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University specialization and new firm creation across industries

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Abstract

This article examines how the scientific specialization of universities impacts new firm creation across industries at the local level. In accordance with the Pavitt-Miozzo-Soete taxonomy, we consider eight industry categories, which reflect the characteristics of firms’ innovation patterns and, ultimately, the knowledge inputs that firms require. Using data on new firm creation in Italian provinces (i.e., at the NUTS3 level), we estimate negative binomial regression models separately for each industry category to relate new firm creation to the scientific specialization in basic sciences, applied sciences and engineering, and social sciences and humanities of neighboring universities. We find that universities specialized in applied sciences and engineering have a broad positive effect on new firm creation in a given province, this effect being especially strong in service industries. Conversely, the positive effect of university specialization in basic sciences is confined to new firm creation in science-based manufacturing industries, even if this effect is of large magnitude. Universities specialized in social sciences and humanities have no effect on new firm creation at the local level whatever industry category is considered.

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Notes

  1. Following Knight’s (1921) distinction between uncertainty and risk, Alvarez and Barney (2005) argue that while under risk a probability distribution of the values of economic variables can be calculated, no such calculation is possible under uncertainty. Confronted with uncertainty, incumbent firms then suffer from organizational inertia, leaving room for newly created firms to capture the entrepreneurial opportunities generated out of innovative ideas.

  2. For sake of clarity, Miozzo and Soete (2001) identify the industry category “science-based and specialized suppliers.” To avoid confusion with the industry categories SB and SS in the manufacturing industries, here we prefer to use the terminology used by Castellacci (2008) and Castaldi (2009), which refers to the KIBS category.

  3. Rao (2001) highlights how the emergence of the stand-alone software industry contributed greatly to undermining the in-house R&D activity of telecommunication service firms (see also Miozzo and Ramirez 2003). Similarly, the financial industry experienced a significant rise in information system outsourcing in the early 1990s (see, e.g., Altinkemer et al. 1994; Palvia 1995).

  4. See Appendix Tables 6 and 7 for the list of industries included.

  5. Information engineering includes computer sciences.

  6. http://www.tagliacarne.it.

  7. Since we do not consider agriculture in the industry taxonomy, we did not take into account the academic staff that is specialized in agricultural sciences.

  8. Data on the latitude and longitude of each province were extracted from ISTAT databases to calculate distances among provinces. Then, we calculated, by means of a GIS program, the Euclidean distance (in km) between the centroids of each province.

  9. In the estimation of the negative binomial regression model for each industry category, we also cluster data at the NUTS2 level. This approach should account for possible spatial autocorrelation in our data (for a similar approach, see Baptista and Mendonça 2010). In Sect. 4.2, we provide an additional check to evaluate whether our results are biased because of spatial autocorrelation. In particular, the results of the spatial autoregressive model that we run on a transformation of our dependent variable are very close to those of the negative binomial regression model.

  10. To evaluate the appropriateness of the negative binomial regression model against the Poisson model, we performed a likelihood ratio test, under the null hypothesis that the over-dispersion coefficient is zero. Furthermore, as the dependent variable assumes value zero in some industry categories in some provinces, we also report the Vuong test to evaluate the appropriateness of the negative binomial model against the zero inflated negative binomial model. Results of the tests are reported at the bottom of Table 5 (see Sect. 4.3) and confirm the appropriateness of the negative binomial regression model.

  11. For the sake of synthesis, the correlation matrix is reported in the Appendix (Table A3). We also performed a variance inflation factor (VIF) analysis, which suggests that multicollinearity is not a problem in our estimates. Indeed, in all industry categories the mean VIF is below the 5 threshold, while the maximum VIF is below the threshold of 10 (Belsley et al. 1980).

  12. Both Bologna and Paris claimed the honor of being the first university in Europe; each began in the second half of the twelfth century. The universities of Padua, Naples, Siena, Rome, and Perugia followed between 1222 and 1308. After a pause, a second wave occurred between 1343 and 1445, with the establishment of Pisa, Florence, Pavia, Ferrara, Turin and Catania. After another century-long pause, a third wave of late Renaissance foundations created the universities of Macerata, Salerno, Messina and Parma between 1540 and 1601 (Grendler 2002; p. 1). See also De Ridder-Symoens (1992) and Rashdall (2010 ).

  13. For a discussion on the mechanisms through which large incumbent firms create barriers to entry, see, Caves and Porter (1977).

  14. The percentage of new firms in the SD category with respect to the total number of new firms is indeed 5.38 %, while the percentage of exits in the SD category with respect to the total number of exits in all industries is 8.96 %.

  15. As a robustness check, we run additional regressions by distinguishing the university specialization in basic, applied and social sciences (i.e., we excluded humanities). Results are similar to those reported in Table 5, confirming that the effect of universities specialized in social sciences on new firm creation at the local level is not significant. Results are available from the authors upon request.

  16. The IRR is the ratio at which the dependent variable increases (or decreases) for a one unit increase in the explanatory variable, while holding all other variables in the model constant.

  17. We also estimated spatial error models (SEM) in the eight industry categories using the same dependent variables. SEM models take into account spatial autocorrelation by including in the error term an additional component that is spatially dependent. Results are qualitatively similar to those obtained through the SAR models and are available from the authors upon request.

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Acknowledgments

The financial support of Regione Toscana Project LILIT: I Living Labs per l’Industria Toscana (PAR FAS REGIONE TOSCANA Linea di Azione 1.1.a.3) is kindly acknowledged.

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Authors and Affiliations

  1. National Agency for the Evaluation of Universities and Research Institutes, Pisa, Italy

    Andrea Bonaccorsi

  2. DIG, Politecnico di Milano, Milan, Italy

    Massimo G. Colombo & Cristina Rossi-Lamastra

  3. DESTEC, University of Pisa, Pisa, Italy

    Massimiliano Guerini

Authors
  1. Andrea Bonaccorsi
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  2. Massimo G. Colombo
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  3. Massimiliano Guerini
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  4. Cristina Rossi-Lamastra
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Correspondence to Massimiliano Guerini.

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Andrea Bonaccorsi is on leave from DESTEC, University of Pisa.

Appendix

Appendix

1.1 Industry categories

See Tables 6 and 7.

Table 6 Manufacturing industries according to the Pavitt (1984) taxonomy
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Table 7 Services industries according to the Miozzo and Soete (2001) taxonomy
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1.2 Correlation matrix

See Table 8.

Table 8 Correlation matrix
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1.3 Robustness checks

See Tables 9, 10 and 11.

Table 9 The impact of university presence and specialization on new firm creation in different industry categories with control for university size
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Table 10 The impact of university presence and specialization on new firm creation in different industry categories with the sum of new firms created in 2009, 2010 and 2011 as dependent variable
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Table 11 SAR models on the impact of university presence and specialization on new firm creation in different industry categories
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Bonaccorsi, A., Colombo, M.G., Guerini, M. et al. University specialization and new firm creation across industries. Small Bus Econ 41, 837–863 (2013). https://doi.org/10.1007/s11187-013-9509-5

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