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Innovation modes and entrepreneurial behavioral characteristics in regional growth

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Abstract

This paper studies the relationship between entrepreneurship and regional growth by arguing that the entrepreneurship/growth relationship is mediated by the characteristics of the innovative environment in which new firms operate, which can explain the high volatility of the empirical results on the entrepreneurship/regional growth nexus existing in the literature. The innovation context represents the pool of discovery opportunities and of creative atmosphere that may explain the birth of an entrepreneurial activity. Moreover, these opportunities may or may not be grasped according to behavioral characteristics of regional entrepreneurs, interpreted as potential capacity to discover, risk orientation and strategic vision. We provide evidence of the complex and spatially heterogeneous interplay between regional innovation modes, entrepreneurial behavioral characteristics and economic growth for 252 NUTS2 regions of the European Union.

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Notes

  1. Regional modes of innovation have been identified by means of a k-means cluster analysis based on a series of indicators capturing the different regional knowledge and innovation propensities, i.e., the regional EU (European Union) share of total patents, the regional share of firms introducing product and/or process innovation, and the regional share of firms introducing marketing and/or organizational innovation. For further details on the variables used in the cluster analysis and the variables representing the key territorial features of the different groups of regions see Capello and Lenzi (2013).

  2. Put briefly, REDI is obtained as the combination of three main subindexes, called entrepreneurial attitude, ability and aspiration, which in turn are the outcomes of the interplay among 14 pillars. These 14 pillars, too, are composite indicators that merge by interaction up to 76 individual and context (i.e. regional and/or national) level variables. Because of data availability constraints, REDI and its constitutive pillars and subindicators have been developed with a mix of NUTS1 and NUTS2 level, depending on the country and for all EU-27 countries with the exception of Bulgaria, Cyprus, Luxembourg, Malta, and Åland in Finland (NUTS2 code FI20). For those countries for which data were available at NUTS1 level only, data at NUTS2 level were extrapolated by assigning the same value to all NUTS2 regions belonging to the same NUTS1. For details on the precise indices forming the composite ones, the operationalization, computation and the rationale of the choice of the variables used to obtain the 14 pillars and the subindexes see the Szerb et al. (2013). A summary description is also presented in Table 4 in “Appendix”.

  3. Acs et al. (2014) severely criticized the traditional indicators of entrepreneurship; in their opinion, their merits notwithstanding, traditional indicators fail to take account of the context in which new firms come to operate and the process through which new businesses come to operate, as well as the feasibility and actual realization of entrepreneurial events.

  4. VIF (Variance inflation factor) for the entrepreneurial characteristics variables is quite high (namely, 6.99, 5.36 and 1.83, respectively, for potential of opportunities perception, risk orientation and strategic vision). In consideration of their high correlations (ranging from 0.62 to 0.89 and all significant at the 5 % level) and VIF, we decided to introduce the three variables separately in the regressions.

  5. As a general remark, we are aware that the period of measurement of the dependent and the entrepreneurship variables may raise concerns. In this respect, more than causally, our estimates are better to be interpreted as a set of partial correlation indices highlighting and describing the combinations of entrepreneurial characteristics and regional innovative environments more likely to lead to growth.

  6. We also performed an additional robustness check by excluding three NUTS2 regions (namely, Brussels, Stockholm and Inner London), suspect of being outlier as their real GDP per capita in 2006 falls in top 1 % of the variable distribution (see also Table 1). Results, unreported for reason of space but available upon request, are fully consistent with the ones presented here.

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Authors and Affiliations

  1. Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Roberta Capello & Camilla Lenzi

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  1. Roberta Capello
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  2. Camilla Lenzi
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Correspondence to Camilla Lenzi.

Appendix

Appendix

See Tables 4, 5 and 6.

Table 4 Definition of REDI’s pillars and subindexes
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Table 5 Robustness checks—Spatial Durbin Error Model (SDEM) specification
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Table 6 Robustness checks—average annual per capita real GDP growth rate adjusted for the crisis
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Capello, R., Lenzi, C. Innovation modes and entrepreneurial behavioral characteristics in regional growth. Small Bus Econ 47, 875–893 (2016). https://doi.org/10.1007/s11187-016-9741-x

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