovation activity; no such positive effect can be found for indicators that are related to size, such as the number of professors and academic personnel or the number of students and of graduates.

  11. See Fritsch (2011) for the classification of German industries as “innovative,” “technologically advanced,” or “technology-intensive services.”

  12. We account for all institutes of the four large public research organizations in Germany, i.e., the Fraunhofer, the Helmholtz, the Leibnitz, and the Max Planck Society. Data have been collected from different sources, mainly from publications of these organizations and the Federal Ministry of Education and Research. Since a number of these institutes have several locations, the publicly available information about their budgets and number of personnel cannot be meaningfully assigned to regions.

  13. If a patent has more than one inventor, the count is divided by the number of the inventors involved and each inventor is registered with his or her share of that patent.

  14. This common classification of German regions by the Federal Office for Building and Regional Planning is based on a region’s population density and settlement structure. For details, see Federal Office for Building and Regional Planning (2003).

  15. The highest number of HEIs can be found in Berlin (34 HEIs), followed by Munich (19), Hamburg (17), and Stuttgart (10). The regions with the highest number of non-university institutions for public research are Berlin (26), Munich (20), and Dresden (17).

  16. A plausible assumption for the selection of “true” zero values could be that the emergence of an innovative start-up in a region requires the presence of at least one university or of a non-university public research institute. This assumption, however, is not unproblematic because it already implies the general hypothesis that innovative start-ups emerge from public research. Running a zero-inflated negative binomial model with the variable “presence of a university or non-university public research institute in the region” for the selection of the “true” zero values, we find that a Vuong test suggests that doing so is not a significant improvement over a standard negative binomial model.

  17. A great deal of the financing and legal framework for universities and non-university public research institutes is the responsibility of the Federal States in Germany. Most of the Federal States also operate their own programs for promoting entrepreneurship.

  18. The AIC is a measure of the relative goodness of fit of a statistical model that accounts for the number of independent variables included in the model. For details, see Akaike (1974) and Greene (2008).

  19. Employment in industry groups and small-firm employment are entered in the regressions as shares in overall regional employment because including these numbers would lead to double counting with the overall number of employees and cause multicollinearity.

  20. We do not distinguish between patents registered by HEIs, non-university public research institutes, or the private sector for several reasons. One reason is that universities and other public research institutes in Germany are to different degrees selective with respect to patenting inventions so the number of patents is not a meaningful indicator of innovative output. A second reason is a change in the legal framework for university patenting that led to considerable change in patenting behavior during the period of analysis (for details, see Proff et al. 2012).

  21. The correlation coefficient between the aggregate indicator for the regional HEIs (the number of non-university public research institutes) and the number of private-sector R&D employees is 0.465 (0.596); see Table 7 in the Appendix. The correlation between the regional number of private-sector R&D employees and the aggregate indicator for HEIs (the number of non-university public research institutes) in adjacent regions is 0.323 (−0.021).

  22. The results are available as online material for this article.

  23. The coefficient of correlation between these two indicators in the overall sample is 0.488 (see Table 7 in the Appendix).

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Authors and Affiliations

  1. School of Economics and Business Administration, Friedrich Schiller University Jena, Carl-Zeiss-Str. 3, 07743, Jena, Germany

    Michael Fritsch & Ronney Aamoucke

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  1. Michael Fritsch
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  2. Ronney Aamoucke
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Correspondence to Michael Fritsch.

Additional information

We have benefited from comments on earlier versions of this paper by participants of several workshops and conferences. Special thanks for suggestions go to Guido Buenstorf, Donato Iacobucci, Haifeng Qian, Colin Wren, Michael Wyrwich, and an anonymous referee.

Appendix

Appendix

See Tables 6, 7, 8, 9, and 10.

Table 6 Descriptive statistics for the relevant variables
Full size table
Table 7 Correlations for the variables in the baseline model
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Table 8 Descriptive statistics for the indicators for universities and other public research institutes
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Table 9 Correlations among different indicators for universities and other pubic research institutes in the region
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Table 10 The factor representing regional universities—factor loadings and unique variances after varimax rotation
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Fritsch, M., Aamoucke, R. Regional public research, higher education, and innovative start-ups: an empirical investigation. Small Bus Econ 41, 865–885 (2013). https://doi.org/10.1007/s11187-013-9510-z

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