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University knowledge and the creation of innovative start-ups: an analysis of the Italian case

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Abstract

Grounding on the Knowledge Spillover Theory of Entrepreneurship, this paper advances research on innovative start-ups by studying whether and how university knowledge fosters the creation of these firms at the local level. First, we contend that geographical proximity shapes the impact of university knowledge on the creation of innovative start-ups in a geographical area. In other words, in this context, university knowledge spillovers are highly localized. Second, we argue that the availability in an area of individuals with open-minded attitudes (regional openness) lessens the localized nature of university knowledge spillovers, favouring the exploitation of geographically distant university knowledge for the creation of innovative start-ups. Results from estimations of zero-inflated negative binomial regressions on a sample of 1188 province–industry pairs confirm our conjectures.

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Notes

  1. The authors include in their analysis German firms in: (1) high-technology manufacturing industries, devoting more than 8.5 % of their input to R&D; (2) technologically advanced manufacturing industries (R&D intensity between 3.5 and 8.5 %); and (3) technology-oriented services, covering only some selected service industries related to innovation and new technologies.

  2. The Nomenclature of territorial units for statistics (NUTS) classification is a hierarchical system for partitioning the European economic territory into sub-territories (regions, counties, and so on). The current NUTS classification lists 97 regions at NUTS-1, 270 regions at NUTS-2 and 1294 regions at NUTS-3 level. Further information is available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction.

  3. In 2011, the Italian Government re-organized the four provinces of the Sardinia region into eight new provinces. However, in several cases the statistical sources of data that we use in the present study provide information on the Sardinia region by referring to the old classification based on four provinces. Because of these data constraints, we have therefore excluded the provinces of Sardinia from our analysis.

  4. For the list of high-tech industries as defined by Eurostat see http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:High-tech.

  5. The following criteria define research-active universities: (1) the existence of institutionally recognized research units; (2) the existence of an official research mandate; (3) the presence of regular PhD programs; (4) the inclusion of research in the strategic planning; and (5) the regular provision of funds for research activities from public agencies as well as from private institutions. For further information, see http://ec.europa.eu/research/era/docs/en/eumida-final-report.pdf.

  6. See the “Appendix” for Table 5 that shows the link between the innovative start-ups’ industries and university disciplinary areas.

  7. Before computing the average, we standardize the value of each component in line with Florida and Tinagli (2005).

  8. Our findings are, however, robust to other estimation techniques. Specifically, results do not change when using negative binomial, Poisson and Tobit models (in the Tobit model, the dependent variable is the logarithm of one plus the number of innovative start-ups in the province/industry pair and the left-censoring limit is zero).

  9. The average ME is the average increase in the number of innovative start-ups in the province/industry due to a one standard deviation increase in the variable of interest (\({\text{LOCAL}}\_{\text{UNIKNOW}}_{i,j }\)), while the average SE is the average percentage increase of the dependent variable due to the same variation of \({\text{LOCAL}}\_{\text{UNIKNOW}}_{i,j }\).

  10. At the end of 2014, the Italian unemployment rate among individuals who are less than 25 years old was 42.7, while the corresponding figure in Europe was 21.9. Data available in the Eurostat website: http://ec.europa.eu/eurostat/web/lfs/data/database.

  11. See http://ec.europa.eu/enterprise/policies/sme/entrepreneurship-2020/index_en.htm for further details.

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Authors and Affiliations

  1. Politecnico di Milano – DIG, Via Lambruschini 4B, 20156, Milan, Italy

    Niccolò Ghio, Massimiliano Guerini & Cristina Rossi-Lamastra

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  1. Niccolò Ghio
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  2. Massimiliano Guerini
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  3. Cristina Rossi-Lamastra
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Correspondence to Cristina Rossi-Lamastra.

Appendix

Appendix

See Tables 5 and 6.

Table 5 Link between the start-up’s industry and university disciplinary areas, based on the studies of Schartinger et al. (2002)
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Table 6 Estimation of the decay parameter
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Ghio, N., Guerini, M. & Rossi-Lamastra, C. University knowledge and the creation of innovative start-ups: an analysis of the Italian case. Small Bus Econ 47, 293–311 (2016). https://doi.org/10.1007/s11187-016-9720-2

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