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Agglomeration economies, inventors and entrepreneurs as engines of European regional economic development

Abstract

In economic agglomeration studies, the distinction of various externalities circumstances related to knowledge spillovers remains largely unclear. This paper introduces human capital, innovation and several types of entrepreneurship as potential drivers of regional economic performance with an impact of agglomeration economies. We use measures of specific types of entrepreneurship, discerned at the individual level, as well as human capital and invention through patenting activity for the period 2001–2006. The empirical application on 111 regions across 14 European countries investigates their relation with observed regional productivity rates in 2006. Our main findings indicate that (i) human capital, patenting activity and entrepreneurship are all linked to regional performance, more so in regions containing large as well as medium-sized cities; (ii) they act as complements rather than substitutes, facilitating productivity differently; and (iii) accounting for patenting activity and entrepreneurship captures agglomeration externalities effects previously subscribed only to the density of resources of regional performance. The particular role of regions with medium-sized cities next to regions with large cities complies with observed growth trends as well as recently proposed place-based development approaches that assume that interactions between institutions and geography are critical for regional economic performance.

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Notes

  1. The adjective ‘objective-1’ is specifically associated with European Regional Development Funds (ERDF) in the programming period 2000–2006. ERDF aims to strengthen economic and social cohesion in the European Union by correcting imbalances between its regions and mainly refers as ‘objective-1’ to the regions lagging behind in terms of development. The term is region (Europe) and time (2000–2006) specific. In the new programming period, the old ‘objective 1’ regions together with the Cohesion Fund are understood as ‘convergence regions’ and consider as well ‘objective 2’ regions (Dühr et al. 2010, pp. 273–286). In our research period, although objective-1 regions are not the only representatives of cohesion policy regions, they form an important part of it (compare Dogaru et al. 2011; Lopez-Rodriguez and Faiña 2006). For regions in Slovenia in our analyses, we used preaccession funds as indicator. Objective-1 regions can be both urban and rural in character.

  2. The initial spatial scale was adopted along a regional classification developed by ESRI and consisted of 125 regions corresponding to Nuts1 levels for Belgium, France, Germany, Greece, Ireland, the Netherlands, and the United Kingdom. Nuts2 levels are applied to Croatia, Denmark, Finland, Hungary, Norway, Portugal, Slovenia, and Sweden, and a combination of Nuts1 and Nuts2 to Italy, Spain, and Switzerland. In a second step, some dense sub-regions were identified within the previously identified larger regions; if the sample size allowed, these dense regions are abstracted and treated separately from the larger region of which they form part. This resulted in an augmented sample of 142 regions.

  3. This is no problem for our data set, as we deal with Nuts2 regions in these countries in our analysis.

  4. We would like to point out that the entrepreneurship measure in our analysis disregards entrepreneurial activity conducted by employees. In Bosma et al. (2012), in a first international comparison of entrepreneurial employee activity, countries like Sweden, Belgium and France appear to exhibit relatively high levels of entrepreneurial employee activity.

  5. There is also no significant correlation between the error terms resulting from our regression and the acreage of the region.

  6. Although this distinction differs somewhat from that in OECD (2012), these cutoff points yield a distribution for the European regional classification adopted in the paper that is comparable to the OECD-distribution on a global scale.

  7. Even though the coefficient for the specialization/diversity measure is not significant, its addition to model 2b leads to a better model fit (\(p<0.05\)).

  8. Auxiliary regressions (not reported) indicate very similar outcomes for Tables 2 and 3 when applying random effects estimation.

  9. Using a binary weight matrix with information on neighbouring regions, the Getis–Ord statistics did not point at spatial lags for the dependent and independent variables, nor for the residuals. In contrast to the more often used Moran’s I statistic, the Getis–Ord statistic focuses on clustering of high and low values (Getis and Ord 1992), which would be the relevant issue of concern in our analysis.

  10. The correlations between these variables are not very high (and never higher than 0.61).

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Appendix

Appendix

See Table 5.

Table 5 Regions included in the empirical analysis

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van Oort, F.G., Bosma, N.S. Agglomeration economies, inventors and entrepreneurs as engines of European regional economic development. Ann Reg Sci 51, 213–244 (2013). https://doi.org/10.1007/s00168-012-0547-8

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  • L26
  • O30